Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Pulvirenti, Alessandra; Yamashita, Rikiya; Midya, Abhishek; Gönen, Mithat; Klimstra, David S.; Reidy, Diane L.; Allen, Peter J.; Do, Richard K. G.; Simpson, Amber L.

2018-02-01

Pancreatic neuroendocrine tumors (PanNETs) account for approximately 5% of all pancreatic tumors, affecting one individual per million each year.1 PanNETs are difficult to treat due to biological variability from benign to highly malignant, indolent to very aggressive. The World Health Organization classifies PanNETs into three categories based on cell proliferative rate, usually detected using the Ki67 index and cell morphology: low-grade (G1), intermediate-grade (G2) and high-grade (G3) tumors. Knowledge of grade prior to treatment would select patients for optimal therapy: G1/G2 tumors respond well to somatostatin analogs and targeted or cytotoxic drugs whereas G3 tumors would be targeted with platinum or alkylating agents.2, 3 Grade assessment is based on the pathologic examination of the surgical specimen, biopsy or ne-needle aspiration; however, heterogeneity in the proliferative index can lead to sampling errors.4 Based on studies relating qualitatively assessed shape and enhancement characteristics on CT imaging to tumor grade in PanNET,5 we propose objective classification of PanNET grade with quantitative analysis of CT images. Fifty-five patients were included in our retrospective analysis. A pathologist graded the tumors. Texture and shape-based features were extracted from CT. Random forest and naive Bayes classifiers were compared for the classification of G1/G2 and G3 PanNETs. The best area under the receiver operating characteristic curve (AUC) of 0:74 and accuracy of 71:64% was achieved with texture features. The shape-based features achieved an AUC of 0:70 and accuracy of 78:73%.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

Comparison of transcriptomic signature of post-Chernobyl and postradiotherapy thyroid tumors.

PubMed

Ory, Catherine; Ugolin, Nicolas; Hofman, Paul; Schlumberger, Martin; Likhtarev, Illya A; Chevillard, Sylvie

2013-11-01

We previously identified two highly discriminating and predictive radiation-induced transcriptomic signatures by comparing series of sporadic and postradiotherapy thyroid tumors (322-gene signature), and by reanalyzing a previously published data set of sporadic and post-Chernobyl thyroid tumors (106-gene signature). The aim of the present work was (i) to compare the two signatures in terms of gene expression deregulations and molecular features/pathways, and (ii) to test the capacity of the postradiotherapy signature in classifying the post-Chernobyl series of tumors and reciprocally of the post-Chernobyl signature in classifying the postradiotherapy-induced tumors. We now explored if postradiotherapy and post-Chernobyl papillary thyroid carcinomas (PTC) display common molecular features by comparing molecular pathways deregulated in the two tumor series, and tested the potential of gene subsets of the postradiotherapy signature to classify the post-Chernobyl series (14 sporadic and 12 post-Chernobyl PTC), and reciprocally of gene subsets of the post-Chernobyl signature to classify the postradiotherapy series (15 sporadic and 12 postradiotherapy PTC), by using conventional principal component analysis. We found that the five genes common to the two signatures classified the learning/training tumors (used to search these signatures) of both the postradiotherapy (seven PTC) and the post-Chernobyl (six PTC) thyroid tumor series as compared with the sporadic tumors (seven sporadic PTC in each series). Importantly, these five genes were also effective for classifying independent series of postradiotherapy (five PTC) and post-Chernobyl (six PTC) tumors compared to independent series of sporadic tumors (eight PTC and six PTC respectively; testing tumors). Moreover, part of each postradiotherapy (32 genes) and post-Chernobyl signature (16 genes) cross-classified the respective series of thyroid tumors. Finally, several molecular pathways deregulated in post-Chernobyl tumors matched those found to be deregulated in postradiotherapy tumors. Overall, our data suggest that thyroid tumors that developed following either external exposure or internal (131)I contamination shared common molecular features, related to DNA repair, oxidative and endoplasmic reticulum stresses, allowing their classification as radiation-induced tumors in comparison with sporadic counterparts, independently of doses and dose rates, which suggests there may be a "general" radiation-induced signature of thyroid tumors.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

PubMed Central

Santagata, Sandro; Thakkar, Ankita; Ergonul, Ayse; Wang, Bin; Woo, Terri; Hu, Rong; Harrell, J. Chuck; McNamara, George; Schwede, Matthew; Culhane, Aedin C.; Kindelberger, David; Rodig, Scott; Richardson, Andrea; Schnitt, Stuart J.; Tamimi, Rulla M.; Ince, Tan A.

2014-01-01

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors. PMID:24463450

Usefulness of a Novel Ultrasonographic Classification Based on Anechoic Area Patterns for Differentiating Warthin Tumors from Pleomorphic Adenomas of the Parotid Gland

PubMed Central

Matsuda, Eriko; Fukuhara, Takahiro; Donishi, Ryohei; Kawamoto, Katsuyuki; Hirooka, Yasuaki; Takeuchi, Hiromi

2018-01-01

Background Ultrasonographic homogeneity is an important differential finding between Warthin tumor and pleomorphic adenoma, two types of benign parotid gland tumors, with the former likely to be heterogeneous and the latter homogeneous. However, differences in the performance of ultrasound machines or the homogeneity cut-off level affect the judgment of ultrasonographic homogeneity. Therefore, in this study, we adopted a novel system for classifying the composition of tumors via ultrasonography, using anechoic area as a substitute for differences in homogeneity to differentiate between Warthin tumors and pleomorphic adenomas. Methods We evaluated 68 tumors that were histopathologically diagnosed as Warthin tumor or pleomorphic adenoma between July 2009 and November 2015. Ultrasonographic images of the tumors were evaluated on the basis of key differentiating features, including features on B-mode imaging and color Doppler imaging. Additionally, the tumors were classified into four groups based on anechoic area, and findings were compared between Warthin tumors and pleomorphic adenomas. Results While 38 of the tumors were pleomorphic adenomas, 30 were Warthin tumors. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for detection of Warthin tumors using our novel classification system were 73.3%, 76.3%, 71.0%, 78.4% and 75.0%, respectively. Compared to pleomorphic adenomas, Warthin tumors showed large or sponge-like anechoic areas, rich vascularization and an oval shape even at large tumor sizes, and the difference was significant. On defining Warthin tumor as a tumor demonstrating two or more of the findings noted above, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy for its detection were 73.3%, 84.2%, 78.6%, 80.0% and 79.4%, respectively. Conclusion Our novel classification system based on anechoic area patterns demonstrated by the tumors had high sensitivity, specificity and diagnostic accuracy for differentiating Warthin tumors from pleomorphic adenomas. PMID:29434491

Usefulness of a Novel Ultrasonographic Classification Based on Anechoic Area Patterns for Differentiating Warthin Tumors from Pleomorphic Adenomas of the Parotid Gland.

PubMed

Matsuda, Eriko; Fukuhara, Takahiro; Donishi, Ryohei; Kawamoto, Katsuyuki; Hirooka, Yasuaki; Takeuchi, Hiromi

2017-12-01

Ultrasonographic homogeneity is an important differential finding between Warthin tumor and pleomorphic adenoma, two types of benign parotid gland tumors, with the former likely to be heterogeneous and the latter homogeneous. However, differences in the performance of ultrasound machines or the homogeneity cut-off level affect the judgment of ultrasonographic homogeneity. Therefore, in this study, we adopted a novel system for classifying the composition of tumors via ultrasonography, using anechoic area as a substitute for differences in homogeneity to differentiate between Warthin tumors and pleomorphic adenomas. We evaluated 68 tumors that were histopathologically diagnosed as Warthin tumor or pleomorphic adenoma between July 2009 and November 2015. Ultrasonographic images of the tumors were evaluated on the basis of key differentiating features, including features on B-mode imaging and color Doppler imaging. Additionally, the tumors were classified into four groups based on anechoic area, and findings were compared between Warthin tumors and pleomorphic adenomas. While 38 of the tumors were pleomorphic adenomas, 30 were Warthin tumors. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for detection of Warthin tumors using our novel classification system were 73.3%, 76.3%, 71.0%, 78.4% and 75.0%, respectively. Compared to pleomorphic adenomas, Warthin tumors showed large or sponge-like anechoic areas, rich vascularization and an oval shape even at large tumor sizes, and the difference was significant. On defining Warthin tumor as a tumor demonstrating two or more of the findings noted above, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy for its detection were 73.3%, 84.2%, 78.6%, 80.0% and 79.4%, respectively. Our novel classification system based on anechoic area patterns demonstrated by the tumors had high sensitivity, specificity and diagnostic accuracy for differentiating Warthin tumors from pleomorphic adenomas.

Array-based DNA-methylation profiling in sarcomas with small blue round cell histology provides valuable diagnostic information.

PubMed

Koelsche, Christian; Hartmann, Wolfgang; Schrimpf, Daniel; Stichel, Damian; Jabar, Susanne; Ranft, Andreas; Reuss, David E; Sahm, Felix; Jones, David T W; Bewerunge-Hudler, Melanie; Trautmann, Marcel; Klingebiel, Thomas; Vokuhl, Christian; Gessler, Manfred; Wardelmann, Eva; Petersen, Iver; Baumhoer, Daniel; Flucke, Uta; Antonescu, Cristina; Esteller, Manel; Fröhling, Stefan; Kool, Marcel; Pfister, Stefan M; Mechtersheimer, Gunhild; Dirksen, Uta; von Deimling, Andreas

2018-03-23

Undifferentiated solid tumors with small blue round cell histology and expression of CD99 mostly resemble Ewing sarcoma. However, they also may include other tumors such as mesenchymal chondrosarcoma, synovial sarcoma, or small cell osteosarcoma. Definitive classification usually requires detection of entity-specific mutations. While this approach identifies the majority of Ewing sarcomas, a subset of lesions remains unclassified and, therefore, has been termed "Ewing-like sarcomas" or small blue round cell tumors not otherwise specified. We developed an approach for further characterization of small blue round cell tumors not otherwise specified using an array-based DNA-methylation profiling approach. Data were analyzed by unsupervised clustering and t-distributed stochastic neighbor embedding analysis and compared with a reference methylation data set of 460 well-characterized prototypical sarcomas encompassing 18 subtypes. Verification was performed by additional FISH analyses, RNA sequencing from formalin-fixed paraffin-embedded material or immunohistochemical marker analyses. In a cohort of more than 1,000 tumors assumed to represent Ewing sarcomas, 30 failed to exhibit the typical EWS translocation. These tumors were subjected to methylation profiling and could be assigned to Ewing sarcoma in 14 (47%), to small blue round cell tumors with CIC alteration in 6 (20%), to small blue round cell tumors with BCOR alteration in 4 (13%), to synovial sarcoma and to malignant rhabdoid tumor in 2 cases each. One single case each was allotted to mesenchymal chondrosarcoma and adamantinoma. 12/14 tumors classified as Ewing sarcoma could be verified by demonstrating either a canonical EWS translocation evading initial testing, by identifying rare breakpoints or fusion partners. The methylation-based assignment of the remaining small blue round cell tumors not otherwise specified also could be verified by entity-specific molecular alterations in 13/16 cases. In conclusion, array-based DNA-methylation analysis of undifferentiated tumors with small blue round cell histology is a powerful tool for precisely classifying this diagnostically challenging tumor group.

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

PubMed Central

2012-01-01

Background Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. PMID:22830977

Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages.

PubMed

Reddy, Anupama; Growney, Joseph D; Wilson, Nick S; Emery, Caroline M; Johnson, Jennifer A; Ward, Rebecca; Monaco, Kelli A; Korn, Joshua; Monahan, John E; Stump, Mark D; Mapa, Felipa A; Wilson, Christopher J; Steiger, Janine; Ledell, Jebediah; Rickles, Richard J; Myer, Vic E; Ettenberg, Seth A; Schlegel, Robert; Sellers, William R; Huet, Heather A; Lehár, Joseph

2015-01-01

Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages

PubMed Central

Reddy, Anupama; Growney, Joseph D.; Wilson, Nick S.; Emery, Caroline M.; Johnson, Jennifer A.; Ward, Rebecca; Monaco, Kelli A.; Korn, Joshua; Monahan, John E.; Stump, Mark D.; Mapa, Felipa A.; Wilson, Christopher J.; Steiger, Janine; Ledell, Jebediah; Rickles, Richard J.; Myer, Vic E.; Ettenberg, Seth A.; Schlegel, Robert; Sellers, William R.

2015-01-01

Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response. PMID:26378449

Immunohistochemical ATRX expression is not a surrogate for 1p19q codeletion.

PubMed

Yamamichi, Akane; Ohka, Fumiharu; Aoki, Kosuke; Suzuki, Hiromichi; Kato, Akira; Hirano, Masaki; Motomura, Kazuya; Tanahashi, Kuniaki; Chalise, Lushun; Maeda, Sachi; Wakabayashi, Toshihiko; Kato, Yukinari; Natsume, Atsushi

2018-04-01

The IDH-mutant and 1p/19q co-deletion (1p19q codel) provides significant diagnostic and prognostic value in lower-grade gliomas. As ATRX mutation and 1p19q codel are mutually exclusive, ATRX immunohistochemistry (IHC) may substitute for 1p19q codel, but this has not been comprehensively examined. In the current study, we performed ATRX-IHC in 78 gliomas whose ATRX statuses were comprehensively determined by whole exome sequencing. Among the 60 IHC-positive and 18 IHC-negative cases, 86.7 and 77.8% were ATRX-wildtype and ATRX-mutant, respectively. ATRX mutational patterns were not consistent with ATRX-IHC. If our cohort had only used IDH status and IHC-based ATRX expression for diagnosis, 78 tumors would have been subtyped as 48 oligodendroglial tumors, 16 IDH-mutant astrocytic tumors, and 14 IDH-wildtype astrocytic tumors. However, when the 1p19q codel test was performed following ATRX-IHC, 8 of 48 ATRX-IHC-positive tumors were classified as "1p19q non-codel" and 3 of 16 ATRX-IHC-negative tumors were classified as "1p19q codel"; a total of 11 tumors (14%) were incorrectly classified. In summary, we observed dissociation between ATRX-IHC and actual 1p19q codel in 11 of 64 IDH-mutant LGGs. In describing the complex IHC expression of ATRX somatic mutations, our results indicate the need for caution when using ATRX-IHC as a surrogate of 1p19q status.

[Molecular Genetics as Best Evidence in Glioma Diagnostics].

PubMed

Masui, Kenta; Komori, Takashi

2016-03-01

The development of a genomic landscape of gliomas has led to the internally consistent, molecularly-based classifiers. However, development of a biologically insightful classification to guide therapy is still ongoing. Further, tumors are heterogeneous, and they change and adapt in response to drugs. The challenge of developing molecular classifiers that provide meaningful ways to stratify patients for therapy remains a major challenge for the field. Therefore, by incorporating molecular markers into the new World Health Organization (WHO) classification of tumors of the central nervous system, the traditional principle of diagnosis based on histologic criteria will be replaced by a multilayered approach combining histologic features and molecular information in an "integrated diagnosis", to define tumor entities as narrowly as possible. We herein review the current status of diagnostic molecular markers for gliomas, focusing on IDH mutation, ATRX mutation, 1p/19q co-deletion, and TERT promoter mutation in adult tumors, as well as BRAF and H3F3A aberrations in pediatric gliomas, the combination of which will be a promising endeavor to render molecular genetics as a best evidence in the glioma diagnositics.

The effect of combining two echo times in automatic brain tumor classification by MRS.

PubMed

García-Gómez, Juan M; Tortajada, Salvador; Vidal, César; Julià-Sapé, Margarida; Luts, Jan; Moreno-Torres, Angel; Van Huffel, Sabine; Arús, Carles; Robles, Montserrat

2008-11-01

(1)H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel (1)H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies. Copyright (c) 2008 John Wiley & Sons, Ltd.

Morphology-based optical separation of subpopulations from a heterogeneous murine breast cancer cell line.

PubMed

Tamura, Masato; Sugiura, Shinji; Takagi, Toshiyuki; Satoh, Taku; Sumaru, Kimio; Kanamori, Toshiyuki; Okada, Tomoko; Matsui, Hirofumi

2017-01-01

Understanding tumor heterogeneity is an urgent and unmet need in cancer research. In this study, we used a morphology-based optical cell separation process to classify a heterogeneous cancer cell population into characteristic subpopulations. To classify the cell subpopulations, we assessed their morphology in hydrogel, a three-dimensional culture environment that induces morphological changes according to the characteristics of the cells (i.e., growth, migration, and invasion). We encapsulated the murine breast cancer cell line 4T1E, as a heterogeneous population that includes highly metastatic cells, in click-crosslinkable and photodegradable gelatin hydrogels, which we developed previously. We observed morphological changes within 3 days of encapsulating the cells in the hydrogel. We separated the 4T1E cell population into colony- and granular-type cells by optical separation, in which local UV-induced degradation of the photodegradable hydrogel around the target cells enabled us to collect those cells. The obtained colony- and granular-type cells were evaluated in vitro by using a spheroid assay and in vivo by means of a tumor growth and metastasis assay. The spheroid assay showed that the colony-type cells formed compact spheroids in 2 days, whereas the granular-type cells did not form spheroids. The tumor growth assay in mice revealed that the granular-type cells exhibited lower tumor growth and a different metastasis behavior compared with the colony-type cells. These results suggest that morphology-based optical cell separation is a useful technique to classify a heterogeneous cancer cell population according to its cellular characteristics.

Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.

PubMed

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; McDonald, Elizabeth S; Rosen, Mark; Mies, Carolyn; Feldman, Michael; Kontos, Despina

2015-06-01

Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer. Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay. Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94). The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information. HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.

Classification of ipsilateral breast tumor recurrences after breast conservation therapy can predict patient prognosis and facilitate treatment planning

PubMed Central

Yi, Min; Buchholz, Thomas A.; Meric-Bernstam, Funda; Bedrosian, Isabelle; Hwang, Rosa F.; Ross, Merrick I.; Kuerer, Henry M.; Luo, Sheng; Gonzalez-Angulo, Ana M.; Buzdar, Aman U.; Symmans, W. Fraser; Feig, Barry W.; Lucci, Anthony; Huang, Eugene H.; Hunt, Kelly K.

2015-01-01

Objective To classify ipsilateral breast tumor recurrences (IBTR) as either new primary tumors (NP) or true local recurrence (TR). We utilized two different methods and compared sensitivities and specificities between them. Our goal was to determine whether distinguishing NP from TR had prognostic value. Summary Background Data After breast-conservation therapy (BCT), IBTR may be classified into two distinct types (NP and TR). Studies have attempted to classify IBTR by using tumor location, histologic subtype, DNA flow cytometry data, or gene-expression profiling data. Methods 447 (7.9%) of 5660 patients undergoing BCT from 1970 to 2005 experienced IBTR. Clinical data from 397 patients were available for review. We classified IBTRs as NP or TR on the basis of either tumor location and histologic subtype (method 1) or tumor location, histologic subtype, estrogen receptor (ER) status and human epidermal growth factor receptor 2 (HER-2) status (method 2). Kaplan-Meier curves and log-rank tests were used to evaluate overall and disease-specific survival (DSS) differences between the two groups. Classification methods were validated by calculating sensitivity and specificity values using a Bayesian method. Results Of 397 patients, 196 (49.4%) were classified as NP by method 1 and 212 (53.4%) were classified as NP by method 2. The sensitivity and specificity values were 0.812 and 0.867 for method 1 and 0.870 and 0.800 for method 2, respectively. Regardless of method used, patients classified as NP developed contralateral breast carcinoma more often but had better 10-year overall and DSS rates than those classified as TR. Patients with TR were more likely to develop metastatic disease after IBTR. Conclusion IBTR classified as TR and NP had clinically different features, suggesting that classifying IBTR may provide clinically significant data for the management of IBTR. PMID:21209588

Effective user guidance in online interactive semantic segmentation

NASA Astrophysics Data System (ADS)

Petersen, Jens; Bendszus, Martin; Debus, Jürgen; Heiland, Sabine; Maier-Hein, Klaus H.

2017-03-01

With the recent success of machine learning based solutions for automatic image parsing, the availability of reference image annotations for algorithm training is one of the major bottlenecks in medical image segmentation. We are interested in interactive semantic segmentation methods that can be used in an online fashion to generate expert segmentations. These can be used to train automated segmentation techniques or, from an application perspective, for quick and accurate tumor progression monitoring. Using simulated user interactions in a MRI glioblastoma segmentation task, we show that if the user possesses knowledge of the correct segmentation it is significantly (p <= 0.009) better to present data and current segmentation to the user in such a manner that they can easily identify falsely classified regions compared to guiding the user to regions where the classifier exhibits high uncertainty, resulting in differences of mean Dice scores between +0.070 (Whole tumor) and +0.136 (Tumor Core) after 20 iterations. The annotation process should cover all classes equally, which results in a significant (p <= 0.002) improvement compared to completely random annotations anywhere in falsely classified regions for small tumor regions such as the necrotic tumor core (mean Dice +0.151 after 20 it.) and non-enhancing abnormalities (mean Dice +0.069 after 20 it.). These findings provide important insights for the development of efficient interactive segmentation systems and user interfaces.

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

PubMed Central

2013-01-01

Background Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Results Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. Conclusions The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences. PMID:23815266

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients.

PubMed

Cangelosi, Davide; Blengio, Fabiola; Versteeg, Rogier; Eggert, Angelika; Garaventa, Alberto; Gambini, Claudio; Conte, Massimo; Eva, Alessandra; Muselli, Marco; Varesio, Luigi

2013-01-01

Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.

Genomic Analysis of Tumor Microenvironment Immune Types across 14 Solid Cancer Types: Immunotherapeutic Implications.

PubMed

Chen, Yu-Pei; Zhang, Yu; Lv, Jia-Wei; Li, Ying-Qin; Wang, Ya-Qin; He, Qing-Mei; Yang, Xiao-Jing; Sun, Ying; Mao, Yan-Ping; Yun, Jing-Ping; Liu, Na; Ma, Jun

2017-01-01

We performed a comprehensive immuno-genomic analysis of tumor microenvironment immune types (TMITs), which is classified into four groups based on PD-L1+CD8A or PD-L1+cytolytic activity (CYT) expression, across a broad spectrum of solid tumors in order to help identify patients who will benefit from anti- PD-1/PD-L1 therapy. The mRNA sequencing data from The Cancer Genome Atlas (TCGA) of 14 solid cancer types representing 6,685 tumor samples was analyzed. TMIT was classified only for those tumor types that both PD-L1 and CD8A/CYT could prefict mutation and/or neoantigen number. The mutational and neoepitope features of the tumor were compared according to the four TMITs. We found that PD-L1/CD8A/CYT subgroups could not distinguish different mutation and neoantigen numbers in certain tumor types such as glioblastoma multiforme, prostate adenocarcinoma, and head and neck and lung squamous cell carcinoma. For the remaining tumor types, compared with TIMT II (low PD-L1 and CD8A/CYT), TIMT I (high PD-L1 and CD8A/CYT) had a significantly higher number of mutations or neoantigens in bladder urothelial carcinoma, breast and cervical cancer, colorectal, stomach and lung adenocarcinoma, and melanoma. In contrast, TMIT I of kidney clear cell, liver hepatocellular, and thyroid carcinoma were negatively correlated with mutation burden or neoantigen numbers. Our findings show that the TMIT stratification proposed could serve as a favorable approach for tailoring optimal immunotherapeutic strategies in certain tumor types. Going forward, it will be important to test the clinical practicability of TMIT based on quantification of immune infiltrates using mRNA-seq to predict clinical response to these and other immunotherapeutic strategies in more different tumors.

The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Heyse, E-mail: heyse.li@mail.utoronto.ca; Becker, Nathan; Raman, Srinivas

2015-08-15

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derivedmore » from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman’s rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew’s correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse.« less

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

PubMed Central

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-01-01

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions. PMID:28154470

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

PubMed

Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

2016-05-26

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

A mathematical theory of shape and neuro-fuzzy methodology-based diagnostic analysis: a comparative study on early detection and treatment planning of brain cancer.

PubMed

Kar, Subrata; Majumder, D Dutta

2017-08-01

Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM (μ) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%. The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

PubMed Central

Yamamoto, Yoichiro; Saito, Akira; Tateishi, Ayako; Shimojo, Hisashi; Kanno, Hiroyuki; Tsuchiya, Shinichi; Ito, Ken-ichi; Cosatto, Eric; Graf, Hans Peter; Moraleda, Rodrigo R.; Eils, Roland; Grabe, Niels

2017-01-01

Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression. PMID:28440283

Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

NASA Astrophysics Data System (ADS)

Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed

2018-02-01

Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor's heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

Spinal focal lesion detection in multiple myeloma using multimodal image features

NASA Astrophysics Data System (ADS)

Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

2015-03-01

Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Juhun, E-mail: leej15@upmc.edu; Nishikawa, Robert M.; Reiser, Ingrid

2015-09-15

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimalmore » feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C{sub T}) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C{sub T} yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C{sub T} were conducted. The results showed that C{sub T} was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.« less

[Establishment and Management of Multicentral Collection Bio-sample Banks of Malignant Tumors from Digestive System].

PubMed

Shen, Si; Shen, Junwei; Zhu, Liang; Wu, Chaoqun; Li, Dongliang; Yu, Hongyu; Qiu, Yuanyuan; Zhou, Yi

2015-11-01

To establish and manage of multicentral collection bio-sample banks of malignant tumors from digestive system, the paper designed a multicentral management system, established the standard operation procedures (SOPs) and leaded ten hospitals nationwide to collect tumor samples. The biobank has been established for half a year, and has collected 695 samples from patients with digestive system malignant tumor. The clinical data is full and complete, labeled in a unified way and classified to be managed. The clinical and molecular biology researches were based on the biobank, and obtained achievements. The biobank provides a research platform for malignant tumor of digestive system from different regions and of different types.

Prognostic factors in breast phyllodes tumors: a nomogram based on a retrospective cohort study of 404 patients.

PubMed

Zhou, Zhi-Rui; Wang, Chen-Chen; Sun, Xiang-Jie; Yang, Zhao-Zhi; Chen, Xing-Xing; Shao, Zhi-Ming; Yu, Xiao-Li; Guo, Xiao-Mao

2018-04-01

The aim of this study was to explore the independent prognostic factors related to postoperative recurrence-free survival (RFS) in patients with breast phyllodes tumors (PTBs). A retrospective analysis was conducted in Fudan University Shanghai Cancer Center. According to histological type, patients with benign PTBs were classified as a low-risk group, while borderline and malignant PTBs were classified as a high-risk group. The Cox regression model was adopted to identify factors affecting postoperative RFS in the two groups, and a nomogram was generated to predict recurrence-free survival at 1, 3, and 5 years. Among the 404 patients, 168 (41.6%) patients had benign PTB, 184 (45.5%) had borderline PTB, and 52 (12.9%) had malignant PTB. Fifty-five patients experienced postoperative local recurrence, including six benign cases, 26 borderline cases, and 22 malignant cases; the three histological types of PTB had local recurrence rates of 3.6%, 14.1%, and 42.3%, respectively. Stromal cell atypia was an independent prognostic factor for RFS in the low-risk group, while the surgical approach and tumor border were independent prognostic factors for RFS in the high-risk group, and patients receiving simple excision with an infiltrative tumor border had a higher recurrence rate. A nomogram developed based on clinicopathologic features and surgical approaches could predict recurrence-free survival at 1, 3, and 5 years. For high-risk patients, this predictive nomogram based on tumor border, tumor residue, mitotic activity, degree of stromal cell hyperplasia, and atypia can be applied for patient counseling and clinical management. The efficacy of adjuvant radiotherapy remains uncertain. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Challenging the Cancer Molecular Stratification Dogma: Intratumoral Heterogeneity Undermines Consensus Molecular Subtypes and Potential Diagnostic Value in Colorectal Cancer.

PubMed

Dunne, Philip D; McArt, Darragh G; Bradley, Conor A; O'Reilly, Paul G; Barrett, Helen L; Cummins, Robert; O'Grady, Tony; Arthur, Ken; Loughrey, Maurice B; Allen, Wendy L; McDade, Simon S; Waugh, David J; Hamilton, Peter W; Longley, Daniel B; Kay, Elaine W; Johnston, Patrick G; Lawler, Mark; Salto-Tellez, Manuel; Van Schaeybroeck, Sandra

2016-08-15

A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled. We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients. We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR = 2.914 (confidence interval 0.9286-9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stem-like biology have undergone a widespread epithelial-mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed. Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer. Clin Cancer Res; 22(16); 4095-104. ©2016 AACRSee related commentary by Morris and Kopetz, p. 3989. ©2016 American Association for Cancer Research.

Differentiating malignant from benign breast tumors on acoustic radiation force impulse imaging using fuzzy-based neural networks with principle component analysis

NASA Astrophysics Data System (ADS)

Liu, Hsiao-Chuan; Chou, Yi-Hong; Tiu, Chui-Mei; Hsieh, Chi-Wen; Liu, Brent; Shung, K. Kirk

2017-03-01

Many modalities have been developed as screening tools for breast cancer. A new screening method called acoustic radiation force impulse (ARFI) imaging was created for distinguishing breast lesions based on localized tissue displacement. This displacement was quantitated by virtual touch tissue imaging (VTI). However, VTIs sometimes express reverse results to intensity information in clinical observation. In the study, a fuzzy-based neural network with principle component analysis (PCA) was proposed to differentiate texture patterns of malignant breast from benign tumors. Eighty VTIs were randomly retrospected. Thirty four patients were determined as BI-RADS category 2 or 3, and the rest of them were determined as BI-RADS category 4 or 5 by two leading radiologists. Morphological method and Boolean algebra were performed as the image preprocessing to acquire region of interests (ROIs) on VTIs. Twenty four quantitative parameters deriving from first-order statistics (FOS), fractal dimension and gray level co-occurrence matrix (GLCM) were utilized to analyze the texture pattern of breast tumors on VTIs. PCA was employed to reduce the dimension of features. Fuzzy-based neural network as a classifier to differentiate malignant from benign breast tumors. Independent samples test was used to examine the significance of the difference between benign and malignant breast tumors. The area Az under the receiver operator characteristic (ROC) curve, sensitivity, specificity and accuracy were calculated to evaluate the performance of the system. Most all of texture parameters present significant difference between malignant and benign tumors with p-value of less than 0.05 except the average of fractal dimension. For all features classified by fuzzy-based neural network, the sensitivity, specificity, accuracy and Az were 95.7%, 97.1%, 95% and 0.964, respectively. However, the sensitivity, specificity, accuracy and Az can be increased to 100%, 97.1%, 98.8% and 0.985, respectively if PCA was performed to reduce the dimension of features. Patterns of breast tumors on VTIs can effectively be recognized by quantitative texture parameters, and differentiated malignant from benign lesions by fuzzy-based neural network with PCA.

Blood vessel-based liver segmentation through the portal phase of a CT dataset

NASA Astrophysics Data System (ADS)

Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Moriyama, Noriyuki; Utsunomiya, Toru; Shimada, Mitsuo

2013-02-01

Blood vessels are dispersed throughout the human body organs and carry unique information for each person. This information can be used to delineate organ boundaries. The proposed method relies on abdominal blood vessels (ABV) to segment the liver considering the potential presence of tumors through the portal phase of a CT dataset. ABV are extracted and classified into hepatic (HBV) and nonhepatic (non-HBV) with a small number of interactions. HBV and non-HBV are used to guide an automatic segmentation of the liver. HBV are used to individually segment the core region of the liver. This region and non-HBV are used to construct a boundary surface between the liver and other organs to separate them. The core region is classified based on extracted posterior distributions of its histogram into low intensity tumor (LIT) and non-LIT core regions. Non-LIT case includes normal part of liver, HBV, and high intensity tumors if exist. Each core region is extended based on its corresponding posterior distribution. Extension is completed when it reaches either a variation in intensity or the constructed boundary surface. The method was applied to 80 datasets (30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI data) including 60 datasets with tumors. Our results for the MICCAI-test data were evaluated by sliver07 [1] with an overall score of 79.7, which ranks seventh best on the site (December 2013). This approach seems a promising method for extraction of liver volumetry of various shapes and sizes and low intensity hepatic tumors.

Clinicopathological characteristics of duodenal epithelial neoplasms: Focus on tumors with a gastric mucin phenotype (pyloric gland-type tumors).

PubMed

Mitsuishi, Takehiro; Hamatani, Shigeharu; Hirooka, Shinichi; Fukasawa, Nei; Aizawa, Daisuke; Hara, Yuko; Dobashi, Akira; Goda, Kenichi; Fukuda, Takahiro; Saruta, Masayuki; Urashima, Mitsuyoshi; Ikegami, Masahiro

2017-01-01

Epithelial tumors less commonly occur in the duodenum than in the stomach or large intestine. The clinicopathological characteristics of duodenal epithelial tumors remain a matter of debate. We therefore studied resected specimens to investigate the clinicopathological characteristics of duodenal epithelial tumors. Among duodenal epithelial tumors resected endoscopically or surgically in our hospital, we studied the clinicopathological characteristics of 110 adenomas or intramucosal carcinomas. The grade of atypia of all tumors was classified into 3 groups according to the World Health Organization (WHO) 2010 classification. The tumors were immunohistochemically evaluated to determine the frequency of differentiation toward fundic glands. As for patient characteristics, there were 76 men (75.2%) and 25 women (24.8%), with a median age of 65 years (range, 34 to 84). The tumors most commonly arose in the first to second part of the duodenum. Many lesions were flat, and the median tumor diameter was 8.0 mm. The lesions were classified into 2 types according to mucin phenotype: intestinal-type tumors (98 lesions, 89.1%) and gastric-type tumors (12 lesions, 10.9%). Intestinal-type tumors were subdivided into 2 groups: tubular-type tumors (91 lesions, 82.7%) and tubulovillous-type tumors (7 lesions, 6.4%). Gastric-type tumors were classified into 2 types: foveolar type (3 lesions, 2.7%) and pyloric gland-type (PG) tumors (9 lesions, 8.2%). The grade of atypia was significantly higher in gastric-type tumors (p<0.01). PG tumors were gastric-type tumors characterized by pyloric glands and findings suggesting differentiation toward fundic glands. About 10% of the duodenal tumors had a gastric-type mucin phenotype. Gastric-type tumors showed high-grade atypia. In particular, PG tumors showed similarities to PG tumors of the stomach, such as differentiation toward fundic glands.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.

PubMed

Yuan, Yinyin; Failmezger, Henrik; Rueda, Oscar M; Ali, H Raza; Gräf, Stefan; Chin, Suet-Feung; Schwarz, Roland F; Curtis, Christina; Dunning, Mark J; Bardwell, Helen; Johnson, Nicola; Doyle, Sarah; Turashvili, Gulisa; Provenzano, Elena; Aparicio, Sam; Caldas, Carlos; Markowetz, Florian

2012-10-24

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

PubMed

Abbasian Ardakani, Ali; Reiazi, Reza; Mohammadi, Afshin

2018-03-30

This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs. © 2018 by the American Institute of Ultrasound in Medicine.

Between-Region Genetic Divergence Reflects the Mode and Tempo of Tumor Evolution

PubMed Central

Sun, Ruping; Hu, Zheng; Sottoriva, Andrea; Graham, Trevor A.; Harpak, Arbel; Ma, Zhicheng; Fischer, Jared M.; Shibata, Darryl; Curtis, Christina

2017-01-01

Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multi-region sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, revealing different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, accumulate intra-tumor heterogeneity, and ultimately how they may be more effectively treated. PMID:28581503

Assessing Variability in Brain Tumor Segmentation to Improve Volumetric Accuracy and Characterization of Change.

PubMed

Rios Piedra, Edgar A; Taira, Ricky K; El-Saden, Suzie; Ellingson, Benjamin M; Bui, Alex A T; Hsu, William

2016-02-01

Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), providing a more precise description of disease progression to better inform clinical decision-making and treatment planning. While a multitude of segmentation approaches exist, inherent variability in the results of these algorithms may incorrectly indicate changes in tumor volume. In this work, we present a systematic approach to characterize variability in tumor boundaries that utilizes equivalence tests as a means to determine whether a tumor volume has significantly changed over time. To demonstrate these concepts, 32 MRI studies from 8 patients were segmented using four different approaches (statistical classifier, region-based, edge-based, knowledge-based) to generate different regions of interest representing tumor extent. We showed that across all studies, the average Dice coefficient for the superset of the different methods was 0.754 (95% confidence interval 0.701-0.808) when compared to a reference standard. We illustrate how variability obtained by different segmentations can be used to identify significant changes in tumor volume between sequential time points. Our study demonstrates that variability is an inherent part of interpreting tumor segmentation results and should be considered as part of the interpretation process.

Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer

NASA Astrophysics Data System (ADS)

He, Ting; Fan, Ming; Zhang, Peng; Li, Hui; Zhang, Juan; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer can be classified into four molecular subtypes of Luminal A, Luminal B, HER2 and Basal-like, which have significant differences in treatment and survival outcomes. We in this study aim to predict immunohistochemistry (IHC) determined molecular subtypes of breast cancer using image features derived from tumor and peritumoral stroma region based on diffusion weighted imaging (DWI). A dataset of 126 breast cancer patients were collected who underwent preoperative breast MRI with a 3T scanner. The apparent diffusion coefficients (ADCs) were recorded from DWI, and breast image was segmented into regions comprising the tumor and the surrounding stromal. Statistical characteristics in various breast tumor and peritumoral regions were computed, including mean, minimum, maximum, variance, interquartile range, range, skewness, and kurtosis of ADC values. Additionally, the difference of features between each two regions were also calculated. The univariate logistic based classifier was performed for evaluating the performance of the individual features for discriminating subtypes. For multi-class classification, multivariate logistic regression model was trained and validated. The results showed that the tumor boundary and proximal peritumoral stroma region derived features have a higher performance in classification compared to that of the other regions. Furthermore, the prediction model using statistical features, difference features and all the features combined from these regions generated AUC values of 0.774, 0.796 and 0.811, respectively. The results in this study indicate that ADC feature in tumor and peritumoral stromal region would be valuable for estimating the molecular subtype in breast cancer.

Classifying Non-Small Cell Lung Cancer by Status of Programmed Cell Death Ligand 1 and Tumor-Infiltrating Lymphocytes on Tumor Cells.

PubMed

Cui, Shaohua; Dong, Lili; Qian, Jialin; Ye, Lin; Jiang, Liyan

2018-01-01

Purpose: To explore the possible correlation between programmed death ligand 1 (PD-L1)/tumor-infiltrating lymphocytes (TIL) status and clinical factors in non-small cell lung (NSCLC). Materials and Methods: A total of 126 surgical NSCLC samples with stage I to IIIA were retrospectively collected and analyzed. Immunohistochemistry (IHC) assays were used to detect PD-L1 protein expression. PD-L1 positivity on tumor cells was defined by positive tumor cell (TC) percentage using 5% cutoff value. Results: Thirty-seven patients (29.4%), thirty patients (23.8%), six patients (4.8%) and fifty-three patients (42%) were classified as type I (PD-L1+, TIL+), type II (PD-L1-, TIL-), type III (PD-L1+, TIL-) and type IV (PD-L1-, TIL+) tumor environments according to PD-L1/TIL status, respectively. Statistical differences could be observed in factors including gender ( P <0.001), smoking status ( P <0.001), age ( P =0.002), histological types ( P <0.001), EGFR mutation ( P =0.008) and KRAS mutation ( P =0.003) across the four type tumors. Type I tumors were associated with ever smoking, non-adenocarcinoma histological types and KRAS mutation. Type II tumors were associated with female gender, never-smoking, adenocarcinoma histological types and EGFR mutation. Type III tumors were associated with ever smoking and type IV tumors were associated with female gender and EGFR mutation. Conclusion: Clinical factors associated with NSCLC microenvironment types based on PD-L1/TIL differed a lot across different types. The findings of this study may help to facilitate the understanding of the relationship between tumor microenvironment and clinical factors, and also the selecting of patients for combination immunotherapies.

Identifying aggressive prostate cancer foci using a DNA methylation classifier.

PubMed

Mundbjerg, Kamilla; Chopra, Sameer; Alemozaffar, Mehrdad; Duymich, Christopher; Lakshminarasimhan, Ranjani; Nichols, Peter W; Aron, Manju; Siegmund, Kimberly D; Ukimura, Osamu; Aron, Monish; Stern, Mariana; Gill, Parkash; Carpten, John D; Ørntoft, Torben F; Sørensen, Karina D; Weisenberger, Daniel J; Jones, Peter A; Duddalwar, Vinay; Gill, Inderbir; Liang, Gangning

2017-01-12

Slow-growing prostate cancer (PC) can be aggressive in a subset of cases. Therefore, prognostic tools to guide clinical decision-making and avoid overtreatment of indolent PC and undertreatment of aggressive disease are urgently needed. PC has a propensity to be multifocal with several different cancerous foci per gland. Here, we have taken advantage of the multifocal propensity of PC and categorized aggressiveness of individual PC foci based on DNA methylation patterns in primary PC foci and matched lymph node metastases. In a set of 14 patients, we demonstrate that over half of the cases have multiple epigenetically distinct subclones and determine the primary subclone from which the metastatic lesion(s) originated. Furthermore, we develop an aggressiveness classifier consisting of 25 DNA methylation probes to determine aggressive and non-aggressive subclones. Upon validation of the classifier in an independent cohort, the predicted aggressive tumors are significantly associated with the presence of lymph node metastases and invasive tumor stages. Overall, this study provides molecular-based support for determining PC aggressiveness with the potential to impact clinical decision-making, such as targeted biopsy approaches for early diagnosis and active surveillance, in addition to focal therapy.

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry

PubMed Central

AAlAbdulsalam, Abdulrahman K.; Garvin, Jennifer H.; Redd, Andrew; Carter, Marjorie E.; Sweeny, Carol; Meystre, Stephane M.

2018-01-01

Cancer stage is one of the most important prognostic parameters in most cancer subtypes. The American Joint Com-mittee on Cancer (AJCC) specifies criteria for staging each cancer type based on tumor characteristics (T), lymph node involvement (N), and tumor metastasis (M) known as TNM staging system. Information related to cancer stage is typically recorded in clinical narrative text notes and other informal means of communication in the Electronic Health Record (EHR). As a result, human chart-abstractors (known as certified tumor registrars) have to search through volu-minous amounts of text to extract accurate stage information and resolve discordance between different data sources. This study proposes novel applications of natural language processing and machine learning to automatically extract and classify TNM stage mentions from records at the Utah Cancer Registry. Our results indicate that TNM stages can be extracted and classified automatically with high accuracy (extraction sensitivity: 95.5%–98.4% and classification sensitivity: 83.5%–87%). PMID:29888032

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry.

PubMed

AAlAbdulsalam, Abdulrahman K; Garvin, Jennifer H; Redd, Andrew; Carter, Marjorie E; Sweeny, Carol; Meystre, Stephane M

2018-01-01

Cancer stage is one of the most important prognostic parameters in most cancer subtypes. The American Joint Com-mittee on Cancer (AJCC) specifies criteria for staging each cancer type based on tumor characteristics (T), lymph node involvement (N), and tumor metastasis (M) known as TNM staging system. Information related to cancer stage is typically recorded in clinical narrative text notes and other informal means of communication in the Electronic Health Record (EHR). As a result, human chart-abstractors (known as certified tumor registrars) have to search through volu-minous amounts of text to extract accurate stage information and resolve discordance between different data sources. This study proposes novel applications of natural language processing and machine learning to automatically extract and classify TNM stage mentions from records at the Utah Cancer Registry. Our results indicate that TNM stages can be extracted and classified automatically with high accuracy (extraction sensitivity: 95.5%-98.4% and classification sensitivity: 83.5%-87%).

Paragangliomas of the Head & Neck: the KMC experience.

PubMed

Prasad, Sampath Chandra; Thada, Nikhil; Pallavi; Prasad, Kishore Chandra

2011-01-01

To determine the clinical features, investigations, intra-operative findings, surgical approaches used and the results of the treatment for paragangliomas of the head and neck. Retrospective study of 14 cases of paragangliomas in head and neck seen over a period of 10 years including five carotid body tumors, seven glomus jugulares and two glomus tympanicums. HRCT scans and bilateral carotid angiography were done in all cases of glomus jugulare. Pre-operative embolization was done in most cases. The trans-cervical approach was used for all cases of carotid body. In three cases of Type B jugulare tumors, a post-aural tympanotomy was used. A Fisch Type A approach was done for three cases of Type D jugulare tumors. Postaural tympanotomy approach was used for both patients with glomus tympanicum. In one case of extratympanic glomus jugulare tumor with hypoglossal palsy, a neck exploration was done to isolate and excise the tumor. Five patients with carotid body tumors presented as unilateral, painless, pulsatile swelling in the upper neck. Intra-operatively, three of the tumors were classified into Shamlin's Grade II and one each into Grade III and Grade I. A carotid blow-out occurred in one of the patients with Grade II disease, which was managed. ECA resection had to be done in one case. Seven patients were diagnosed to have glomus jugulare and two with glomus tympanicum. Six glomus jugulare tumors presented with hearing loss, ear discharge and obvious swelling. Glomus tympanicums presented with hearing loss but no bleeding from the ear. On examination, tumors presented with an aural polyp with no VII nerve deficits. Both tympanicums were classified as Fisch Type A, three of the jugulares classified as Type B, two as Type D2 and one as Type D1. Tumors were found to be supplied predominantly by the ascending pharyngeal artery. In three cases of Type B jugulare tumors, a post-aural tympanotomy was used. A Fisch Type A approach was done for three cases of Type D jugulare. The transcanal approach was used for both patients with glomus tympanicum. Paragangliomas are uncommon tumors that need accurate diagnosis and skilled operative techniques. Though the surgical approaches may appear complicated, the removal provides good cure rates with minimal morbidity and recurrence. Lateral skull base approaches should be the armamentarium of every head and neck surgeon.

Automated lung tumor segmentation for whole body PET volume based on novel downhill region growing

NASA Astrophysics Data System (ADS)

Ballangan, Cherry; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Feng, Dagan

2010-03-01

We propose an automated lung tumor segmentation method for whole body PET images based on a novel downhill region growing (DRG) technique, which regards homogeneous tumor hotspots as 3D monotonically decreasing functions. The method has three major steps: thoracic slice extraction with K-means clustering of the slice features; hotspot segmentation with DRG; and decision tree analysis based hotspot classification. To overcome the common problem of leakage into adjacent hotspots in automated lung tumor segmentation, DRG employs the tumors' SUV monotonicity features. DRG also uses gradient magnitude of tumors' SUV to improve tumor boundary definition. We used 14 PET volumes from patients with primary NSCLC for validation. The thoracic region extraction step achieved good and consistent results for all patients despite marked differences in size and shape of the lungs and the presence of large tumors. The DRG technique was able to avoid the problem of leakage into adjacent hotspots and produced a volumetric overlap fraction of 0.61 +/- 0.13 which outperformed four other methods where the overlap fraction varied from 0.40 +/- 0.24 to 0.59 +/- 0.14. Of the 18 tumors in 14 NSCLC studies, 15 lesions were classified correctly, 2 were false negative and 15 were false positive.

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

[Risk factors for malignant evolution of gastrointestinal stromal tumors].

PubMed

Andrei, S; Andrei, Adriana; Tonea, A; Andronesi, D; Becheanu, G; Dumbravă, Mona; Pechianu, C; Herlea, V; Popescu, I

2007-01-01

Gastrointestinal stromal tumors are the most frequent non-epithelial digestive tumors, being classified in the group of primitive mesenchymal tumors of the digestive tract. These tumors have a non predictable evolution and where stratified regarding the risk for malignant behavior in 4 categories: very low risk, low risk, intermediate risk and high risk. We performed a retrospective non randomised study including the patients with gastrointestinal stromal tumors treated in the Department of General Surgery and Liver Transplantation of Fundeni Clinical Institute in the period January 2002 - June 2007, to define the epidemiological, clinico-paraclinical, histological and especially evolutive features of the gastrointestinal stromal tumors from this group, with a special regard to the risk factors for their malignant behavior. The most important risk factors in gastrointestinal stromal tumors are the tumor size and the mitotic index, based on them being realised the classification of Fletcher in the 4 risk categories mentioned above. In our group all the local advanced or metastatic gastrointestinal stromal tumors, regardless of their location, were classified in the group of high risk for the malignant behavior. The gastric location and the epithelioid type were positive prognostic factors, and the complete resection of the tumor, an other important positive prognostic feature, was possible in about 80% of the cases, probably because the gastrointestinal stromal tumors in our study were diagnosed in less advanced evolutive situations, only about one third being metastatic and about 14% being locally advanced at the time of diagnose. The association with other neoplasias was in our cases insignificant, only 5% of the patients presenting concomitant malignant digestive tumors and 7.6% intraabdominal benign tumors. Gastrointestinal stromal tumors remain a challenge for the medical staff, regarding their diagnose and therapeutical management, the stratification of the risk for their malignant behavior being essential for the evolution of these patients.

Clinical statistics of gynecologic cancers in Japan.

PubMed

Yamagami, Wataru; Nagase, Satoru; Takahashi, Fumiaki; Ino, Kazuhiko; Hachisuga, Toru; Aoki, Daisuke; Katabuchi, Hidetaka

2017-03-01

Cervical, endometrial, and ovarian cancers, have both high morbidity and mortality among the gynecologic malignant tumors in Japan. The present study was conducted using both the population-based cancer registry and the gynecologic cancer registry to elucidate the characteristics of gynecologic malignant tumors in Japan. Based on nationwide estimates from the population-based cancer registry in Japan, the morbidities and mortality of cervical, endometrial, and ovarian cancers were obtained and used for analysis. Clinicopathologic factors for cervical cancer, endometrial cancer, ovarian cancer, including age, clinical stage, postsurgical stage, histological type, therapeutic strategy, and prognosis were retrieved from the gynecologic cancer registry published by the Japan Society of Obstetrics and Gynecology and used for analysis. The morbidities of cervical, endometrial, and ovarian cancers were 10,908, 13,606, and 9,384 women in 2012, respectively. The prevalence of endometrial cancer has significantly and consistently been increasing and represents the most common gynecologic malignant tumor in Japan. The mortalities of cervical, endometrial, and ovarian cancers were 2.1, 1.3, and 3.2 per 100,000 in 2012, respectively. In 2014, 52.2% of cervical cancer patients were classified as stage I, 22.5% as stage II, 10.2% as stage III, and 11.2% as stage IV. In addition, 71.9% of endometrial cancer patients were classified as stage I, 6.0% as stage II, 13.3% as stage III, and 7.5% as stage IV. Finally, 43.2% of ovarian cancer patients were classified as stage I, 9.1% as stage II, 27.6% as stage III, and 7.2% as stage IV. Twelve-point six percent of ovarian cancer patients received neoadjuvant chemotherapy. Copyright © 2017. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology.

Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.

PubMed

Stember, Joseph N; Deng, Fang-Ming; Taneja, Samir S; Rosenkrantz, Andrew B

2014-08-01

To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input. Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors. In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence. Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input. © 2013 Wiley Periodicals, Inc.

Matching mice to malignancy: molecular subgroups and models of medulloblastoma

PubMed Central

Lau, Jasmine; Schmidt, Christin; Markant, Shirley L.; Taylor, Michael D.; Wechsler-Reya, Robert J.

2012-01-01

Introduction Medulloblastoma, the largest group of embryonal brain tumors, has historically been classified into five variants based on histopathology. More recently, epigenetic and transcriptional analyses of primary tumors have sub-classified medulloblastoma into four to six subgroups, most of which are incongruous with histopathological classification. Discussion Improved stratification is required for prognosis and development of targeted treatment strategies, to maximize cure and minimize adverse effects. Several mouse models of medulloblastoma have contributed both to an improved understanding of progression and to developmental therapeutics. In this review, we summarize the classification of human medulloblastoma subtypes based on histopathology and molecular features. We describe existing genetically engineered mouse models, compare these to human disease, and discuss the utility of mouse models for developmental therapeutics. Just as accurate knowledge of the correct molecular subtype of medulloblastoma is critical to the development of targeted therapy in patients, we propose that accurate modeling of each subtype of medulloblastoma in mice will be necessary for preclinical evaluation and optimization of those targeted therapies. PMID:22315164

Impact of Tumor Factors on Survival in Patients with Hepatocellular Carcinoma Classified Based on Kinki Criteria Stage B2.

PubMed

Arizumi, Tadaaki; Minami, Tomohiro; Chishina, Hirokazu; Kono, Masashi; Takita, Masahiro; Yada, Norihisa; Hagiwara, Satoru; Minami, Yasunori; Ida, Hiroshi; Ueshima, Kazuomi; Kamata, Ken; Minaga, Kosuke; Komeda, Yoriaki; Takenaka, Mamoru; Sakurai, Toshiharu; Watanabe, Tomohiro; Nishida, Naoshi; Kudo, Masatoshi

2017-01-01

Tumors classified based on the Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) are heterogeneous in nature. Previously, the Kinki criterion was proposed for a more precise subclassification of tumors in BCLC-stage B. However, tumors in sub-stage B2 include various size and number of HCCs even with the Kinki criteria, which could lead to heterogeneity for overall survival (OS). In this study, we assessed how the size and number of tumors affect the OS and time to progression (TTP) in patients with Kinki criteria stage B2 tumors and treated with transarterial chemoembolization (TACE). Of 906 HCC patients treated with TACE at Kindai University Hospital, 236 patients with HCC considered as Kinki criteria stage B2 were examined. They were classified into the following 4 groups according to the maximum tumor diameter and number of tumors: B2a group, tumor size ≤6 cm and total number of tumors ≤6; B2b group, size ≤6 cm and number >6; B2c group, size >6 cm and number ≤6; and B2d group, size >6 cm and number >6. The OS and TTP of patients in each group were compared. There were 131 patients (55.5%) in the B2a group, 58 (24.6%) in the B2b group, 41 (17.4%) in the B2c group, and 6 (0.03%) in the B2d group. Comparison of the survivals revealed that the median OS was 2.8 years (95% CI 2.0-3.5) in the B2a group, 2.8 years (95% CI 2.0-3.3) in the B2b group, 1.9 years (95% CI 0.8-4.0) in the B2c group, and 2.3 years (95% CI 1.2-ND [no data]) in the B2d group, respectively (p = 0.896). The median TTP in B2a, B2b, B2c, and B2d sub-substage HCC were13.2, 12.1, 13.8, and 11.5 months, respectively (p = 0.047). The median TTP in B2a + B2c sub-substage patients was longer than that in B2b + B2d sub-substage HCC patients (14.0 months and 10.4 months; p = 0.002). No significant differences were observed in the OS among HCC patients subclassified based on the maximum tumor diameter and tumor number in Kinki criteria stage B2. Consequently, Kinki criteria stage B2 HCC is a homogeneous subgroup in terms of OS prediction. However, shorter TTP in B2b+B2c sub-substage HCC patients than that in B2a + B2c sub-substage HCC patients suggests that different treatment strategy, such as systemic therapy with targeted agents instead of TACE, may be suitable to preserve the liver function. © 2017 S. Karger AG, Basel.

Multi-fractal detrended texture feature for brain tumor classification

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Mays, Randall; Iftekharuddin, Khan M.

2015-03-01

We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

[Molecular biology for sarcoma: useful or necessary?].

PubMed

Neuville, Agnès; Coindre, Jean-Michel; Chibon, Frédéric

2015-01-01

Sarcomas are a heterogeneous group of tumors. Their diagnosis is based on morphology and immunohistochemical profile, with categories of tumors according to the type of tissue that they resemble. Nevertheless, for several tumors, cellular origin is unknown. Molecular analysis performed in recent years allowed, combining histophenotype and genomics, better classifying such sarcomas, individualizing new entities and grouping some tumors. Simple and recurrent genetic alterations, such as translocation, mutation, amplification, can be identified in one of two sarcomas and appear as new diagnostic markers. Their identification in specialized laboratories in molecular pathology of sarcomas is often useful and sometimes necessary for a good diagnosis, leading to a heavy and multidisciplinary multi-step treatment. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

T4 category revision enhances the accuracy and significance of stage III breast cancer.

PubMed

Güth, Uwe; Singer, Gad; Langer, Igor; Schötzau, Andreas; Herberich, Linda; Holzgreve, Wolfgang; Wight, Edward

2006-06-15

Because of the considerable heterogeneity in breast carcinoma with noninflammatory skin involvement (T4b/Stage IIIB), a revision was proposed of the TNM staging system that would classify these tumors exclusively based on their tumor size and lymph node status. In the current study, the authors evaluated how implementation of this proposal will affect Stage III noninflammatory breast cancer. Two hundred seven patients who were classified with noninflammatory Stage III breast cancer were treated consecutively between 1990 and 1999 at the University Hospital Basel, Switzerland. To assess the extent of T4b/Stage IIIB tumors independent of the clinicopathologic feature of skin involvement, the reclassification was undertaken. Of 68 patients who had nonmetastatic T4b breast cancer, 37 patients (54.4%) had a tumor extent in accordance with Stage I/II and had improved disease-specific survival (DSS) compared with patients who had Stage III breast cancer (P = .008). Excluding those patients from Stage III led to a 17.9% reduction of the number of patients in this group (n = 170 patients). The 10-year DSS declined from 48.5% to 42.9%. Considerable numbers of patients who are classified with noninflammatory Stage IIIB breast cancer show only a limited disease extent. Through a revision of the T4 category, these low-risk patients were excluded from the highest nonmetastatic TNM stage, and overstaging could be avoided. This procedure decreased the degree of heterogeneity of the entire Stage III group and may result in a more precise assessment of this disease entity. Copyright 2006 American Cancer Society.

The case for DNA methylation based molecular profiling to improve diagnostic accuracy for central nervous system embryonal tumors (not otherwise specified) in adults.

PubMed

Halliday, Gail C; Junckerstorff, Reimar C; Bentel, Jacqueline M; Miles, Andrew; Jones, David T W; Hovestadt, Volker; Capper, David; Endersby, Raelene; Cole, Catherine H; van Hagen, Tom; Gottardo, Nicholas G

2018-01-01

Central nervous system primitive neuro-ectodermal tumors (CNS-PNETs), have recently been re-classified in the most recent 2016 WHO Classification into a standby catch all category, "CNS Embryonal Tumor, not otherwise specified" (CNS embryonal tumor, NOS) based on epigenetic, biologic and histopathologic criteria. CNS embryonal tumors (NOS) are a rare, histologically and molecularly heterogeneous group of tumors that predominantly affect children, and occasionally adults. Diagnosis of this entity continues to be challenging and the ramifications of misdiagnosis of this aggressive class of brain tumors are significant. We report the case of a 45-year-old woman who was diagnosed with a central nervous system embryonal tumor (NOS) based on immunohistochemical analysis of the patient's tumor at diagnosis. However, later genome-wide methylation profiling of the diagnostic tumor undertaken to guide treatment, revealed characteristics most consistent with IDH-mutant astrocytoma. DNA sequencing and immunohistochemistry confirmed the presence of IDH1 and ATRX mutations resulting in a revised diagnosis of high-grade small cell astrocytoma, and the implementation of a less aggressive treatment regime tailored more appropriately to the patient's tumor type. This case highlights the inadequacy of histology alone for the diagnosis of brain tumours and the utility of methylation profiling and integrated genomic analysis for the diagnostic verification of adults with suspected CNS embryonal tumor (NOS), and is consistent with the increasing realization in the field that a combined diagnostic approach based on clinical, histopathological and molecular data is required to more accurately distinguish brain tumor subtypes and inform more effective therapy. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Analysis of vestibular testing in patients with vestibular schwannoma based on the nerve of origin, the localization, and the size of the tumor.

PubMed

Suzuki, Mitsuya; Yamada, Chikako; Inoue, Rika; Kashio, Akinori; Saito, Yuki; Nakanishi, Wakako

2008-10-01

We aimed to analyze the factors influencing caloric response and vestibular evoked myogenic potential (VEMP) in vestibular schwannoma. The subjects comprised 130 patients with unilateral vestibular schwannoma pathologically diagnosed by surgery. Caloric response and the amplitude and latency of VEMP were measured and analyzed based on the nerve of origin, localization, and size of the tumor. The tumors were classified into 3 types based on localization: intracanalicular, intermediate, and medial; and into 4 grades based on size: 9 mm or less, 10 to 19 mm, 20 to 29 mm, and 30 mm or greater. : Abnormal rates of caloric response and VEMP in patients with tumors arising from the superior vestibular nerve were not significantly different from those in patients with tumors of the inferior vestibular nerve. In the intermediate and medial type-but not in the intracanalicular type-a significant difference in tumor size was observed between patients with normal caloric response and those with canal paresis as also between patients with normal VEMP and those with abnormal VEMP. In patients with tumors that maximally measured 10 to 19 mm or of the intermediate type, the p- and n-wave latencies of VEMP were significantly prolonged compared with those in the normal opposite ear. 1) The nerve of origin of tumors cannot be predicted based on caloric response and VEMP. 2) In the intermediate and medial types, caloric response and the VEMP amplitude are significantly diminished in association with an increase in tumor size. 3) Prolonged VEMP latencies seem to be not only caused by tumor compression to the brainstem or vestibular spinal tract but also by tumor compression isolated to the inferior vestibular nerve.

L1CAM Expression is Related to Non-Endometrioid Histology, and Prognostic for Poor Outcome in Endometrioid Endometrial Carcinoma.

PubMed

Geels, Yvette P; Pijnenborg, Johanna M A; Gordon, Bart B M; Fogel, Mina; Altevogt, Peter; Masadah, Rina; Bulten, Johan; van Kempen, Léon C; Massuger, Leon F A G

2016-10-01

The majority of endometrial carcinomas are classified as Type I endometrioid endometrial carcinomas (EECs) and have a good prognosis. Type II non-endometrioid endometrial carcinomas (NEECs) have a significant worse outcome. Yet, 20 % of the EECs are associated with an unexplained poor outcome. The aim of this study was to determine if L1CAM expression, a recently reported biomarker for aggressive tumor behavior in endometrial carcinoma, was associated with clinicopathological features of EECs. A total of 103 patients diagnosed as EEC at the Radboud University Medical Centre, based on the pathology report were selected. L1CAM status of these tumors was determined, and histologic slides were reviewed by two expert pathologists. L1CAM-positivity was observed in 17 % (18/103). Review of the diagnostic slides revealed that 11 out of these 18 L1CAM-positive tumors (61 %) contained a serous- or mixed carcinoma component that was not initially mentioned in the pathology report. L1CAM-expression was associated with advanced age, poor tumor grade, and lymphovascular space invasion. A worse five year progression free survival rate was observed for patients with L1CAM-positive tumors (55.6 % for the L1CAM-positive group, compared to 83.3 % for the L1CAM-negative group P = 0.01). L1CAM expression carries prognostic value for histologically classified EEC and supports the identification of tumors with a NEEC component.

Multiclass cancer diagnosis using tumor gene expression signatures

DOE PAGES

Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

2001-12-11

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

Use of electron microscopy to classify canine perivascular wall tumors.

PubMed

Palmieri, C; Avallone, G; Cimini, M; Roccabianca, P; Stefanello, D; Della Salda, L

2013-03-01

The histologic classification of canine perivascular wall tumors (PWTs) is controversial. Many PWTs are still classified as hemangiopericytomas (HEPs), and the distinction from peripheral nerve sheath tumors (PNSTs) is still under debate. A recent histologic classification of canine soft tissue sarcomas included most histologic types of PWT but omitted those that were termed undifferentiated. Twelve cases of undifferentiated canine PWTs were evaluated by transmission electron microscopy. The ultrastructural findings supported a perivascular wall origin for all cases with 4 categories of differentiation: myopericytic (n = 4), myofibroblastic (n = 1), fibroblastic (n = 2), and undifferentiated (n = 5). A PNST was considered unlikely in each case based on immunohistochemical expression of desmin and/or the lack of typical ultrastructural features, such as basal lamina. Electron microscopy was pivotal for the subclassification of canine PWTs, and the results support the hypothesis that canine PWTs represent a continuum paralleling the phenotypic plasticity of vascular mural cells. The hypothesis that a subgroup of PWTs could arise from a pluripotent mesenchymal perivascular wall cell was also considered and may explain the diverse differentiation of canine PWTs.

Automated noninvasive classification of renal cancer on multiphase CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Linguraru, Marius George; Wang, Shijun; Shah, Furhawn

2011-10-15

Purpose: To explore the added value of the shape of renal lesions for classifying renal neoplasms. To investigate the potential of computer-aided analysis of contrast-enhanced computed-tomography (CT) to quantify and classify renal lesions. Methods: A computer-aided clinical tool based on adaptive level sets was employed to analyze 125 renal lesions from contrast-enhanced abdominal CT studies of 43 patients. There were 47 cysts and 78 neoplasms: 22 Von Hippel-Lindau (VHL), 16 Birt-Hogg-Dube (BHD), 19 hereditary papillary renal carcinomas (HPRC), and 21 hereditary leiomyomatosis and renal cell cancers (HLRCC). The technique quantified the three-dimensional size and enhancement of lesions. Intrapatient and interphasemore » registration facilitated the study of lesion serial enhancement. The histograms of curvature-related features were used to classify the lesion types. The areas under the curve (AUC) were calculated for receiver operating characteristic curves. Results: Tumors were robustly segmented with 0.80 overlap (0.98 correlation) between manual and semi-automated quantifications. The method further identified morphological discrepancies between the types of lesions. The classification based on lesion appearance, enhancement and morphology between cysts and cancers showed AUC = 0.98; for BHD + VHL (solid cancers) vs. HPRC + HLRCC AUC = 0.99; for VHL vs. BHD AUC = 0.82; and for HPRC vs. HLRCC AUC = 0.84. All semi-automated classifications were statistically significant (p < 0.05) and superior to the analyses based solely on serial enhancement. Conclusions: The computer-aided clinical tool allowed the accurate quantification of cystic, solid, and mixed renal tumors. Cancer types were classified into four categories using their shape and enhancement. Comprehensive imaging biomarkers of renal neoplasms on abdominal CT may facilitate their noninvasive classification, guide clinical management, and monitor responses to drugs or interventions.« less

[Categorization of uterine cervix tumors : What's new in the 2014 WHO classification].

PubMed

Lax, S F; Horn, L-C; Löning, T

2016-11-01

In the 2014 WHO classification, squamous cell precursor lesions are classified as low-grade and high-grade intraepithelial lesions. LSIL corresponds to CIN1, HSIL includes CIN2 and CIN3. Only adenocarcinoma in situ (AIS) is accepted as precursor of adenocarcinoma and includes the stratified mucin-producing intraepithelial lesion (SMILE). Although relatively rare, adenocarcinoma and squamous cell carcinoma can be mixed with a poorly differentiated neuroendocrine carcinoma. Most cervical adenocarcinomas are low grade and of endocervical type. Mucinous carcinomas show marked intra- and extracellular mucin production. Almost all squamous cell carcinomas, the vast majority of adenocarcinomas, and many rare carcinoma types are HPV related. For low grade endocervical adenocarcinomas, the pattern-based classification according to Silva should be reported. Neuroendocrine tumors are rare and are classified into low-grade and high-grade, whereby the term carcinoid is still used.

Childhood Central Nervous System Embryonal Tumors (PDQ®)—Health Professional Version

Cancer.gov

Pediatric CNS embryonal tumors are a collection of heterogeneous lesions (medulloblastoma, and nonmedulloblastoma). Molecular genetic studies are used to classify embryonal tumors, stratify risk, and plan treatment. Get detailed information about tumor biology, diagnosis, prognosis, and treatment of untreated and recurrent CNS embryonal tumors in this summary for clinicians.

Metastatic breast carcinomas display genomic and transcriptomic heterogeneity

PubMed Central

Weigelt, Britta; Ng, Charlotte KY; Shen, Ronglai; Popova, Tatiana; Schizas, Michail; Natrajan, Rachael; Mariani, Odette; Stern, Marc-Henri; Norton, Larry; Vincent-Salomon, Anne; Reis-Filho, Jorge S

2015-01-01

Metaplastic breast carcinoma is a rare and aggressive histologic type of breast cancer, preferentially displaying a triple-negative phenotype. We sought to define the transcriptomic heterogeneity of metaplastic breast cancers on the basis of current gene expression microarray-based classifiers, and to determine whether these tumors display gene copy number profiles consistent with those of BRCA1-associated breast cancers. Twenty-eight consecutive triple-negative metaplastic breast carcinomas were reviewed, and the metaplastic component present in each frozen specimen was defined (ie, spindle cell, squamous, chondroid metaplasia). RNA and DNA extracted from frozen sections with tumor cell content >60% were subjected to gene expression (Illumina HumanHT-12 v4) and copy number profiling (Affymetrix SNP 6.0), respectively. Using the best practice PAM50/claudin-low microarray-based classifier, all metaplastic breast carcinomas with spindle cell metaplasia were of claudin-low subtype, whereas those with squamous or chondroid metaplasia were preferentially of basal-like subtype. Triple-negative breast cancer subtyping using a dedicated website (http://cbc.mc.vanderbilt.edu/tnbc/) revealed that all metaplastic breast carcinomas with chondroid metaplasia were of mesenchymal-like subtype, spindle cell carcinomas preferentially of unstable or mesenchymal stem-like subtype, and those with squamous metaplasia were of multiple subtypes. None of the cases was classified as immunomodulatory or luminal androgen receptor subtype. Integrative clustering, combining gene expression and gene copy number data, revealed that metaplastic breast carcinomas with spindle cell and chondroid metaplasia were preferentially classified as of integrative clusters 4 and 9, respectively, whereas those with squamous metaplasia were classified into six different clusters. Eight of the 26 metaplastic breast cancers subjected to SNP6 analysis were classified as BRCA1-like. The diversity of histologic features of metaplastic breast carcinomas is reflected at the transcriptomic level, and an association between molecular subtypes and histology was observed. BRCA1-like genomic profiles were found only in a subset (31%) of metaplastic breast cancers, and were not associated with a specific molecular or histologic subtype. PMID:25412848

Association of diabetes and diabetes treatment with incidence of breast cancer.

PubMed

García-Esquinas, Esther; Guinó, Elisabeth; Castaño-Vinyals, Gemma; Pérez-Gómez, Beatriz; Llorca, Javier; Altzibar, Jone M; Peiró-Pérez, Rosana; Martín, Vicente; Moreno-Iribas, Concepción; Tardón, Adonina; Caballero, Francisco Javier; Puig-Vives, Montse; Guevara, Marcela; Villa, Tania Fernández; Salas, Dolores; Amiano, Pilar; Dierssen-Sotos, Trinidad; Pastor-Barriuso, Roberto; Sala, María; Kogevinas, Manolis; Aragonés, Nuria; Moreno, Víctor; Pollán, Marina

2016-02-01

The aim of this study was to evaluate the association of diabetes and diabetes treatment with risk of postmenopausal breast cancer. Histologically confirmed incident cases of postmenopausal breast (N = 916) cancer were recruited from 23 Spanish public hospitals. Population-based controls (N = 1094) were randomly selected from primary care center lists within the catchment areas of the participant hospitals. ORs (95 % CI) were estimated using mixed-effects logistic regression models, using the recruitment center as a random effect term. Breast tumors were classified into hormone receptor positive (ER+ or PR+), HER2+ and triple negative (TN). Diabetes was not associated with the overall risk of breast cancer (OR 1.09; 95 % CI 0.82-1.45), and it was only linked to the risk of developing TN tumors: Among 91 women with TN tumors, 18.7 % were diabetic, while the corresponding figure among controls was 9.9 % (OR 2.25; 95 % CI 1.22-4.15). Regarding treatment, results showed that insulin use was more prevalent among diabetic cases (2.5 %) as compared to diabetic controls (0.7 %); OR 2.98; 95 % CI 1.26-7.01. They also showed that, among diabetics, the risk of developing HR+/HER2- tumors decreased with longer metformin use (ORper year 0.89; 95 % CI 0.81-0.99; based on 24 cases and 43 controls). This study reinforces the need to correctly classify breast cancers when studying their association with diabetes. Given the low survival rates in women diagnosed with TN breast tumors and the potential impact of diabetes control on breast cancer prevention, more studies are needed to better characterize this association.

Reconstructing the Prostate Cancer Transcriptional Regulatory Network

DTIC Science & Technology

2010-07-01

the Medical Scientist Training Program. The funders had no role in study design , data collection and analysis , decision to publish, or preparation of...reverse analysis , building a cell line subtype classifier to classify 86 breast tumors (from the original Stanford/Norway study defining the five tumor...Army position, policy or decision unless so designated by other documentation. REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public

A binary histologic grading system for ovarian serous carcinoma is an independent prognostic factor: a population-based study of 4317 women diagnosed in Denmark 1978-2006.

PubMed

Hannibal, Charlotte Gerd; Vang, Russell; Junge, Jette; Kjaerbye-Thygesen, Anette; Kurman, Robert J; Kjaer, Susanne K

2012-06-01

To evaluate the prognostic significance of histologic grade on survival of ovarian serous cancer in Denmark during nearly 30 years. Using the nationwide Danish Pathology Data Bank, we evaluated 4317 women with ovarian serous carcinoma in 1978-2006. All pathology reports were scrutinized and tumors classified as either low-grade serous carcinomas (LGSC) or high-grade serous carcinomas (HGSC). Tumors in which the original pathology reports were described as well-differentiated were classified as LGSC, and those that were described as moderately or poorly differentiated were classified as HGSC. We obtained histologic slides from the pathology departments for women with a diagnosis of well-differentiated serous carcinoma during 1997-2006, which were then reviewed by expert gynecologic pathologists. Data were analyzed using Kaplan-Meier methods and Cox proportional hazards regression analysis with follow-up through June 2009. Women with HGSC had a significantly increased risk of dying (HR=1.9; 95% CI: 1.6-2.3) compared with women with LGSC while adjusting for age and stage. Expert review of 171 women originally classified as well-differentiated in 1997-2006 were interpreted as LGSC in 30% of cases, whereas 12% were interpreted as HGSC and 50% as serous borderline ovarian tumors (SBT). Compared with women with confirmed LGSC, women with SBT at review had a significantly lower risk of dying (HR=0.5; 95% CI: 0.22-0.99), and women with HGSC at review had a non-significantly increased risk of dying (HR=1.6; 95% CI: 0.7-3.4). A binary grading system is a significant predictor of survival for ovarian serous carcinoma. Copyright © 2012 Elsevier Inc. All rights reserved.

Performance of 177Lu-DOTATATE-based peptide receptor radionuclide therapy in metastatic gastroenteropancreatic neuroendocrine tumor: a multiparametric response evaluation correlating with primary tumor site, tumor proliferation index, and dual tracer imaging characteristics.

PubMed

Thapa, Pradeep; Ranade, Rohit; Ostwal, Vikas; Shrikhande, Shailesh V; Goel, Mahesh; Basu, Sandip

2016-10-01

To assess the performance of Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) in metastatic gastroenteropancreatic neuroendocrine tumor (GEP-NET) and correlate it with primary tumor site, tumor proliferation index, and dual tracer imaging characteristics. Fifty patients (M : F 33 : 17, age: 26-71 years) with histopathologically confirmed metastatic/inoperable NETs who had undergone at least three cycles of PRRT with Lu-DOTATATE were included in the analysis. As part of the pretreatment evaluation, they underwent either Tc-HYNIC TOC (n=40)/Ga-DOTATATE PET (n=10) or fluorine-18-fluorodeoxyglucose (F-FDG) PET-computed tomography (CT). Response was assessed after three and five cycles PRRT on the basis of three parameters: (a) symptomatic and subjective scale, (b) biochemical tumor marker level, and (c) objective imaging (F-FDG/Ga DOTATATE PET/CT, Tc-HYNIC TOC, ceCT), and was categorized using predefined criteria (detailed in methods). Stable disease on imaging assessment with response on symptomatic or biochemical tumor marker scales or both were included in the responder group. The study population was broadly classified into (a) metastatic GEP-NET with known primary (n=43 i.e. 86%), which was further subclassified according to the site of primary and (b) those with unknown primary (n=7 i.e. 14%). Symptomatic response: 96% of patients showed a symptomatic response or improvement in health-related quality of life, irrespective of tumor proliferation index, dual tracer imaging characteristics, and response or progression of disease in the scan. Biochemical tumor marker response: 83% of scan responders showed a decrease, 10% showed a stable value, and 7% showed an increase in tumor marker levels. Among the scan nonresponders, 67% patients showed a corresponding increase in the tumor marker level, 22% patient showed a decrease, whereas 11% showed stable values. Scan response: 31 out of total 50 patients (62%) showed a partial scan response with either a decrease in the number of somatostatin receptor (SSTR)-positive lesions or metabolic activity in F-FDG/Ga-DOTATATE PET-CT or both, 10 patients (20%) showed stable disease, and nine patients (18%) showed progressive disease. The higher objective partial scan response documented can be explained by the introduction of the F-FDG-PET/CT parameter as a determinant criterion. Among the responders category (n=41), 32 (78.04%) showed discordance between F-FDG-PET/CT-based and SSTR-based imaging, whereas eight out of nine patients with nonresponse category (88.89%) showed concordance between SSTR-based imaging and F-FDG-PET/CT. Conversely, 32 of 33 patients (96.97%) with SSTR/F-FDG discordance and nine out of 17 (52.94%) with concordance were finally classified as responders, whereas the remaining, that is, 1/33 (3.03%) in the 'discordant' category and 8/17 (47.06%) with imaging concordance were classified as nonresponders, respectively. Our data show that high pretherapy F-FDG maximum standardized uptake values were associated with increased chances of treatment refractoriness in GEP-NETs. However, symptomatic improvement was observed in most cases irrespective of grade and F-FDG uptake. High pretherapy F-FDG maximum standardized uptake value in both low-grade and high-grade NET predicted a poor outcome and was associated with disease progression. Introduction of F-FDG-PET/CT parameter as a determinant of response classification increases the percentage of objective scan responders among patients with grades I and II GEP-NETs as F-FDG activity was observed to decrease before SSTR-based imaging and more frequently compared with the latter.

Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes.

PubMed

Roche, Kimberly E; Weinstein, Marvin; Dunwoodie, Leland J; Poehlman, William L; Feltus, Frank A

2018-05-25

We applied two state-of-the-art, knowledge independent data-mining methods - Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) - to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn't detectable with the core biomarker transcripts present. This suggests the existence of a "background classification" potential in which the pattern of gene expression after continued removal of "biomarker" transcripts could still classify tumors in agreement with the tumor type.

Development Process and Technical Aspects of Laparoscopic Hepatectomy: Learning Curve Based on 15 Years of Experience.

PubMed

Komatsu, Shohei; Scatton, Olivier; Goumard, Claire; Sepulveda, Ailton; Brustia, Raffaele; Perdigao, Fabiano; Soubrane, Olivier

2017-05-01

Laparoscopic hepatectomy continues to be a challenging operation associated with a steep learning curve. This study aimed to evaluate the learning process during 15 years of experience with laparoscopic hepatectomy and to identify approaches to standardization of this procedure. Prospectively collected data of 317 consecutive laparoscopic hepatectomies performed from January 2000 to December 2014 were reviewed retrospectively. The operative procedures were classified into 4 categories (minor hepatectomy, left lateral sectionectomy [LLS], left hepatectomy, and right hepatectomy), and indications were classified into 5 categories (benign-borderline tumor, living donor, metastatic liver tumor, biliary malignancy, and hepatocellular carcinoma). During the first 10 years, the procedures were limited mainly to minor hepatectomy and LLS, and the indications were limited to benign-borderline tumor and living donor. Implementation of major hepatectomy rapidly increased the proportion of malignant tumors, especially hepatocellular carcinoma, starting from 2011. Conversion rates decreased with experience for LLS (13.3% vs 3.4%; p = 0.054) and left hepatectomy (50.0% vs 15.0%; p = 0.012), but not for right hepatectomy (41.4% vs 35.7%; p = 0.661). Our 15-year experience clearly demonstrates the stepwise procedural evolution from LLS through left hepatectomy to right hepatectomy, as well as the trend in indications from benign-borderline tumor/living donor to malignant tumors. In contrast to LLS and left hepatectomy, a learning curve was not observed for right hepatectomy. The ongoing development process can contribute to faster standardization necessary for future advances in laparoscopic hepatectomy. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

PubMed

Even, Aniek J G; Reymen, Bart; La Fontaine, Matthew D; Das, Marco; Jochems, Arthur; Mottaghy, Felix M; Belderbos, José S A; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-11-01

Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model. We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Computer aided detection of tumor and edema in brain FLAIR magnetic resonance image using ANN

NASA Astrophysics Data System (ADS)

Pradhan, Nandita; Sinha, A. K.

2008-03-01

This paper presents an efficient region based segmentation technique for detecting pathological tissues (Tumor & Edema) of brain using fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. This work segments FLAIR brain images for normal and pathological tissues based on statistical features and wavelet transform coefficients using k-means algorithm. The image is divided into small blocks of 4×4 pixels. The k-means algorithm is used to cluster the image based on the feature vectors of blocks forming different classes representing different regions in the whole image. With the knowledge of the feature vectors of different segmented regions, supervised technique is used to train Artificial Neural Network using fuzzy back propagation algorithm (FBPA). Segmentation for detecting healthy tissues and tumors has been reported by several researchers by using conventional MRI sequences like T1, T2 and PD weighted sequences. This work successfully presents segmentation of healthy and pathological tissues (both Tumors and Edema) using FLAIR images. At the end pseudo coloring of segmented and classified regions are done for better human visualization.

Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil.

PubMed

Lei, Zhengdeng; Tan, Iain Beehuat; Das, Kakoli; Deng, Niantao; Zouridis, Hermioni; Pattison, Sharon; Chua, Clarinda; Feng, Zhu; Guan, Yeoh Khay; Ooi, Chia Huey; Ivanova, Tatiana; Zhang, Shenli; Lee, Minghui; Wu, Jeanie; Ngo, Anna; Manesh, Sravanthy; Tan, Elisabeth; Teh, Bin Tean; So, Jimmy Bok Yan; Goh, Liang Kee; Boussioutas, Alex; Lim, Tony Kiat Hon; Flotow, Horst; Tan, Patrick; Rozen, Steven G

2013-09-01

Almost all gastric cancers are adenocarcinomas, which have considerable heterogeneity among patients. We sought to identify subtypes of gastric adenocarcinomas with particular biological properties and responses to chemotherapy and targeted agents. We compared gene expression patterns among 248 gastric tumors; using a robust method of unsupervised clustering, consensus hierarchical clustering with iterative feature selection, we identified 3 major subtypes. We developed a classifier for these subtypes and validated it in 70 tumors from a different population. We identified distinct genomic and epigenomic properties of the subtypes. We determined drug sensitivities of the subtypes in primary tumors using clinical survival data, and in cell lines through high-throughput drug screening. We identified 3 subtypes of gastric adenocarcinoma: proliferative, metabolic, and mesenchymal. Tumors of the proliferative subtype had high levels of genomic instability, TP53 mutations, and DNA hypomethylation. Cancer cells of the metabolic subtype were more sensitive to 5-fluorouracil than the other subtypes. Furthermore, in 2 independent groups of patients, those with tumors of the metabolic subtype appeared to have greater benefits with 5-fluorouracil treatment. Tumors of the mesenchymal subtype contain cells with features of cancer stem cells, and cell lines of this subtype are particularly sensitive to phosphatidylinositol 3-kinase-AKT-mTOR inhibitors in vitro. Based on gene expression patterns, we classified gastric cancers into 3 subtypes, and validated these in an independent set of tumors. The subgroups have differences in molecular and genetic features and response to therapy; this information might be used to select specific treatment approaches for patients with gastric cancer. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.

Identification of two clinical hepatocellular carcinoma patient phenotypes from results of standard screening parameters

PubMed Central

Carr, Brian I.; Giannini, Edoardo G.; Farinati, Fabio; Ciccarese, Francesca; Rapaccini, Gian Ludovico; Marco, Maria Di; Benvegnù, Luisa; Zoli, Marco; Borzio, Franco; Caturelli, Eugenio; Chiaramonte, Maria; Trevisani, Franco

2014-01-01

Background Previous work has shown that 2 general processes contribute to hepatocellular cancer (HCC) prognosis. They are: a. liver damage, monitored by indices such as blood bilirubin, prothrombin time and AST; as well as b. tumor biology, monitored by indices such as tumor size, tumor number, presence of PVT and blood AFP levels. These 2 processes may affect one another, with prognostically significant interactions between multiple tumor and host parameters. These interactions form a context that provide personalization of the prognostic meaning of these factors for every patient. Thus, a given level of bilirubin or tumor diameter might have a different significance in different personal contexts. We previously applied Network Phenotyping Strategy (NPS) to characterize interactions between liver function indices of Asian HCC patients and recognized two clinical phenotypes, S and L, differing in tumor size and tumor nodule numbers. Aims To validate the applicability of the NPS-based HCC S/L classification on an independent European HCC cohort, for which survival information was additionally available. Methods Four sets of peripheral blood parameters, including AFP-platelets, derived from routine blood parameter levels and tumor indices from the ITA.LI.CA database, were analyzed using NPS, a graph-theory based approach, which compares personal patterns of complete relationships between clinical data values to reference patterns with significant association to disease outcomes. Results Without reference to the actual tumor sizes, patients were classified by NPS into 2 subgroups with S and L phenotypes. These two phenotypes were recognized using solely the HCC screening test results, consisting of eight common blood parameters, paired by their significant correlations, including an AFP-Platelets relationship. These trends were combined with patient age, gender and self-reported alcoholism into NPS personal patient profiles. We subsequently validated (using actual scan data) that patients in L phenotype group had 1.5x larger mean tumor masses relative to S, p=6×10−16. Importantly, with the new data, liver test pattern-identified S-phenotype patients had typically 1.7 × longer survival compared to L-phenotype. NPS integrated the liver, tumor and basic demographic factors. Cirrhosis associated thrombocytopenia was typical for smaller S-tumors. In L-tumor phenotype, typical platelet levels increased with the tumor mass. Hepatic inflammation and tumor factors contributed to more aggressive L tumors, with parenchymal destruction and shorter survival. Summary NPS provides integrative interpretation for HCC behavior, identifying two tumor and survival phenotypes by clinical parameter patterns. The NPS classifier is provided as an Excel tool. The NPS system shows the importance of considering each tumor marker and parameter in the total context of all the other parameters of an individual patient. PMID:25023357

A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass

PubMed Central

Moore, Richard G.; McMeekin, D. Scott; Brown, Amy K.; DiSilvestro, Paul; Miller, M. Craig; Allard, W. Jeffrey; Gajewski, Walter; Kurman, Robert; Bast, Robert C.; Skates, Steven J.

2012-01-01

Introduction Patients diagnosed with epithelial ovarian cancer (EOC) have improved outcomes when cared for at centers experienced in the management of EOC. The objective of this trial was to validate a predictive model to assess the risk for EOC in women with a pelvic mass. Methods Women diagnosed with a pelvic mass and scheduled to have surgery were enrolled on a multicenter prospective study. Preoperative serum levels of HE4 and CA125 were measured. Separate logistic regression algorithms for premenopausal and postmenopausal women were utilized to categorize patients into low and high risk groups for EOC. Results Twelve sites enrolled 531 evaluable patients with 352 benign tumors, 129 EOC, 22 LMP tumors, 6 non EOC and 22 non ovarian cancers. The postmenopausal group contained 150 benign cases of which 112 were classified as low risk giving a specificity of 75.0% (95% CI 66.9-81.4), and 111 EOC and 6 LMP tumors of which 108 were classified as high risk giving a sensitivity of 92.3% (95% CI=85.9-96.4). The premenopausal group had 202 benign cases of which 151 were classified as low risk providing a specificity of 74.8% (95% CI=68.2--80.6), and 18 EOC and 16 LMP tumors of which 26 were classified as high risk, providing a sensitivity of 76.5% (95% CI=58.8--89.3). Conclusion An algorithm utilizing HE4 and CA125 successfully classified patients into high and low risk groups with 93.8% of EOC correctly classified as high risk. This model can be used to effectively triage patients to centers of excellence. PMID:18851871

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma.

PubMed

Klett, Hagen; Fuellgraf, Hannah; Levit-Zerdoun, Ella; Hussung, Saskia; Kowar, Silke; Küsters, Simon; Bronsert, Peter; Werner, Martin; Wittel, Uwe; Fritsch, Ralph; Busch, Hauke; Boerries, Melanie

2018-01-01

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic.

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma

PubMed Central

Klett, Hagen; Fuellgraf, Hannah; Levit-Zerdoun, Ella; Hussung, Saskia; Kowar, Silke; Küsters, Simon; Bronsert, Peter; Werner, Martin; Wittel, Uwe; Fritsch, Ralph; Busch, Hauke; Boerries, Melanie

2018-01-01

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic. PMID:29675033

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

PubMed

Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

2017-06-01

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

[Evaluation of cardiac tumors by multidetector computed tomography and magnetic resonance imaging].

PubMed

Mercado-Guzman, Marcela P; Meléndez-Ramírez, Gabriela; Castillo-Castellon, Francisco; Kimura-Hayama, Eric

Cardiac tumors, are a rare pathology (0.002-0.3%) in all age groups, however, they have a clinic importance, due the affected organ. They are classified in primary (benign or malignant) and secondary (metastasis) types. Among primary type, mixoma, is the most common benign tumor, and sarcoma represents most of the malignant injuries. Cardiac metastasis are more frequent than primary tumors. Clinic effects of cardiac tumors are unspecific and vary according their location, size and agresivity. The use of Multidetector Computed Tomography (MDCT) and Magnetic Resonance Imaging (MRI) assist on the location, sizing, anatomical relationships and the compromise of adyacents structures, besides, MRI is useful for tissue characterization of the tumor. Due to the previous reasons, studies based on noninvasive cardiovascular imaging, have an important role on the characterization of these lesions and the differential diagnosis among them. Copyright © 2016 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

Childhood Extracranial Germ Cell Tumors Treatment (PDQ®)—Health Professional Version

Cancer.gov

Childhood extracranial germ cell tumors (GCTs) are classified as teratomas (immature, mature) or malignant GCTs (seminoma, dysgerminoma, germinoma, yolk sac tumor, choriocarcinoma, embryonal carcinoma, mixed GCT). Get detailed information about newly diagnosed and recurrent extracranial GCTs including symptoms, diagnosis, histology, tumor biology, classification, prognosis, staging, and treatment in this summary for clinicians.

Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Shiju; Qian, Wei; Guan, Yubao

2016-06-15

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk prediction performance for stage I NSCLS patients by integrating oversampling, feature selection, and score fusion techniques and develop an optimal prediction model. Methods: A dataset involving 94 early stage lung cancer patients was retrospectively assembled, which includes CT images, nine clinical and biological (CB) markers, and outcome of 3-yr disease-free survival (DFS) after surgery. Among the 94 patients, 74 remained DFS and 20 had cancer recurrence. Applying a computer-aided detection scheme, tumors were segmented from the CT images and 35 quantitative image (QI) features were initiallymore » computed. Two normalized Gaussian radial basis function network (RBFN) based classifiers were built based on QI features and CB markers separately. To improve prediction performance, the authors applied a synthetic minority oversampling technique (SMOTE) and a BestFirst based feature selection method to optimize the classifiers and also tested fusion methods to combine QI and CB based prediction results. Results: Using a leave-one-case-out cross-validation (K-fold cross-validation) method, the computed areas under a receiver operating characteristic curve (AUCs) were 0.716 ± 0.071 and 0.642 ± 0.061, when using the QI and CB based classifiers, respectively. By fusion of the scores generated by the two classifiers, AUC significantly increased to 0.859 ± 0.052 (p < 0.05) with an overall prediction accuracy of 89.4%. Conclusions: This study demonstrated the feasibility of improving prediction performance by integrating SMOTE, feature selection, and score fusion techniques. Combining QI features and CB markers and performing SMOTE prior to feature selection in classifier training enabled RBFN based classifier to yield improved prediction accuracy.« less

Lung Adenocarcinoma with Anaplastic Lymphoma Kinase (ALK) Rearrangement Presenting as Carcinoma of Unknown Primary Site: Recognition and Treatment Implications.

PubMed

Hainsworth, John D; Anthony Greco, F

Molecular cancer classifier assays are being used with increasing frequency to predict tissue of origin and direct site-specific therapy for patients with carcinoma of unknown primary site (CUP). We postulated some CUP patients predicted to have non-small-cell lung cancer (NSCLC) by molecular cancer classifier assay may have anaplastic lymphoma kinase (ALK) rearranged tumors, and benefit from treatment with ALK inhibitors. We retrospectively reviewed CUP patients who had the 92-gene molecular cancer classifier assay (CancerTYPE ID; bioTheranostics, Inc.) performed on tumor biopsies to identify patients predicted to have NSCLC. Beginning in 2011, we have tested these patients for ALK rearrangements and epidermal growth factor receptor (EGFR) activating mutations, based on the proven therapeutic value of these targets in NSCLC. We identified CUP patients with predicted NSCLC who were subsequently found to have ALK rearrangements. NSCLC was predicted by the molecular cancer classifier assay in 37 of 310 CUP patients. Twenty-one of these patients were tested for ALK rearrangements, and four had an EML4-ALK fusion gene detected. The diagnosis of lung cancer was strongly suggested in only one patient prior to molecular testing. One patient received ALK inhibitor treatment and has had prolonged benefit. We report on patients with lung adenocarcinoma and ALK rearrangements originally diagnosed as CUP who were identified using a molecular cancer classifier assay. Although ALK inhibitors treatment experience is limited, this newly identifiable group of lung cancer patients should be considered for therapy according to guidelines for stage IV ALK-positive NSCLC.

Lung Adenocarcinoma with Anaplastic Lymphoma Kinase (ALK) Rearrangement Presenting as Carcinoma of Unknown Primary Site: Recognition and Treatment Implications.

PubMed

Hainsworth, John D; Anthony Greco, F

2016-03-01

Molecular cancer classifier assays are being used with increasing frequency to predict tissue of origin and direct site-specific therapy for patients with carcinoma of unknown primary site (CUP). We postulated some CUP patients predicted to have non-small-cell lung cancer (NSCLC) by molecular cancer classifier assay may have anaplastic lymphoma kinase (ALK) rearranged tumors, and benefit from treatment with ALK inhibitors. We retrospectively reviewed CUP patients who had the 92-gene molecular cancer classifier assay (CancerTYPE ID; bioTheranostics, Inc.) performed on tumor biopsies to identify patients predicted to have NSCLC. Beginning in 2011, we have tested these patients for ALK rearrangements and epidermal growth factor receptor (EGFR) activating mutations, based on the proven therapeutic value of these targets in NSCLC. We identified CUP patients with predicted NSCLC who were subsequently found to have ALK rearrangements. NSCLC was predicted by the molecular cancer classifier assay in 37 of 310 CUP patients. Twenty-one of these patients were tested for ALK rearrangements, and four had an EML4-ALK fusion gene detected. The diagnosis of lung cancer was strongly suggested in only one patient prior to molecular testing. One patient received ALK inhibitor treatment and has had prolonged benefit. We report on patients with lung adenocarcinoma and ALK rearrangements originally diagnosed as CUP who were identified using a molecular cancer classifier assay. Although ALK inhibitors treatment experience is limited, this newly identifiable group of lung cancer patients should be considered for therapy according to guidelines for stage IV ALK-positive NSCLC.

Regularization strategies for hyperplane classifiers: application to cancer classification with gene expression data.

PubMed

Andries, Erik; Hagstrom, Thomas; Atlas, Susan R; Willman, Cheryl

2007-02-01

Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.

Endocrine tumors of the duodenum. A study of 55 cases relative to clinicopathological features and hormone content.

PubMed

Heymann, M F; Hamy, A; Triau, S; Miraillé, E; Toquet, C; Chomarat, H; Cohen, C; Maitre, F; Le Bodie, M F

2004-01-01

Study of prognosis of duodenal endocrine tumors. Retrospective study concerned 55 duodenal endocrine tumors discovered in biopsy or surgical specimens. Follow-up records available for 49 patients indicated that inconspicuous associated clinical manifestations were often found subsequently. Seven patients were classified as Zollinger-Ellison syndrome and seven as multiple endocrine neoplasia (6 MEN I and 1 MEN II). Tumors were small (mean 1.28cm) and located preferentially in the first and second part of the duodenum. Fifty-four were well-differentiated and one poorly differentiated. Immunochemistry revealed 30 G-cell tumors (54.6%), 15 D-cell (27.3%), two plurihormonal (EC cell and G cell), and one GRH-cell, whereas seven could not be classified. Fifteen patients died (five in relation to their disease). Twenty-one had metastases (liver, nodes, lung), eight of whom are still alive. Eighty-eight percent of duodenal endocrine tumors were gastrinomas, small plurifocal tumors and somatostatinomas preferentially located in the ampullar region and diagnosed because of hematemesis or icterus. Size is an important prognostic factor in determining whether surgery is required. The prognosis is better for D- and G-cell tumors than pancreatic endocrine tumors. Duodenal endocrine tumors in multiple endocrine neoplasia have a good prognosis, but can be associated with pancreatic plurihormonal tumors and metastases.

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Correlation of tumor response on computed tomography with pathological necrosis in hepatocellular carcinoma treated by chemoembolization before liver transplantation.

PubMed

Dioguardi Burgio, Marco; Ronot, Maxime; Bruno, Onorina; Francoz, Claire; Paradis, Valérie; Castera, Laurent; Durand, François; Soubrane, Olivier; Vilgrain, Valérie

2016-11-01

The purpose of this article was to compare the results of Response Evaluation Criteria in Solid Tumors (RECIST), modified Response Evaluation Criteria in Solid Tumors (mRECIST), and European Association for the Study of the Liver (EASL) criteria for the evaluation of tumor necrosis in patients treated with transarterial chemoembolization before liver transplantation (LT) for hepatocellular carcinoma. Response to treatment was evaluated on computed tomography scan by 2 independent readers based on RECIST, mRECIST, and EASL criteria, and compared with tumor necrosis assessed by explant pathology. Necrosis was defined as major when >90%. Factors associated with major necrosis were tested by multivariate analysis. Fifty-eight patients (53 males; mean age, 54 years; range, 31-64 years) were included with 88 nodules. Fifty-one (58%) nodules were shown to have major necrosis. Among them readers 1 and 2 identified a complete response (CR) according to RECIST, mRECIST, and EASL criteria in 2 (4%), 47 (92%), and 47 (92%), and 1 (2%), 45 (88%), and 45 (88%) nodules, respectively. However, 12-14 of 59 nodules classified as CR on mRECIST or EASL criteria were found to have intermediate or minor necrosis (overestimation in 20%-24% of the patients). Combining the classification of CR by mRECIST and EASL criteria and complete lipiodol deposition reduced the overestimation to 11%. Among 59 nodules classified with a CR according to mRECIST or EASL, those with complete lipiodol deposition (n = 36, 61%) had a higher rate of necrosis than those with incomplete lipiodol deposition (n = 23, 39%): 95% versus 68% and 95% versus 63% for reader 1 and 2, respectively. In conclusion, CR based on mRECIST/EASL combined with complete lipiodol deposition was better for identification of major tumor necrosis. Even in the presence of CR according to mRECIST/EASL, incomplete lipiodol deposition should be considered indicative of substantial viable tumor remnant. Liver Transplantation 22 1491-1500 2016 AASLD. © 2016 by the American Association for the Study of Liver Diseases.

Determination of BRAF V600E (VE1) protein expression and BRAF gene mutation status in codon 600 in borderline and low-grade ovarian cancers.

PubMed

Sadlecki, Pawel; Walentowicz, Pawel; Bodnar, Magdalena; Marszalek, Andrzej; Grabiec, Marek; Walentowicz-Sadlecka, Malgorzata

2017-05-01

Epithelial ovarian tumors are a group of morphologically and genetically heterogeneous neoplasms. Based on differences in clinical phenotype and genetic background, ovarian neoplasms are classified as low-grade and high-grade tumor. Borderline ovarian tumors represent approximately 10%-20% of all epithelial ovarian masses. Various histological subtypes of ovarian malignancies differ in terms of their risk factor profiles, precursor lesions, clinical course, patterns of spread, molecular genetics, response to conventional chemotherapy, and prognosis. The most frequent genetic aberrations found in low-grade serous ovarian carcinomas and serous borderline tumors, as well as in mucinous cancers, are mutations in BRAF and KRAS genes. The most commonly observed BRAF mutation is substitution of glutamic acid for valine in codon 600 (V600E) in exon 15. The primary aim of this study was to determine whether fully integrated, real-time polymerase chain reaction-based Idylla™ system may be useful in determination of BRAF gene mutation status in codon 600 in patients with borderline ovarian tumors and low-grade ovarian carcinomas. The study included tissue specimens from 42 patients with histopathologically verified ovarian masses, who were operated on at the Department of Obstetrics and Gynecology, Nicolaus Copernicus University Collegium Medicum in Bydgoszcz (Poland). Based on histopathological examination of surgical specimens, 35 lesions were classified as low-grade ovarian carcinomas, and 7 as borderline ovarian tumors. Specimens with expression of BRAF V600E (VE1) protein were tested for mutations in codon 600 of the BRAF gene, using an automated molecular diagnostics platform Idylla™. Cytoplasmic immunoexpression of BRAF V600E (VE1) protein was found in three specimens: serous superficial papilloma, serous papillary cystadenoma of borderline malignancy, and partially proliferative serous cystadenoma. All specimens with the expression of BRAF V600E (VE1) protein were tested positively for BRAF V600E/E2/D mutation. No statistically significant relationship (p > 0.05) was found between the presence of BRAF V600E mutation and the probability of 5-year survival. BRAF mutation testing with a rapid, fully integrated molecular diagnostics system Idylla™ may be also a powerful prognostic tool in subjects with newly diagnosed serous borderline tumors, identifying a subset of patients who are unlikely to progress.

Maternal and Birth Characteristics and Childhood Embryonal Solid Tumors: A Population-Based Report from Brazil.

PubMed

de Paula Silva, Neimar; de Souza Reis, Rejane; Garcia Cunha, Rafael; Pinto Oliveira, Júlio Fernando; Santos, Marceli de Oliveira; Pombo-de-Oliveira, Maria S; de Camargo, Beatriz

2016-01-01

Several maternal and birth characteristics have been reported to be associated with an increased risk of many childhood cancers. Our goal was to evaluate the risk of childhood embryonal solid tumors in relation to pre- and perinatal characteristics. A case-cohort study was performed using two population-based datasets, which were linked through R software. Tumors were classified as central nervous system (CNS) or non-CNS-embryonal (retinoblastoma, neuroblastoma, renal tumors, germ cell tumors, hepatoblastoma and soft tissue sarcoma). Children aged <6 years were selected. Adjustments were made for potential confounders. Odds ratios (OR) with 95% confidence intervals (CI) were computed by unconditional logistic regression analysis using SPSS. Males, high maternal education level, and birth anomalies were independent risk factors. Among children diagnosed older than 24 months of age, cesarean section (CS) was a significant risk factor. Five-minute Apgar ≤8 was an independent risk factor for renal tumors. A decreasing risk with increasing birth order was observed for all tumor types except for retinoblastoma. Among children with neuroblastoma, the risk decreased with increasing birth order (OR = 0.82 (95% CI 0.67-1.01)). Children delivered by CS had a marginally significantly increased OR for all tumors except retinoblastoma. High maternal education level showed a significant increase in the odds for all tumors together, CNS tumors, and neuroblastoma. This evidence suggests that male gender, high maternal education level, and birth anomalies are risk factors for childhood tumors irrespective of the age at diagnosis. Cesarean section, birth order, and 5-minute Apgar score were risk factors for some tumor subtypes.

Rapid multiple immunofluorescent staining for the simultaneous detection of cytokeratin and vimentin in the cytology of canine tumors.

PubMed

Sawa, Mariko; Yabuki, Akira; Kohyama, Moeko; Miyoshi, Noriaki; Yamato, Osamu

2018-06-01

Immunocytochemistry (ICC) is utilized as an advanced technique in veterinary cytology. In tumor diagnosis, cytokeratin and vimentin are markers used to distinguish the origin of tumor cells. Standard enzyme-based ICC has limitations in clinical use; and therefore, more convenient and reliable methods are needed. The purpose of this study was to develop a rapid multiple immunofluorescent (RMIF) detection method for dual cytokeratin and vimentin staining on cytology slides in dogs. Air-dried smear samples from solid tumors and sediments of pleural effusions were prepared from dogs (n = 14) that were admitted to the Veterinary Teaching Hospital, Kagoshima University, Japan. Mouse monoclonal anti-human cytokeratin (AE1/AE3) and rabbit monoclonal anti-human vimentin (SP20) antibodies were used as primary antibodies, followed by staining with Alexa Fluor-conjugated secondary antibodies. Staining using the RMIF method was compared with enzyme-based ICC staining. Rapid multiple immunofluorescent immunostaining was clear and specific in the evaluated smears, whereas the enzyme-based ICC showed nonspecific signals. By using the RMIF staining method, epithelial cells, mesenchymal cells, and mesothelial cells could be classified on a single smear of a pleural effusion. In smears of lymph nodes with epithelial tumor metastases, the RMIF method successfully detected metastatic epithelial tumor cells. The RMIF method might be a useful tool for diagnostic cytology in veterinary medicine. © 2018 American Society for Veterinary Clinical Pathology.

Tumor phenotype and breast density in distinct categories of interval cancer: results of population-based mammography screening in Spain

PubMed Central

2014-01-01

Introduction Interval cancers are tumors arising after a negative screening episode and before the next screening invitation. They can be classified into true interval cancers, false-negatives, minimal-sign cancers, and occult tumors based on mammographic findings in screening and diagnostic mammograms. This study aimed to describe tumor-related characteristics and the association of breast density and tumor phenotype within four interval cancer categories. Methods We included 2,245 invasive tumors (1,297 screening-detected and 948 interval cancers) diagnosed from 2000 to 2009 among 645,764 women aged 45 to 69 who underwent biennial screening in Spain. Interval cancers were classified by a semi-informed retrospective review into true interval cancers (n = 455), false-negatives (n = 224), minimal-sign (n = 166), and occult tumors (n = 103). Breast density was evaluated using Boyd’s scale and was conflated into: <25%; 25 to 50%; 50 to 75%; >75%. Tumor-related information was obtained from cancer registries and clinical records. Tumor phenotype was defined as follows: luminal A: ER+/HER2- or PR+/HER2-; luminal B: ER+/HER2+ or PR+/HER2+; HER2: ER-/PR-/HER2+; triple-negative: ER-/PR-/HER2-. The association of tumor phenotype and breast density was assessed using a multinomial logistic regression model. Adjusted odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. All statistical tests were two-sided. Results Forty-eight percent of interval cancers were true interval cancers and 23.6% false-negatives. True interval cancers were associated with HER2 and triple-negative phenotypes (OR = 1.91 (95% CI:1.22-2.96), OR = 2.07 (95% CI:1.42-3.01), respectively) and extremely dense breasts (>75%) (OR = 1.67 (95% CI:1.08-2.56)). However, among true interval cancers a higher proportion of triple-negative tumors was observed in predominantly fatty breasts (<25%) than in denser breasts (28.7%, 21.4%, 11.3% and 14.3%, respectively; <0.001). False-negatives and occult tumors had similar phenotypic characteristics to screening-detected cancers, extreme breast density being strongly associated with occult tumors (OR = 6.23 (95% CI:2.65-14.66)). Minimal-sign cancers were biologically close to true interval cancers but showed no association with breast density. Conclusions Our findings revealed that both the distribution of tumor phenotype and breast density play specific and independent roles in each category of interval cancer. Further research is needed to understand the biological basis of the overrepresentation of triple-negative phenotype among predominantly fatty breasts in true interval cancers. PMID:24410848

Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies.

PubMed

Vivanti, Refael; Joskowicz, Leo; Lev-Cohain, Naama; Ephrat, Ariel; Sosna, Jacob

2018-03-10

Radiological longitudinal follow-up of tumors in CT scans is essential for disease assessment and liver tumor therapy. Currently, most tumor size measurements follow the RECIST guidelines, which can be off by as much as 50%. True volumetric measurements are more accurate but require manual delineation, which is time-consuming and user-dependent. We present a convolutional neural networks (CNN) based method for robust automatic liver tumor delineation in longitudinal CT studies that uses both global and patient specific CNNs trained on a small database of delineated images. The inputs are the baseline scan and the tumor delineation, a follow-up scan, and a liver tumor global CNN voxel classifier built from radiologist-validated liver tumor delineations. The outputs are the tumor delineations in the follow-up CT scan. The baseline scan tumor delineation serves as a high-quality prior for the tumor characterization in the follow-up scans. It is used to evaluate the global CNN performance on the new case and to reliably predict failures of the global CNN on the follow-up scan. High-scoring cases are segmented with a global CNN; low-scoring cases, which are predicted to be failures of the global CNN, are segmented with a patient-specific CNN built from the baseline scan. Our experimental results on 222 tumors from 31 patients yield an average overlap error of 17% (std = 11.2) and surface distance of 2.1 mm (std = 1.8), far better than stand-alone segmentation. Importantly, the robustness of our method improved from 67% for stand-alone global CNN segmentation to 100%. Unlike other medical imaging deep learning approaches, which require large annotated training datasets, our method exploits the follow-up framework to yield accurate tumor tracking and failure detection and correction with a small training dataset. Graphical abstract Flow diagram of the proposed method. In the offline mode (orange), a global CNN is trained as a voxel classifier to segment liver tumor as in [31]. The online mode (blue) is used for each new case. The input is baseline scan with delineation and the follow-up CT scan to be segmented. The main novelty is the ability to predict failures by trying the system on the baseline scan and the ability to correct them using the patient-specific CNN.

The Emergence of Pan-Cancer CIMP and Its Elusive Interpretation

PubMed Central

Miller, Brendan F.; Sánchez-Vega, Francisco; Elnitski, Laura

2016-01-01

Epigenetic dysregulation is recognized as a hallmark of cancer. In the last 16 years, a CpG island methylator phenotype (CIMP) has been documented in tumors originating from different tissues. However, a looming question in the field is whether or not CIMP is a pan-cancer phenomenon or a tissue-specific event. Here, we give a synopsis of the history of CIMP and describe the pattern of DNA methylation that defines the CIMP phenotype in different cancer types. We highlight new conceptual approaches of classifying tumors based on CIMP in a cancer type-agnostic way that reveal the presence of distinct CIMP tumors in a multitude of The Cancer Genome Atlas (TCGA) datasets, suggesting that this phenotype may transcend tissue-type specificity. Lastly, we show evidence supporting the clinical relevance of CIMP-positive tumors and suggest that a common CIMP etiology may define new mechanistic targets in cancer treatment. PMID:27879658

The Emergence of Pan-Cancer CIMP and Its Elusive Interpretation.

PubMed

Miller, Brendan F; Sánchez-Vega, Francisco; Elnitski, Laura

2016-11-22

Epigenetic dysregulation is recognized as a hallmark of cancer. In the last 16 years, a CpG island methylator phenotype (CIMP) has been documented in tumors originating from different tissues. However, a looming question in the field is whether or not CIMP is a pan-cancer phenomenon or a tissue-specific event. Here, we give a synopsis of the history of CIMP and describe the pattern of DNA methylation that defines the CIMP phenotype in different cancer types. We highlight new conceptual approaches of classifying tumors based on CIMP in a cancer type-agnostic way that reveal the presence of distinct CIMP tumors in a multitude of The Cancer Genome Atlas (TCGA) datasets, suggesting that this phenotype may transcend tissue-type specificity. Lastly, we show evidence supporting the clinical relevance of CIMP-positive tumors and suggest that a common CIMP etiology may define new mechanistic targets in cancer treatment.

Six stroma-based RNA markers diagnostic for prostate cancer in European-Americans validated at the RNA and protein levels in patients in China

PubMed Central

Zhu, Jianguo; Pan, Cong; Jiang, Jun; Deng, Mingsen; Gao, Hengjun; Men, Bozhao; McClelland, Michael; Mercola, Dan; Zhong, Wei-De; Jia, Zhenyu

2015-01-01

We previously analyzed human prostate tissue containing stroma near to tumor and from cancer-negative tissues of volunteers. Over 100 candidate gene expression differences were identified and used to develop a classifier that could detect nearby tumor with an accuracy of 97% (sensitivity = 98% and specificity = 88%) based on 364 independent test cases from primarily European American cases. These stroma-based gene signatures have the potential to identify cancer patients among those with negative biopsies. In this study, we used prostate tissues from Chinese cases to validate six of these markers (CAV1, COL4A2, HSPB1, ITGB3, MAP1A and MCAM). In validation by real-time PCR, four genes (COL4A2, HSPB1, ITGB3, and MAP1A) demonstrated significantly lower expression in tumor-adjacent stroma compared to normal stroma (p value ≤ 0.05). Next, we tested whether these expression differences could be extended to the protein level. In IHC assays, all six selected proteins showed lower expression in tumor-adjacent stroma compared to the normal stroma, of which COL4A2, HSPB1 and ITGB3 showed significant differences (p value ≤ 0.05). These results suggest that biomarkers for diagnosing prostate cancer based on tumor microenvironment may be applicable across multiple racial groups. PMID:26158290

GyneScan

PubMed Central

Acharya, U. Rajendra; Sree, S. Vinitha; Kulshreshtha, Sanjeev; Molinari, Filippo; Koh, Joel En Wei; Saba, Luca; Suri, Jasjit S.

2014-01-01

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. PMID:24325128

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ikushima, K; Arimura, H; Jin, Z

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree ofmore » GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.« less

Canine ocular gliomas: a retrospective study.

PubMed

Naranjo, Carolina; Schobert, Charles; Dubielzig, Richard

2008-01-01

The purpose of this paper is to classify glial tumors observed in the canine retina and optic nerve, describe the histopathological features and provide prognostic information on these neoplasms. The database of the Comparative Ocular Pathology Laboratory of Wisconsin (COPLOW) was searched to collect canine glioma cases. Clinical and follow-up information was gathered from submission forms and an extensive follow-up survey. Slides were reviewed to describe the histopathological characteristics of the neoplasm and classify them. Immunohistochemistry for Glial Fibrillary Acidic Protein (GFAP) was performed in all cases. 18 canine glioma cases were found in the COPLOW database. There was no breed or gender predilection. The mean age was 9.33 +/- 3.67 years. Follow-up information was available for 12 dogs, 8 of which were dead at the time of most recent contact, with a survival time ranging from 0 days (globes received after euthanasia) up to 20 months post-enucleation. In 6 of the 8 dogs that had died during this stud), tumor extended to the margin where the optic nerve had been sectioned. Light microscopic examination of the optic nerve of the affected eyes of four dogs that were still alive during this study revealed no tumor at this surgical margin. One neoplasm was classified as low-grade astrocytoma, 5 tumors as medium-grade astrocytoma, 11 tumors as high grade-astrocytoma and 1 tumor as oligodendroglioma. GFAP was positive in all but two tumors. Retinal and optic nerve gliomas may be considered as differential diagnoses of intraocular and orbital masses. The metastatic potential appears to be low, but ascending invasion into the ventral aspect of the brain is possible.

The Prospective External Validation of International Ovarian Tumor Analysis (IOTA) Simple Rules in the Hands of Level I and II Examiners.

PubMed

Knafel, A; Banas, T; Nocun, A; Wiechec, M; Jach, R; Ludwin, A; Kabzinska-Turek, M; Pietrus, M; Pitynski, K

2016-10-01

Objective: To externally validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR) by examiners with different levels of sonographic experience defined by the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) and to assess the morphological ultrasound features of the adnexal tumors classified as inconclusive based on IOTA SR. Materials and Methods: In the two-year prospective study adnexal tumors were assessed preoperatively with transvaginal ultrasound by examiners with different levels of experience (level 1- IOTA SR1, level 2-IOTA SR2). Additionally, an expert (level 3) evaluated all tumors by subjective assessment (SA). If the rules could not be applied, the tumors were considered inconclusive. The final diagnosis was based on the histopathological result of the removed mass. The diagnostic performance measures for the assessed model were sensitivity, specificity, negative (LR-) and positive(LR+) likelihood ratios, accuracy (ACC) and diagnostic odds ratio (DOR). Results: 226 women with adnexal tumors scheduled for surgery were included in the stutdy. The prevalence of malignancy was 36.3 % in the group of all studied tumors and was 52.5 % in the inconclusive group (n = 40) (p = 0.215). Fewer tumors were classified as inconclusive by level 2 examiners compared to level 1 examiners [20 (8.8 %) vs. 40 (17.7 %); p = 0.008], resulting from the discrepancy in the evaluation of acoustic shadows and the vascularization within the tumor. For level 1 examiners a diagnostic strategy using IOTA SR1 +MA (assuming malignancy when SR inconclusive) achieved a sensitivity, specificity and DOR of 96.3 %, 81.9 %, 13.624 respectively. For level 2 examiners the diagnostic strategy for IOTA SR2 +MA achieved a sensitivity, specificity and DOR of 95.1 %, 89.6 %, 137,143, respectively. Adding SA by an expert (or level 3 examiner) when IOTA SR were not applicable improved the specificity of the test and achieved a DOR of 505.137 (SR1 +SA) and 293.627 (SR2 +SA). The SA by an expert proved to have the best diagnostic performance with a DOR of 5768.857, and a sensitivity and specificity of 97.6 % and 99.3 % respectively. Within the inconclusive group the most common tumors were unilocular-solid (n-13), solid (n-8) and multilocular-solid (n-10) ones. All multilocular tumors were classified as inconclusive because of their size (≥ 100 mm) and were found to be benign by pathology. Most of the inconclusive tumors with cystic content presented low-level (43.75 %) echogenicity, followed by ground-glass (34.37 %), mixed (12.5 %) and anechoic (9.4 %). Conclusion: The study results show excellent diagnostic performance of IOTA Simple Rules followed by subjective expert assessment in inconclusive tumors irrespective of the level of experience, while subjective assessment by an expert still has the highest diagnostic odds ratio. The number of inconclusive cases seems to depend on the level of ultrasound expertise and less experienced examiners have a tendency to overestimate blood flow and a presence of acoustic shadows within the tumors. IOTA SR were not applicable either because no benign or malignant features were found or both were identified. Within inconclusive tumors the majority of cases comprise malignant masses that are either unilocular-solid, solid tumors or small multilocular-solid ones with a diameter of less than 100 mm. © Georg Thieme Verlag KG Stuttgart · New York.

Ensemble stump classifiers and gene expression signatures in lung cancer.

PubMed

Frey, Lewis; Edgerton, Mary; Fisher, Douglas; Levy, Shawn

2007-01-01

Microarray data sets for cancer tumor tissue generally have very few samples, each sample having thousands of probes (i.e., continuous variables). The sparsity of samples makes it difficult for machine learning techniques to discover probes relevant to the classification of tumor tissue. By combining data from different platforms (i.e., data sources), data sparsity is reduced, but this typically requires normalizing data from the different platforms, which can be non-trivial. This paper proposes a variant on the idea of ensemble learners to circumvent the need for normalization. To facilitate comprehension we build ensembles of very simple classifiers known as decision stumps--decision trees of one test each. The Ensemble Stump Classifier (ESC) identifies an mRNA signature having three probes and high accuracy for distinguishing between adenocarcinoma and squamous cell carcinoma of the lung across four data sets. In terms of accuracy, ESC outperforms a decision tree classifier on all four data sets, outperforms ensemble decision trees on three data sets, and simple stump classifiers on two data sets.

Does tumor size have its prognostic role in colorectal cancer? Re-evaluating its value in colorectal adenocarcinoma with different macroscopic growth pattern.

PubMed

Dai, Weixing; Li, Yaqi; Meng, Xianke; Cai, Sanjun; Li, Qingguo; Cai, Guoxiang

2017-09-01

Few previous studies have taken the growth pattern into consideration when analyzing the prognostic value of tumor size in colorectal cancer (CRC). We sought to reveal the prognostic role of tumor size in different macroscopic growth patterns of CRC. Using Cancer Center datasets, we identified 4057 cases with colorectal adenocarcinoma treated with curative resection. Macroscopic growth patterns of tumors were classified into three types: infiltrative, ulcerative and expansive types based on tumor gross appearance. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic factors for overall survival (OS) and disease-free survival (DFS). In whole cohort, tumor size was an independent factor for OS (HR 1.10, 95%CI 1.04-1.16, p < 0.001). Subgroup analysis based on macroscopic growth pattern suggested that tumor size was an independent factor for OS both in the infiltrative (HR 1.37, 95%CI 1.12-1.66, p = 0.002) group and ulcerative group (HR 1.08, 95%CI 1.00-1.16, p = 0.044) and tumor size (HR 1.22, 95%CI 1.06-1.40, p = 0.004) was found as an independent factor for DFS only in infiltrative group. Tumor size is an independent factor for OS and DFS in patients with colorectal adenocarcinoma of infiltrative type, while only for OS in patients of ulcerative type. Copyright © 2017. Published by Elsevier Ltd.

Intrahepatic cholangiocarcinoma.

PubMed

Nakano, Masayuki; Ariizumi, Shun-Ichi; Yamamoto, Masakazu

2017-03-01

Cholangiocarcinoma, also referred to as cholangiocellular carcinoma (particularly in Japan), develops along the biliary tract. The tumor may be intra- or extrahepatic and have different features with specific treatments based on the site of origin. Guidelines for diagnosis and management of cholangiorcarcinoma, such as those proposed by EASL (European Association for the Study of the Liver) 1 and the Mayo Clinic 2 classify the tumor into intrahepatic, perihilar, and distal cholangiocarcinoma. There are three main macroscopic patterns of growth of cholangiocarcinoma: mass-forming, periductal-infiltrating and intraductal. A combination of mass-forming and periductal infiltrating tumors have been shown to have a poor prognosis. 3 Intrahepatic cholangiocarcinoma (ICC) comprises two microscopic subtypes: bile duct and cholangiolar. 4 The bile duct subtype has tall columnar cells that form large glands, whereas cholangiolar tumors are composed of cuboidal and low columnar cells. Patients with cholangiolar tumors, referred to as cholangiolocellular carcinoma, reportedly have a better 5-year survival rate than those with the bile duct type. 4 . Copyright © 2017 Elsevier Inc. All rights reserved.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

PubMed

Waks, Zeev; Weissbrod, Omer; Carmeli, Boaz; Norel, Raquel; Utro, Filippo; Goldschmidt, Yaara

2016-12-23

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

TumorNext-Lynch-MMR: a comprehensive next generation sequencing assay for the detection of germline and somatic mutations in genes associated with mismatch repair deficiency and Lynch syndrome.

PubMed

Gray, Phillip N; Tsai, Pei; Chen, Daniel; Wu, Sitao; Hoo, Jayne; Mu, Wenbo; Li, Bing; Vuong, Huy; Lu, Hsiao-Mei; Batth, Navanjot; Willett, Sara; Uyeda, Lisa; Shah, Swati; Gau, Chia-Ling; Umali, Monalyn; Espenschied, Carin; Janicek, Mike; Brown, Sandra; Margileth, David; Dobrea, Lavinia; Wagman, Lawrence; Rana, Huma; Hall, Michael J; Ross, Theodora; Terdiman, Jonathan; Cullinane, Carey; Ries, Savita; Totten, Ellen; Elliott, Aaron M

2018-04-17

The current algorithm for Lynch syndrome diagnosis is highly complex with multiple steps which can result in an extended time to diagnosis while depleting precious tumor specimens. Here we describe the analytical validation of a custom probe-based NGS tumor panel, TumorNext-Lynch-MMR, which generates a comprehensive genetic profile of both germline and somatic mutations that can accelerate and streamline the time to diagnosis and preserve specimen. TumorNext-Lynch-MMR can detect single nucleotide variants, small insertions and deletions in 39 genes that are frequently mutated in Lynch syndrome and colorectal cancer. Moreover, the panel provides microsatellite instability status and detects loss of heterozygosity in the five Lynch genes; MSH2 , MSH6 , MLH1 , PMS2 and EPCAM . Clinical cases are described that highlight the assays ability to differentiate between somatic and germline mutations, precisely classify variants and resolve discordant cases.

TumorNext-Lynch-MMR: a comprehensive next generation sequencing assay for the detection of germline and somatic mutations in genes associated with mismatch repair deficiency and Lynch syndrome

PubMed Central

Gray, Phillip N.; Tsai, Pei; Chen, Daniel; Wu, Sitao; Hoo, Jayne; Mu, Wenbo; Li, Bing; Vuong, Huy; Lu, Hsiao-Mei; Batth, Navanjot; Willett, Sara; Uyeda, Lisa; Shah, Swati; Gau, Chia-Ling; Umali, Monalyn; Espenschied, Carin; Janicek, Mike; Brown, Sandra; Margileth, David; Dobrea, Lavinia; Wagman, Lawrence; Rana, Huma; Hall, Michael J.; Ross, Theodora; Terdiman, Jonathan; Cullinane, Carey; Ries, Savita; Totten, Ellen; Elliott, Aaron M.

2018-01-01

The current algorithm for Lynch syndrome diagnosis is highly complex with multiple steps which can result in an extended time to diagnosis while depleting precious tumor specimens. Here we describe the analytical validation of a custom probe-based NGS tumor panel, TumorNext-Lynch-MMR, which generates a comprehensive genetic profile of both germline and somatic mutations that can accelerate and streamline the time to diagnosis and preserve specimen. TumorNext-Lynch-MMR can detect single nucleotide variants, small insertions and deletions in 39 genes that are frequently mutated in Lynch syndrome and colorectal cancer. Moreover, the panel provides microsatellite instability status and detects loss of heterozygosity in the five Lynch genes; MSH2, MSH6, MLH1, PMS2 and EPCAM. Clinical cases are described that highlight the assays ability to differentiate between somatic and germline mutations, precisely classify variants and resolve discordant cases. PMID:29755653

Glial tumors with neuronal differentiation.

PubMed

Park, Chul-Kee; Phi, Ji Hoon; Park, Sung-Hye

2015-01-01

Immunohistochemical studies for neuronal differentiation in glial tumors revealed subsets of tumors having both characteristics of glial and neuronal lineages. Glial tumors with neuronal differentiation can be observed with diverse phenotypes and histologic grades. The rosette-forming glioneuronal tumor of the fourth ventricle and papillary glioneuronal tumor have been newly classified as distinct disease entities. There are other candidates for classification, such as the glioneuronal tumor without pseudopapillary architecture, glioneuronal tumor with neuropil-like islands, and the malignant glioneuronal tumor. The clinical significance of these previously unclassified tumors should be confirmed. Copyright © 2015 Elsevier Inc. All rights reserved.

[Soft tissue sarcomas: the role of histology and molecular pathology for differential diagnosis].

PubMed

Poremba, C

2006-01-01

Soft tissue sarcomas include a wide spectrum of different entities. The so-called small round blue cell tumors and spindle cell tumors are difficult to classify based solely on conventional histology. To identify different subtypes of tumors special histochemical and immunohistochemical techniques are necessary. Analysis of protein expression by immunohistochemistry provides a helpful tool to investigate the histogenesis of tumors. A basic spectrum of antibodies should be included to study these tumors: Desmin and myogenin (or MyoD1) for skeletal differentiation; S-100, NSE, CD56, and synaptophysin for neural/neuroendocrine differentiation; CD3, CD20, and CD79 alpha for malignant lymphomas; CD34, sm-actin, and beta-catenin for spindle cell tumors; additional antigens, e. g. Ki-67 and p 53, for estimation of proliferation and tumor suppressor gene malfunctions. Nevertheless, the molecular analysis of soft tissue sarcomas is necessary for demonstration of specific translocations or gene defects to specify and proof a diagnosis. For this purpose, RT-PCR for RNA expression analysis of gene fusion transcripts and multi-color FISH for analysis of chromosomal rearrangements are used. Further investigations, using DNA microrrays may help to subclassify such tumors, with respect to prognosis or prediction of therapeutic response.

Adenomatous tumors of the middle ear and temporal bone: clinical, morphological and tumor biological characteristics of challenging neoplastic lesions.

PubMed

Duderstadt, M; Förster, Christine; Welkoborsky, H-J; Ostertag, H

2012-03-01

Adenomatous tumors of the middle ear and temporal bone are rare tumors. In this retrospective study, we examined nine patients who underwent surgery for an adenomatous tumor of the middle ear, mastoid cavity or eustachian tube. In seven patients, a middle ear adenoma (MEA) and in two patients an aggressive papillary tumor (APT) was diagnosed. We report the clinical, radiologic, morphologic, immunohistochemical and DNA image cytometrical characteristics that can help to correctly classify these tumors. Therapy consisted of surgical excision of the tumors in eight cases. In one elderly patient, only a large biopsy was taken, because this patient suffered from cardial and kidney disorders and was not suitable for an extended surgical approach. This patient received stereotactic radiotherapy. Seven patients underwent planned second look operation. Recurrences occurred in three patients (one with APT, two with MEA), whereas in two of these cases rather a residual tumor due to initial incomplete tumor resection occurred. By image analysis, DNA cytometry MEA were considered benign, whereas the appearance of aneuploid tumor cells in APT confirmed these tumors as low grade malignant lesions. The proliferation rates were equally low in both entities. APT and MEA are tumor entities which can only be correctly classified by a synopsis of histopathology, immunohistochemistry and DNA image cytometry. The recommended therapy is the complete tumor excision. In cases of APT, von Hippel-Lindau syndrome has to be excluded.

Recommendations for dose calculations of lung cancer treatment plans treated with stereotactic ablative body radiotherapy (SABR)

NASA Astrophysics Data System (ADS)

Devpura, S.; Siddiqui, M. S.; Chen, D.; Liu, D.; Li, H.; Kumar, S.; Gordon, J.; Ajlouni, M.; Movsas, B.; Chetty, I. J.

2014-03-01

The purpose of this study was to systematically evaluate dose distributions computed with 5 different dose algorithms for patients with lung cancers treated using stereotactic ablative body radiotherapy (SABR). Treatment plans for 133 lung cancer patients, initially computed with a 1D-pencil beam (equivalent-path-length, EPL-1D) algorithm, were recalculated with 4 other algorithms commissioned for treatment planning, including 3-D pencil-beam (EPL-3D), anisotropic analytical algorithm (AAA), collapsed cone convolution superposition (CCC), and Monte Carlo (MC). The plan prescription dose was 48 Gy in 4 fractions normalized to the 95% isodose line. Tumors were classified according to location: peripheral tumors surrounded by lung (lung-island, N=39), peripheral tumors attached to the rib-cage or chest wall (lung-wall, N=44), and centrally-located tumors (lung-central, N=50). Relative to the EPL-1D algorithm, PTV D95 and mean dose values computed with the other 4 algorithms were lowest for "lung-island" tumors with smallest field sizes (3-5 cm). On the other hand, the smallest differences were noted for lung-central tumors treated with largest field widths (7-10 cm). Amongst all locations, dose distribution differences were most strongly correlated with tumor size for lung-island tumors. For most cases, convolution/superposition and MC algorithms were in good agreement. Mean lung dose (MLD) values computed with the EPL-1D algorithm were highly correlated with that of the other algorithms (correlation coefficient =0.99). The MLD values were found to be ~10% lower for small lung-island tumors with the model-based (conv/superposition and MC) vs. the correction-based (pencil-beam) algorithms with the model-based algorithms predicting greater low dose spread within the lungs. This study suggests that pencil beam algorithms should be avoided for lung SABR planning. For the most challenging cases, small tumors surrounded entirely by lung tissue (lung-island type), a Monte-Carlo-based algorithm may be warranted.

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Baochun; Huang, Cheng; Zhou, Shoujun

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-levelmore » active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.« less

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

PubMed

He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

2016-05-01

A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

Cancer.gov

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

NASA Astrophysics Data System (ADS)

Bowman, Tyler; Chavez, Tanny; Khan, Kamrul; Wu, Jingxian; Chakraborty, Avishek; Rajaram, Narasimhan; Bailey, Keith; El-Shenawee, Magda

2018-02-01

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

Epigenetic silencing of MLH1 in endometrial cancers is associated with larger tumor volume, increased rate of lymph node positivity and reduced recurrence-free survival.

PubMed

Cosgrove, Casey M; Cohn, David E; Hampel, Heather; Frankel, Wendy L; Jones, Dan; McElroy, Joseph P; Suarez, Adrian A; Zhao, Weiqiang; Chen, Wei; Salani, Ritu; Copeland, Larry J; O'Malley, David M; Fowler, Jeffrey M; Yilmaz, Ahmet; Chassen, Alexis S; Pearlman, Rachel; Goodfellow, Paul J; Backes, Floor J

2017-09-01

To determine the relationship between mismatch repair (MMR) classification and clinicopathologic features including tumor volume, and explore outcomes by MMR class in a contemporary cohort. Single institution cohort evaluating MMR classification for endometrial cancers (EC). MMR immunohistochemistry (IHC)±microsatellite instability (MSI) testing and reflex MLH1 methylation testing was performed. Tumors with MMR abnormalities by IHC or MSI and MLH1 methylation were classified as epigenetic MMR deficiency while those without MLH1 methylation were classified as probable MMR mutations. Clinicopathologic characteristics were analyzed. 466 endometrial cancers were classified; 75% as MMR proficient, 20% epigenetic MMR defects, and 5% as probable MMR mutations. Epigenetic MMR defects were associated with advanced stage, higher grade, presence of lymphovascular space invasion, and older age. MMR class was significantly associated with tumor volume, an association not previously reported. The epigenetic MMR defect tumors median volume was 10,220mm 3 compared to 3321mm 3 and 2,846mm 3 , for MMR proficient and probable MMR mutations respectively (P<0.0001). Higher tumor volume was associated with lymph node involvement. Endometrioid EC cases with epigenetic MMR defects had significantly reduced recurrence-free survival (RFS). Among advanced stage (III/IV) endometrioid EC the epigenetic MMR defect group was more likely to recur compared to the MMR proficient group (47.7% vs 3.4%) despite receiving similar adjuvant therapy. In contrast, there was no difference in the number of early stage recurrences for the different MMR classes. MMR testing that includes MLH1 methylation analysis defines a subset of tumors that have worse prognostic features and reduced RFS. Copyright © 2017 Elsevier Inc. All rights reserved.

TCGA's Testicular Germ Cell Tumor Study - TCGA

Cancer.gov

TCGA network researchers identify molecular characteristics that classify testicular germ cell tumor types, including a separate subset of seminomas defined by KIT mutations. This provides a set of candidate biomarkers for risk stratification and potential therapeutic targeting.

Surface-enhanced Raman spectroscopy of saliva proteins for the noninvasive differentiation of benign and malignant breast tumors

PubMed Central

Feng, Shangyuan; Huang, Shaohua; Lin, Duo; Chen, Guannan; Xu, Yuanji; Li, Yongzeng; Huang, Zufang; Pan, Jianji; Chen, Rong; Zeng, Haishan

2015-01-01

The capability of saliva protein analysis, based on membrane protein purification and surface-enhanced Raman spectroscopy (SERS), for detecting benign and malignant breast tumors is presented in this paper. A total of 97 SERS spectra from purified saliva proteins were acquired from samples obtained from three groups: 33 healthy subjects; 33 patients with benign breast tumors; and 31 patients with malignant breast tumors. Subtle but discernible changes in the mean SERS spectra of the three groups were observed. Tentative assignments of the saliva protein SERS spectra demonstrated that benign and malignant breast tumors led to several specific biomolecular changes of the saliva proteins. Multiclass partial least squares–discriminant analysis was utilized to analyze and classify the saliva protein SERS spectra from healthy subjects, benign breast tumor patients, and malignant breast tumor patients, yielding diagnostic sensitivities of 75.75%, 72.73%, and 74.19%, as well as specificities of 93.75%, 81.25%, and 86.36%, respectively. The results from this exploratory work demonstrate that saliva protein SERS analysis combined with partial least squares–discriminant analysis diagnostic algorithms has great potential for the noninvasive and label-free detection of breast cancer. PMID:25609959

Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

NASA Astrophysics Data System (ADS)

Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

2017-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

On the Significance of Fuzzification of the N and M in Cancer Staging

PubMed Central

Yones, Sara A; Moussa, Ahmed S; Hassan, Hesham; Alieldin, Nelly H

2014-01-01

The tumor, node, metastasis (TNM) staging system has been regarded as one of the most widely used staging systems for solid cancer. The “T” is assigned a value according to the primary tumor size, whereas the “N” and “M” are dependent on the number of regional lymph nodes and the presence of distant metastasis, respectively. The current TNM model classifies stages into five crisp classes. This is unrealistic since the drastic modification in treatment that is based on a change in one class may be based on a slight shift around the class boundary. Moreover, the system considers any tumor that has distant metastasis as stage 4, disregarding the metastatic lesion concentration and size. We had handled the problem of T staging in previous studies using fuzzy logic. In this study, we focus on the fuzzification of N and M staging for more accurate and realistic modeling which may, in turn, lead to better treatment and medical decisions. PMID:25089089

Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

PubMed Central

Dinalankara, Wikum; Bravo, Héctor Corrada

2015-01-01

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings. PMID:26078586

Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.

PubMed

Kim, Kyu-Tae; Lee, Hye Won; Lee, Hae-Ock; Kim, Sang Cheol; Seo, Yun Jee; Chung, Woosung; Eum, Hye Hyeon; Nam, Do-Hyun; Kim, Junhyong; Joo, Kyeung Min; Park, Woong-Yang

2015-06-19

Intra-tumoral genetic and functional heterogeneity correlates with cancer clinical prognoses. However, the mechanisms by which intra-tumoral heterogeneity impacts therapeutic outcome remain poorly understood. RNA sequencing (RNA-seq) of single tumor cells can provide comprehensive information about gene expression and single-nucleotide variations in individual tumor cells, which may allow for the translation of heterogeneous tumor cell functional responses into customized anti-cancer treatments. We isolated 34 patient-derived xenograft (PDX) tumor cells from a lung adenocarcinoma patient tumor xenograft. Individual tumor cells were subjected to single cell RNA-seq for gene expression profiling and expressed mutation profiling. Fifty tumor-specific single-nucleotide variations, including KRAS(G12D), were observed to be heterogeneous in individual PDX cells. Semi-supervised clustering, based on KRAS(G12D) mutant expression and a risk score representing expression of 69 lung adenocarcinoma-prognostic genes, classified PDX cells into four groups. PDX cells that survived in vitro anti-cancer drug treatment displayed transcriptome signatures consistent with the group characterized by KRAS(G12D) and low risk score. Single-cell RNA-seq on viable PDX cells identified a candidate tumor cell subgroup associated with anti-cancer drug resistance. Thus, single-cell RNA-seq is a powerful approach for identifying unique tumor cell-specific gene expression profiles which could facilitate the development of optimized clinical anti-cancer strategies.

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

PubMed

Durmaz, Arda; Henderson, Tim A D; Brubaker, Douglas; Bebek, Gurkan

2017-01-01

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks.

PubMed

Lee, Young Han

2018-04-04

The purposes of this study are to evaluate the feasibility of protocol determination with a convolutional neural networks (CNN) classifier based on short-text classification and to evaluate the agreements by comparing protocols determined by CNN with those determined by musculoskeletal radiologists. Following institutional review board approval, the database of a hospital information system (HIS) was queried for lists of MRI examinations, referring department, patient age, and patient gender. These were exported to a local workstation for analyses: 5258 and 1018 consecutive musculoskeletal MRI examinations were used for the training and test datasets, respectively. The subjects for pre-processing were routine or tumor protocols and the contents were word combinations of the referring department, region, contrast media (or not), gender, and age. The CNN Embedded vector classifier was used with Word2Vec Google news vectors. The test set was tested with each classification model and results were output as routine or tumor protocols. The CNN determinations were evaluated using the receiver operating characteristic (ROC) curves. The accuracies were evaluated by a radiologist-confirmed protocol as the reference protocols. The optimal cut-off values for protocol determination between routine protocols and tumor protocols was 0.5067 with a sensitivity of 92.10%, a specificity of 95.76%, and an area under curve (AUC) of 0.977. The overall accuracy was 94.2% for the ConvNet model. All MRI protocols were correct in the pelvic bone, upper arm, wrist, and lower leg MRIs. Deep-learning-based convolutional neural networks were clinically utilized to determine musculoskeletal MRI protocols. CNN-based text learning and applications could be extended to other radiologic tasks besides image interpretations, improving the work performance of the radiologist.

Bayesian pretest probability estimation for primary malignant bone tumors based on the Surveillance, Epidemiology and End Results Program (SEER) database.

PubMed

Benndorf, Matthias; Neubauer, Jakob; Langer, Mathias; Kotter, Elmar

2017-03-01

In the diagnostic process of primary bone tumors, patient age, tumor localization and to a lesser extent sex affect the differential diagnosis. We therefore aim to develop a pretest probability calculator for primary malignant bone tumors based on population data taking these variables into account. We access the SEER (Surveillance, Epidemiology and End Results Program of the National Cancer Institute, 2015 release) database and analyze data of all primary malignant bone tumors diagnosed between 1973 and 2012. We record age at diagnosis, tumor localization according to the International Classification of Diseases (ICD-O-3) and sex. We take relative probability of the single tumor entity as a surrogate parameter for unadjusted pretest probability. We build a probabilistic (naïve Bayes) classifier to calculate pretest probabilities adjusted for age, tumor localization and sex. We analyze data from 12,931 patients (647 chondroblastic osteosarcomas, 3659 chondrosarcomas, 1080 chordomas, 185 dedifferentiated chondrosarcomas, 2006 Ewing's sarcomas, 281 fibroblastic osteosarcomas, 129 fibrosarcomas, 291 fibrous malignant histiocytomas, 289 malignant giant cell tumors, 238 myxoid chondrosarcomas, 3730 osteosarcomas, 252 parosteal osteosarcomas, 144 telangiectatic osteosarcomas). We make our probability calculator accessible at http://ebm-radiology.com/bayesbone/index.html . We provide exhaustive tables for age and localization data. Results from tenfold cross-validation show that in 79.8 % of cases the pretest probability is correctly raised. Our approach employs population data to calculate relative pretest probabilities for primary malignant bone tumors. The calculator is not diagnostic in nature. However, resulting probabilities might serve as an initial evaluation of probabilities of tumors on the differential diagnosis list.

Follow-up segmentation of lung tumors in PET and CT data

NASA Astrophysics Data System (ADS)

Opfer, Roland; Kabus, Sven; Schneider, Torben; Carlsen, Ingwer C.; Renisch, Steffen; Sabczynski, Jörg

2009-02-01

Early response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. We have developed algorithms which allow the user to track both tumor volume and standardized uptake value (SUV) measurements during the therapy from series of CT and PET images, respectively. To prepare for tumor volume estimation we have developed a new technique for a fast, flexible, and intuitive 3D definition of meshes. This initial surface is then automatically adapted by means of a model-based segmentation algorithm and propagated to each follow-up scan. If necessary, manual corrections can be added by the user. To determine SUV measurements a prioritized region growing algorithm is employed. For an improved workflow all algorithms are embedded in a PET/CT therapy monitoring software suite giving the clinician a unified and immediate access to all data sets. Whenever the user clicks on a tumor in a base-line scan, the courses of segmented tumor volumes and SUV measurements are automatically identified and displayed to the user as a graph plot. According to each course, the therapy progress can be classified as complete or partial response or as progressive or stable disease. We have tested our methods with series of PET/CT data from 9 lung cancer patients acquired at Princess Margaret Hospital in Toronto. Each patient underwent three PET/CT scans during a radiation therapy. Our results indicate that a combination of mean metabolic activity in the tumor with the PET-based tumor volume can lead to an earlier response detection than a purely volume based (CT diameter) or purely functional based (e.g. SUV max or SUV mean) response measures. The new software seems applicable for easy, faster, and reproducible quantification to routinely monitor tumor therapy.

KRAS mutation testing in borderline ovarian tumors and low-grade ovarian carcinomas with a rapid, fully integrated molecular diagnostic system.

PubMed

Sadlecki, Pawel; Antosik, Paulina; Grzanka, Dariusz; Grabiec, Marek; Walentowicz-Sadlecka, Malgorzata

2017-10-01

Epithelial ovarian neoplasms are a heterogeneous group of tumors, including various malignancies with distinct clinicopathologic and molecular features. Mutations in BRAF and KRAS genes are the most frequent genetic aberrations found in low-grade serous ovarian carcinomas and serous and mucinous borderline tumors. Implementation of targeted therapeutic strategies requires access to highly specific and highly sensitive diagnostic tests for rapid determination of mutation status. One candidate for such test is fully integrated, real-time polymerase chain reaction-based Idylla™ system for quick and simple detection of KRAS mutations in formaldehyde fixed-paraffin embedded tumor samples. The primary aim of this study was to verify whether fully integrated real-time polymerase chain reaction-based Idylla system may be useful in determination of KRAS mutation status in patients with borderline ovarian tumors and low-grade ovarian carcinomas. The study included tissue specimens from 37 patients with histopathologically verified ovarian masses, operated on at the Department of Obstetrics and Gynecology, Nicolaus Copernicus University Collegium Medicum in Bydgoszcz (Poland) between January 2009 and June 2012. Based on histopathological examination of surgical specimens, 30 lesions were classified as low-grade ovarian carcinomas and 7 as borderline ovarian tumors. Seven patients examined with Idylla KRAS Mutation Test tested positive for KRAS mutation. No statistically significant association was found between the incidence of KRAS mutations and histopathological type of ovarian tumors. Mean survival of the study subjects was 48.51 months (range 3-60 months). Presence of KRAS mutation did not exert a significant effect on the duration of survival in our series. Our findings suggest that Idylla KRAS Mutation Test may be a useful tool for rapid detection of KRAS mutations in ovarian tumor tissue.

Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies.

PubMed

Vivanti, R; Szeskin, A; Lev-Cohain, N; Sosna, J; Joskowicz, L

2017-11-01

Radiological longitudinal follow-up of liver tumors in CT scans is the standard of care for disease progression assessment and for liver tumor therapy. Finding new tumors in the follow-up scan is essential to determine malignancy, to evaluate the total tumor burden, and to determine treatment efficacy. Since new tumors are typically small, they may be missed by examining radiologists. We describe a new method for the automatic detection and segmentation of new tumors in longitudinal liver CT studies and for liver tumors burden quantification. Its inputs are the baseline and follow-up CT scans, the baseline tumors delineation, and a tumor appearance prior model. Its outputs are the new tumors segmentations in the follow-up scan, the tumor burden quantification in both scans, and the tumor burden change. Our method is the first comprehensive method that is explicitly designed to find new liver tumors. It integrates information from the scans, the baseline known tumors delineations, and a tumor appearance prior model in the form of a global convolutional neural network classifier. Unlike other deep learning-based methods, it does not require large tagged training sets. Our experimental results on 246 tumors, of which 97 were new tumors, from 37 longitudinal liver CT studies with radiologist approved ground-truth segmentations, yields a true positive new tumors detection rate of 86 versus 72% with stand-alone detection, and a tumor burden volume overlap error of 16%. New tumors detection and tumor burden volumetry are important for diagnosis and treatment. Our new method enables a simplified radiologist-friendly workflow that is potentially more accurate and reliable than the existing one by automatically and accurately following known tumors and detecting new tumors in the follow-up scan.

Endoscopic Criteria for Evaluating Tumor Stage after Preoperative Chemoradiation Therapy in Locally Advanced Rectal Cancer.

PubMed

Han, Kyung Su; Sohn, Dae Kyung; Kim, Dae Yong; Kim, Byung Chang; Hong, Chang Won; Chang, Hee Jin; Kim, Sun Young; Baek, Ji Yeon; Park, Sung Chan; Kim, Min Ju; Oh, Jae Hwan

2016-04-01

Local excision may be an another option for selected patients with markedly down-staged rectal cancer after preoperative chemoradiation therapy (CRT), and proper evaluation of post-CRT tumor stage (ypT) is essential prior to local excision of these tumors. This study was designed to determine the correlations between endoscopic findings and ypT of rectal cancer. In this study, 481 patients with locally advanced rectal cancer who underwent preoperative CRT followed by surgical resection between 2004 and 2013 at a single institution were evaluated retrospectively. Pathological good response (p-GR) was defined as ypT ≤ 1, and pathological minimal or no response (p-MR) as ypT ≥ 2. The patients were randomly classified according to two groups, a testing (n=193) and a validation (n=288) group. Endoscopic criteria were determined from endoscopic findings and ypT in the testing group and used in classifying patients in the validation group as achieving or not achieving p-GR. Based on findings in the testing group, the endoscopic criteria for p-GR included scarring, telangiectasia, and erythema, whereas criteria for p-MR included nodules, ulcers, strictures, and remnant tumors. In the validation group, the kappa statistic was 0.965 (p < 0.001), and the sensitivity, specificity, positive predictive value, and negative predictive value were 0.362, 0.963, 0.654, and 0.885, respectively. The endoscopic criteria presented are easily applicable for evaluation of ypT after preoperative CRT for rectal cancer. These criteria may be used for selection of patients for local excision of down-staged rectal tumors, because patients with p-MR could be easily ruled out.

Time interval between endometrial biopsy and surgical staging for type I endometrial cancer: association between tumor characteristics and survival outcome.

PubMed

Matsuo, Koji; Opper, Neisha R; Ciccone, Marcia A; Garcia, Jocelyn; Tierney, Katherine E; Baba, Tsukasa; Muderspach, Laila I; Roman, Lynda D

2015-02-01

To examine whether wait time between endometrial biopsy and surgical staging correlates with tumor characteristics and affects survival outcomes in patients with type I endometrial cancer. A retrospective study was conducted to examine patients with grade 1 and 2 endometrioid adenocarcinoma diagnosed by preoperative endometrial biopsy who subsequently underwent hysterectomy-based surgical staging between 2000 and 2013. Patients who received neoadjuvant chemotherapy or hormonal treatment were excluded. Time interval and grade change between endometrial biopsy and hysterectomy were correlated to demographics and survival outcomes. Median wait time was 57 days (range 1-177 days) among 435 patients. Upgrading of the tumor to grade 3 in the hysterectomy specimen was seen in 4.7% of 321 tumors classified as grade 1 and 18.4% of 114 tumors classified as grade 2 on the endometrial biopsy, respectively. Wait time was not associated with grade change (P>.05). Controlling for age, ethnicity, body habitus, medical comorbidities, CA 125 level, and stage, multivariable analysis revealed that wait time was not associated with survival outcomes (5-year overall survival rates, wait time 1-14, 15-42, 43-84, and 85 days or more; 62.5%, 93.6%, 95.2%, and 100%, respectively, P>.05); however, grade 1 to 3 on the hysterectomy specimen remained as an independent prognosticator associated with decreased survival (5-year overall survival rates, grade 1 to 3 compared with grade change 1 to 1, 82.1% compared with 98.5%, P=.01). Among grade 1 preoperative biopsies, grade 1 to 3 was significantly associated with nonobesity (P=.039) and advanced stage (P=.019). Wait time for surgical staging was not associated with decreased survival outcome in patients with type I endometrial cancer.

Histological grading of ovarian clear cell adenocarcinoma: proposal for a simple and reproducible grouping system based on tumor growth architecture.

PubMed

Yamamoto, Sohei; Tsuda, Hitoshi; Shimazaki, Hideyuki; Takano, Masashi; Yoshikawa, Tomoyuki; Kuzuya, Kazuo; Tsuda, Hiroshi; Kurachi, Hirohisa; Kigawa, Junzo; Kikuchi, Yoshihiro; Sugiyama, Toru; Matsubara, Osamu

2012-03-01

In this study, we aimed to develop a histological grading system for ovarian clear cell adenocarcinoma (CCA), based on the tumor growth architectures. Cases were defined as Group A if ≥90% of a tumor examined was composed of well-differentiated tubulocystic and/or papillary architectures; Group C if at least 10% of the tumor was composed of very poorly differentiated histology (i.e. solid masses or individual infiltrating tumor cells with no or little glandular/papillary differentiation); and tumors not corresponding to the first 2 descriptions were defined as Group B. The interobserver reproducibility and prognostic value of the assigned groups were analyzed for 159 CCAs from 5 institutions. The level of agreement in assigning the groups between 2 pathologists was 88.7% (=0.82). After consensus was reached, 46 (29%), 79 (50%), and 34 (21%) tumors were classified in Groups A, B, and C, respectively. In early-stage cases [International Federation of Gynecology and Obstetrics (FIGO) stage I-II], Group A tumors had significantly better outcomes (100% 5-yr survival) than Group B tumors (82% 5-yr survival, P=0.024 by log-rank test) or Group C tumors (56% 5-yr survival, P=0.00054 by log-rank test). Moreover, early-stage Group B tumors had significantly better outcomes than Group C tumors (P<0.001 by a generalized Wilcoxon test). In advanced cases (FIGO stage III-IV), Group A tumors had significantly better outcomes than Group C tumors (52% vs. 16% 5-yr survival, respectively, P=0.043). Group A and C tumors defined with our system were identified to have favorable and unfavorable prognostic factors, respectively, independent of the clinical stage of the disease and presence of residual tumors after the initial surgery. The proposed grouping system could divide patients with CCA into 3 subgroups with distinct prognostic indications, providing a 3-tier histological grading system for ovarian CCA.

GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we propose a novel model to characterize GBM tissue phenotypes using gray level co-occurrence matrices (GLCM) in three anatomical planes. The GLCM encodes local image patches in terms of informative, orientation-invariant texture descriptors, which are used here to sub-classify GBM tissue phenotypes. Experiments demonstrate the model on MRI data of 41 GBM patients, obtained from the cancer genome atlas (TCGA). Intensity-based automatic image registration is applied to align corresponding pairs of fixed T1˗weighted (T1˗WI) post-contrast and fluid attenuated inversion recovery (FLAIR) images. GBM tissue regions are then segmented using the 3D Slicer tool. Texture features are computed from 12 quantifier functions operating on GLCM descriptors, that are generated from MRI intensities within segmented GBM tissue regions. Various classifier models are used to evaluate the effectiveness of texture features for discriminating between GBM phenotypes. Results based on T1-WI scans showed a phenotype classification accuracy of over 88.14%, a sensitivity of 85.37% and a specificity of 96.1%, using the linear discriminant analysis (LDA) classifier. This model has the potential to provide important characteristics of tumors, which can be used for the sub-classification of GBM phenotypes.

The Effect of Molecular Diagnostics on the Treatment of Glioma.

PubMed

Bush, Nancy Ann Oberheim; Butowski, Nicholas

2017-04-01

This review summarizes the use of molecular diagnostics in glioma and its effect on the development of novel therapeutics and management decisions. Genomic and proteomic profiling of brain tumors has provided significant expansion of our understanding of oncogenesis, characterization, and prognostication of brain tumors. Molecular markers such as MGMT, EGFR, IDH, 1p19q, ATRX, TERT, FGFR-TACC, and BRAF are now being used to classify brain tumors as well as influence management decisions. Several of these markers are also being used as therapeutic targets. We review the use of several molecular diagnostics in gliomas and discuss their impact on drug development and clinical trial design. In the future, molecular characterization based on a specific genomic, proteomic as well as transcriptomes for bioformatics analysis will provide clinicians the ability to rationally select drugs with actionable targets for each patient.

Internal and External Triggering Mechanism of "Smart" Nanoparticle-Based DDSs in Targeted Tumor Therapy.

PubMed

Qiana, Xian-Ling; Li, Jun; Wei, Ran; Lin, Hui; Xiong, Li-Xia

2018-05-09

Anticancer chemotherapeutics have a lot of problems via conventional drug delivery systems (DDSs), including non-specificity, burst release, severe side-effects, and damage to normal cells. Owing to its potential to circumventing these problems, nanotechnology has gained increasing attention in targeted tumor therapy. Chemotherapeutic drugs or genes encapsulated in nanoparticles could be used to target therapies to the tumor site in three ways: "passive", "active", and "smart" targeting. To summarize the mechanisms of various internal and external "smart" stimulating factors on the basis of findings from in vivo and in vitro studies. A thorough search of PubMed was conducted in order to identify the majority of trials, studies and novel articles related to the subject. Activated by internal triggering factors (pH, redox, enzyme, hypoxia, etc.) or external triggering factors (temperature, light of different wavelengths, ultrasound, magnetic fields, etc.), "smart" DDSs exhibit targeted delivery to the tumor site, and controlled release of chemotherapeutic drugs or genes. In this review article, we summarize and classify the internal and external triggering mechanism of "smart" nanoparticle-based DDSs in targeted tumor therapy, and the most recent research advances are illustrated for better understanding. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A CpG island methylator phenotype of colorectal cancer that is contiguous with conventional adenomas, but not serrated polyps

PubMed Central

HOKAZONO, KOJI; UEKI, TAKASHI; NAGAYOSHI, KINUKO; NISHIOKA, YASUNOBU; HATAE, TATSUNOBU; KOGA, YUTAKA; HIRAHASHI, MINAKO; ODA, YOSHINAO; TANAKA, MASAO

2014-01-01

A subset of colorectal cancers (CRCs) harbor the CpG island methylator phenotype (CIMP), with concurrent multiple promoter hypermethylation of tumor-related genes. A serrated pathway in which CIMP is developed from serrated polyps is proposed. The present study characterized CIMP and morphologically examined precursor lesions of CIMP. In total, 104 CRCs treated between January 1996 and December 2004 were examined. Aberrant promoter methylation of 15 cancer-related genes was analyzed. CIMP status was classified according to the number of methylated genes and was correlated with the clinicopathological features, including the concomitant polyps in and around the tumors. The frequency of aberrant methylation in each CRC showed a bimodal pattern, and the CRCs were classified as CIMP-high (CIMP-H), CIMP-low (CIMP-L) and CIMP-negative (CIMP-N). CIMP-H was associated with aberrant methylation of MLH1 (P=0.005) and with an improved recurrence-free survival (RFS) rate following curative resection compared with CIMP-L/N (five-year RFS rate, 93.8 vs. 67.1%; P=0.044), while CIMP-N tumors were associated with frequent distant metastases at diagnosis (P=0.023). No concomitant serrated lesions were present in the tumors, whereas conventional adenoma was contiguous with 11 (10.6%) of 104 CRCs, including four CIMP-H CRCs. CIMP-H was classified in CRCs by a novel CIMP marker panel and the presence of concomitant tumors revealed that certain CIMP-H CRCs may have arisen from conventional adenomas. PMID:25289081

A CpG island methylator phenotype of colorectal cancer that is contiguous with conventional adenomas, but not serrated polyps.

PubMed

Hokazono, Koji; Ueki, Takashi; Nagayoshi, Kinuko; Nishioka, Yasunobu; Hatae, Tatsunobu; Koga, Yutaka; Hirahashi, Minako; Oda, Yoshinao; Tanaka, Masao

2014-11-01

A subset of colorectal cancers (CRCs) harbor the CpG island methylator phenotype (CIMP), with concurrent multiple promoter hypermethylation of tumor-related genes. A serrated pathway in which CIMP is developed from serrated polyps is proposed. The present study characterized CIMP and morphologically examined precursor lesions of CIMP. In total, 104 CRCs treated between January 1996 and December 2004 were examined. Aberrant promoter methylation of 15 cancer-related genes was analyzed. CIMP status was classified according to the number of methylated genes and was correlated with the clinicopathological features, including the concomitant polyps in and around the tumors. The frequency of aberrant methylation in each CRC showed a bimodal pattern, and the CRCs were classified as CIMP-high (CIMP-H), CIMP-low (CIMP-L) and CIMP-negative (CIMP-N). CIMP-H was associated with aberrant methylation of MLH1 (P=0.005) and with an improved recurrence-free survival (RFS) rate following curative resection compared with CIMP-L/N (five-year RFS rate, 93.8 vs. 67.1%; P=0.044), while CIMP-N tumors were associated with frequent distant metastases at diagnosis (P=0.023). No concomitant serrated lesions were present in the tumors, whereas conventional adenoma was contiguous with 11 (10.6%) of 104 CRCs, including four CIMP-H CRCs. CIMP-H was classified in CRCs by a novel CIMP marker panel and the presence of concomitant tumors revealed that certain CIMP-H CRCs may have arisen from conventional adenomas.

Mammary gland tumors in captive African hedgehogs.

PubMed

Raymond, J T; Gerner, M

2000-04-01

From December 1995 to July 1999, eight mammary gland tumors were diagnosed in eight adult captive female African hedgehogs (Atelerix albiventris). The tumors presented as single or multiple subcutaneous masses along the cranial or caudal abdomen that varied in size for each hedgehog. Histologically, seven of eight (88%) mammary gland tumors were malignant. Tumors were classified as solid (4 cases), tubular (2 cases), and papillary (2 cases). Seven tumors had infiltrated into the surrounding stroma and three tumors had histologic evidence of neoplastic vascular invasion. Three hedgehogs had concurrent neoplasms. These are believed to be the first reported cases of mammary gland tumors in African hedgehogs.

Lung tumor diagnosis and subtype discovery by gene expression profiling.

PubMed

Wang, Lu-yong; Tu, Zhuowen

2006-01-01

The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.

Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment.

PubMed

Rojas, Veronica; Hirshfield, Kim M; Ganesan, Shridar; Rodriguez-Rodriguez, Lorna

2016-12-15

Epithelial ovarian cancer is a highly heterogeneous disease characterized by multiple histological subtypes. Molecular diversity has been shown to occur within specific histological subtypes of epithelial ovarian cancer, between different tumors of an individual patient, as well as within individual tumors. Recent advances in the molecular characterization of epithelial ovarian cancer tumors have provided the basis for a simplified classification scheme in which these cancers are classified as either type I or type II tumors, and these two categories have implications regarding disease pathogenesis and prognosis. Molecular analyses, primarily based on next-generation sequencing, otherwise known as high-throughput sequencing, are allowing for further refinement of ovarian cancer classification, facilitating the elucidation of the site(s) of precursor lesions of high-grade serous ovarian cancer, and providing insight into the processes of clonal selection and evolution that may be associated with development of chemoresistance. Potential therapeutic targets have been identified from recent molecular profiling studies of these tumors, and the effectiveness and safety of a number of specific targeted therapies have been evaluated or are currently being studied for the treatment of women with this disease.

Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment

PubMed Central

Rojas, Veronica; Hirshfield, Kim M.; Ganesan, Shridar; Rodriguez-Rodriguez, Lorna

2016-01-01

Epithelial ovarian cancer is a highly heterogeneous disease characterized by multiple histological subtypes. Molecular diversity has been shown to occur within specific histological subtypes of epithelial ovarian cancer, between different tumors of an individual patient, as well as within individual tumors. Recent advances in the molecular characterization of epithelial ovarian cancer tumors have provided the basis for a simplified classification scheme in which these cancers are classified as either type I or type II tumors, and these two categories have implications regarding disease pathogenesis and prognosis. Molecular analyses, primarily based on next-generation sequencing, otherwise known as high-throughput sequencing, are allowing for further refinement of ovarian cancer classification, facilitating the elucidation of the site(s) of precursor lesions of high-grade serous ovarian cancer, and providing insight into the processes of clonal selection and evolution that may be associated with development of chemoresistance. Potential therapeutic targets have been identified from recent molecular profiling studies of these tumors, and the effectiveness and safety of a number of specific targeted therapies have been evaluated or are currently being studied for the treatment of women with this disease. PMID:27983698

3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.

PubMed

Jiang, Jun; Wu, Yao; Huang, Meiyan; Yang, Wei; Chen, Wufan; Feng, Qianjin

2013-01-01

Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Cytological and cytogenetical studies on brain tumors. V. Preferential loss of sex chromosomes in human meningiomas.

PubMed

Zankl, H; Seidel, H; Zang, K D

1975-01-01

Twelve out of 88 cytogenetically examined meningiomas of female patients showed, in addition to the typical loss of a chromosome 22, a loss of 1 or more chromosomes of group C. Among them 8 tumors had less than 8% cells with Barr-body-like particles, whereas in one tumor 12% and in 3 others over 20% Barr bodies were found, which, based on control studies, were classified as sex-chromatin negative, partly positive, and positive, respectively. In one case the loss of an X chromosome was verified by Giemsa banding. In 6 out of 24 meningiomas of male origin, the chromosomal morphology and association pattern strongly indicated that besides the loss of a chromosome 22, the Y chromosome was also missing. Moreover, the loss of the male sex chromosome could be ascertained in 4 tumors by the conspicuous absence of Y fluorescence in interphase nuclei and in metaphase plates after fluorescence staining. The findings are discussed in connection with the gonosomal loss in other human tumors and in old age.

Unguioblastoma and unguioblastic fibroma--an expanded spectrum of onychomatricoma.

PubMed

Ko, Christine J; Shi, Linda; Barr, Ronald J; Mölne, Lena; Ternesten-Bratel, Annika; Headington, John T

2004-04-01

Onychomatricoma is a rare tumor that appears to originate from cells of the nail matrix. Three cases of onychomatricoma that met Perrin et al.'s1 histologic criteria of onychomatricoma are described. However, using a single term to classify all three tumors ignores the apparent microscopic differences that exist among them. To demonstrate better the spectrum of so-called onychomatricoma and properly acknowledge the noticeable disparity among our cases, a series of terms is proposed. This terminology is based on the histologic spectrum of epithelial-stromal ratio of stromal cellularity and of extent nuclear pleomorphism. Use of such criteria has a precedent in the classification of follicular and odontogenic fibroepithelial neoplasms. This new nomenclature includes "unguioblastoma" for tumors with a predominant epithelial component and "unguioblastic fibroma" for tumors where a cellular stroma is more prominent and characteristic. The term "atypical unguioblastic fibroma" is used to describe a third rare neoplasm, in which the cellular stroma shows nuclear pleomorphism and atypia with an increase of mitotic activity.

Final validation of the ProMisE molecular classifier for endometrial carcinoma in a large population-based case series.

PubMed

Kommoss, S; McConechy, M K; Kommoss, F; Leung, S; Bunz, A; Magrill, J; Britton, H; Kommoss, F; Grevenkamp, F; Karnezis, A; Yang, W; Lum, A; Krämer, B; Taran, F; Staebler, A; Lax, S; Brucker, S Y; Huntsman, D G; Gilks, C B; McAlpine, J N; Talhouk, A

2018-05-01

We have previously developed and confirmed a pragmatic molecular classifier for endometrial cancers; ProMisE (Proactive Molecular Risk Classifier for Endometrial Cancer). Inspired by the Cancer Genome Atlas, ProMisE identifies four prognostically distinct molecular subtypes and can be applied to diagnostic specimens (biopsy/curettings) enabling earlier informed decision-making. We have strictly adhered to the Institute of Medicine (IOM) guidelines for the development of genomic biomarkers, and herein present the final validation step of a locked-down classifier before clinical application. We assessed a retrospective cohort of women from the Tübingen University Women's Hospital treated for endometrial carcinoma between 2003 and 2013. Primary outcomes of overall, disease-specific, and progression-free survival were evaluated for clinical, pathological, and molecular features. Complete clinical and molecular data were evaluable from 452 women. Patient age ranged from 29 to 93 (median 65) years, and 87.8% cases were endometrioid histotype. Grade distribution included 282 (62.4%) G1, 75 (16.6%) G2, and 95 (21.0%) G3 tumors. 276 (61.1%) patients had stage IA disease, with the remaining stage IB [89 (19.7%)], stage II [26 (5.8%)], and stage III/IV [61 (13.5%)]. ProMisE molecular classification yielded 127 (28.1%) MMR-D, 42 (9.3%) POLE, 55 (12.2%) p53abn, and 228 (50.4%) p53wt. ProMisE was a prognostic marker for progression-free (P = 0.001) and disease-specific (P = 0.03) survival even after adjusting for known risk factors. Concordance between diagnostic and surgical specimens was highly favorable; accuracy 0.91, κ 0.88. We have developed, confirmed, and now validated a pragmatic molecular classification tool (ProMisE) that provides consistent categorization of tumors and identifies four distinct prognostic molecular subtypes. ProMisE can be applied to diagnostic samples and thus could be used to inform surgical procedure(s) and/or need for adjuvant therapy. Based on the IOM guidelines this classifier is now ready for clinical evaluation through prospective clinical trials.

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer

PubMed Central

Peláez-García, Alberto; Yébenes, Laura; Berjón, Alberto; Angulo, Antonia; Zamora, Pilar; Sánchez-Méndez, José Ignacio; Espinosa, Enrique; Redondo, Andrés; Heredia-Soto, Victoria; Mendiola, Marta; Feliú, Jaime

2017-01-01

Purpose To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas. Methods Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score). Results The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)). Conclusions This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test. PMID:28886093

Sensitivity study of voxel-based PET image comparison to image registration algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yip, Stephen, E-mail: syip@lroc.harvard.edu; Chen, Aileen B.; Berbeco, Ross

2014-11-01

Purpose: Accurate deformable registration is essential for voxel-based comparison of sequential positron emission tomography (PET) images for proper adaptation of treatment plan and treatment response assessment. The comparison may be sensitive to the method of deformable registration as the optimal algorithm is unknown. This study investigated the impact of registration algorithm choice on therapy response evaluation. Methods: Sixteen patients with 20 lung tumors underwent a pre- and post-treatment computed tomography (CT) and 4D FDG-PET scans before and after chemoradiotherapy. All CT images were coregistered using a rigid and ten deformable registration algorithms. The resulting transformations were then applied to themore » respective PET images. Moreover, the tumor region defined by a physician on the registered PET images was classified into progressor, stable-disease, and responder subvolumes. Particularly, voxels with standardized uptake value (SUV) decreases >30% were classified as responder, while voxels with SUV increases >30% were progressor. All other voxels were considered stable-disease. The agreement of the subvolumes resulting from difference registration algorithms was assessed by Dice similarity index (DSI). Coefficient of variation (CV) was computed to assess variability of DSI between individual tumors. Root mean square difference (RMS{sub rigid}) of the rigidly registered CT images was used to measure the degree of tumor deformation. RMS{sub rigid} and DSI were correlated by Spearman correlation coefficient (R) to investigate the effect of tumor deformation on DSI. Results: Median DSI{sub rigid} was found to be 72%, 66%, and 80%, for progressor, stable-disease, and responder, respectively. Median DSI{sub deformable} was 63%–84%, 65%–81%, and 82%–89%. Variability of DSI was substantial and similar for both rigid and deformable algorithms with CV > 10% for all subvolumes. Tumor deformation had moderate to significant impact on DSI for progressor subvolume with R{sub rigid} = − 0.60 (p = 0.01) and R{sub deformable} = − 0.46 (p = 0.01–0.20) averaging over all deformable algorithms. For stable-disease subvolumes, the correlations were significant (p < 0.001) for all registration algorithms with R{sub rigid} = − 0.71 and R{sub deformable} = − 0.72. Progressor and stable-disease subvolumes resulting from rigid registration were in excellent agreement (DSI > 70%) for RMS{sub rigid} < 150 HU. However, tumor deformation was observed to have negligible effect on DSI for responder subvolumes with insignificant |R| < 0.26, p > 0.27. Conclusions: This study demonstrated that deformable algorithms cannot be arbitrarily chosen; different deformable algorithms can result in large differences of voxel-based PET image comparison. For low tumor deformation (RMS{sub rigid} < 150 HU), rigid and deformable algorithms yield similar results, suggesting deformable registration is not required for these cases.« less

Local morphologic scale: application to segmenting tumor infiltrating lymphocytes in ovarian cancer TMAs

NASA Astrophysics Data System (ADS)

Janowczyk, Andrew; Chandran, Sharat; Feldman, Michael; Madabhushi, Anant

2011-03-01

In this paper we present the concept and associated methodological framework for a novel locally adaptive scale notion called local morphological scale (LMS). Broadly speaking, the LMS at every spatial location is defined as the set of spatial locations, with associated morphological descriptors, which characterize the local structure or heterogeneity for the location under consideration. More specifically, the LMS is obtained as the union of all pixels in the polygon obtained by linking the final location of trajectories of particles emanating from the location under consideration, where the path traveled by originating particles is a function of the local gradients and heterogeneity that they encounter along the way. As these particles proceed on their trajectory away from the location under consideration, the velocity of each particle (i.e. do the particles stop, slow down, or simply continue around the object) is modeled using a physics based system. At some time point the particle velocity goes to zero (potentially on account of encountering (a) repeated obstructions, (b) an insurmountable image gradient, or (c) timing out) and comes to a halt. By using a Monte-Carlo sampling technique, LMS is efficiently determined through parallelized computations. LMS is different from previous local scale related formulations in that it is (a) not a locally connected sets of pixels satisfying some pre-defined intensity homogeneity criterion (generalized-scale), nor is it (b) constrained by any prior shape criterion (ball-scale, tensor-scale). Shape descriptors quantifying the morphology of the particle paths are used to define a tensor LMS signature associated with every spatial image location. These features include the number of object collisions per particle, average velocity of a particle, and the length of the individual particle paths. These features can be used in conjunction with a supervised classifier to correctly differentiate between two different object classes based on local structural properties. In this paper, we apply LMS to the specific problem of classifying regions of interest in Ovarian Cancer (OCa) histology images as either tumor or stroma. This approach is used to classify lymphocytes as either tumor infiltrating lymphocytes (TILs) or non-TILs; the presence of TILs having been identified as an important prognostic indicator for disease outcome in patients with OCa. We present preliminary results on the tumor/stroma classification of 11,000 randomly selected locations of interest, across 11 images obtained from 6 patient studies. Using a Probabilistic Boosting Tree (PBT), our supervised classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.8341 +/-0.0059 over 5 runs of randomized cross validation. The average LMS computation time at every spatial location for an image patch comprising 2000 pixels with 24 particles at every location was only 18s.

Combined Microsatellite Instability, MLH1 Methylation Analysis, and Immunohistochemistry for Lynch Syndrome Screening in Endometrial Cancers From GOG210: An NRG Oncology and Gynecologic Oncology Group Study.

PubMed

Goodfellow, Paul J; Billingsley, Caroline C; Lankes, Heather A; Ali, Shamshad; Cohn, David E; Broaddus, Russell J; Ramirez, Nilsa; Pritchard, Colin C; Hampel, Heather; Chassen, Alexis S; Simmons, Luke V; Schmidt, Amy P; Gao, Feng; Brinton, Louise A; Backes, Floor; Landrum, Lisa M; Geller, Melissa A; DiSilvestro, Paul A; Pearl, Michael L; Lele, Shashikant B; Powell, Matthew A; Zaino, Richard J; Mutch, David

2015-12-20

The best screening practice for Lynch syndrome (LS) in endometrial cancer (EC) remains unknown. We sought to determine whether tumor microsatellite instability (MSI) typing along with immunohistochemistry (IHC) and MLH1 methylation analysis can help identify women with LS. ECs from GOG210 patients were assessed for MSI, MLH1 methylation, and mismatch repair (MMR) protein expression. Each tumor was classified as having normal MMR, defective MMR associated with MLH1 methylation, or probable MMR mutation (ie, defective MMR but no methylation). Cancer family history and demographic and clinical features were compared for the three groups. Lynch mutation testing was performed for a subset of women. Analysis of 1,002 ECs suggested possible MMR mutation in 11.8% of tumors. The number of patients with a family history suggestive of LS was highest among women whose tumors were classified as probable MMR mutation (P = .001). Lynch mutations were identified in 41% of patient cases classified as probable mutation (21 of 51 tested). One of the MSH6 Lynch mutations was identified in a patient whose tumor had intact MSH6 expression. Age at diagnosis was younger for mutation carriers than noncarriers (54.3 v 62.3 years; P < .01), with five carriers diagnosed at age > 60 years. Combined MSI, methylation, and IHC analysis may prove useful in Lynch screening in EC. Twenty-four percent of mutation carriers presented with ECs at age > 60 years, and one carrier had an MSI-positive tumor with no IHC defect. Restricting Lynch testing to women diagnosed at age < 60 years or to women with IHC defects could result in missing a substantial fraction of genetic disease. © 2015 by American Society of Clinical Oncology.

Combined Microsatellite Instability, MLH1 Methylation Analysis, and Immunohistochemistry for Lynch Syndrome Screening in Endometrial Cancers From GOG210: An NRG Oncology and Gynecologic Oncology Group Study

PubMed Central

Goodfellow, Paul J.; Billingsley, Caroline C.; Lankes, Heather A.; Ali, Shamshad; Cohn, David E.; Broaddus, Russell J.; Ramirez, Nilsa; Pritchard, Colin C.; Hampel, Heather; Chassen, Alexis S.; Simmons, Luke V.; Schmidt, Amy P.; Gao, Feng; Brinton, Louise A.; Backes, Floor; Landrum, Lisa M.; Geller, Melissa A.; DiSilvestro, Paul A.; Pearl, Michael L.; Lele, Shashikant B.; Powell, Matthew A.; Zaino, Richard J.; Mutch, David

2015-01-01

Purpose The best screening practice for Lynch syndrome (LS) in endometrial cancer (EC) remains unknown. We sought to determine whether tumor microsatellite instability (MSI) typing along with immunohistochemistry (IHC) and MLH1 methylation analysis can help identify women with LS. Patients and Methods ECs from GOG210 patients were assessed for MSI, MLH1 methylation, and mismatch repair (MMR) protein expression. Each tumor was classified as having normal MMR, defective MMR associated with MLH1 methylation, or probable MMR mutation (ie, defective MMR but no methylation). Cancer family history and demographic and clinical features were compared for the three groups. Lynch mutation testing was performed for a subset of women. Results Analysis of 1,002 ECs suggested possible MMR mutation in 11.8% of tumors. The number of patients with a family history suggestive of LS was highest among women whose tumors were classified as probable MMR mutation (P = .001). Lynch mutations were identified in 41% of patient cases classified as probable mutation (21 of 51 tested). One of the MSH6 Lynch mutations was identified in a patient whose tumor had intact MSH6 expression. Age at diagnosis was younger for mutation carriers than noncarriers (54.3 v 62.3 years; P < .01), with five carriers diagnosed at age > 60 years. Conclusion Combined MSI, methylation, and IHC analysis may prove useful in Lynch screening in EC. Twenty-four percent of mutation carriers presented with ECs at age > 60 years, and one carrier had an MSI-positive tumor with no IHC defect. Restricting Lynch testing to women diagnosed at age < 60 years or to women with IHC defects could result in missing a substantial fraction of genetic disease. PMID:26552419

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

PubMed

LaViolette, Peter S; Daun, Mitchell K; Paulson, Eric S; Schmainda, Kathleen M

2014-02-01

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

[Analysis of histoprognostic factors for the non metastatic rectal cancer in a west Algerian series of 58 cases].

PubMed

Mesli, Smain Nabil; Regagba, Derbali; Tidjane, Anisse; Benkalfat, Mokhtar; Abi-Ayad, Chakib

2016-01-01

The aim of our study was to analyze histoprognostic factors in patients with non-metastatic rectal cancer operated at the division of surgery "A" in Tlemcen, west Algeria, over a period of six years. Retrospective study of 58 patients with rectal adenocarcinoma. Evaluation criterion was survival. Parameters studied were sex, age, tumor stage, tumor recurrence. The average age was 58 years, 52% of men and 48% of women, with sex-ratio (1,08). Tumor seat was: middle rectum 41.37%, lower rectum 34.48% and upper rectum 24.13%. Concerning TNM clinical staging, patients were classified as stage I (17.65%), stage II (18.61%), stage III (53.44%) and stage IV (7.84%). Median overall survival was 40 months ±2,937 months. Survival based on tumor staging: stage III and IV had a lower 3 years survival rate (19%) versus stage I, II which had a survival rate of 75% (P = 0.000) (95%). Patients with tumor recurrences had a lower 3 years survival rate compared to those who had no tumoral recurrences (30.85% vs 64.30% P = 0.043). In this series, univariate analysis of prognostic factors affecting survival allowed to retain only three factors influencing survival: tumor size, stage and tumor recurrences. In multivariate analysis using Cox's model only one factor was retained: tumor recurrence.

Cellular phone enabled non-invasive tissue classifier.

PubMed

Laufer, Shlomi; Rubinsky, Boris

2009-01-01

Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro.

Cellular Phone Enabled Non-Invasive Tissue Classifier

PubMed Central

Laufer, Shlomi; Rubinsky, Boris

2009-01-01

Cellular phone technology is emerging as an important tool in the effort to provide advanced medical care to the majority of the world population currently without access to such care. In this study, we show that non-invasive electrical measurements and the use of classifier software can be combined with cellular phone technology to produce inexpensive tissue characterization. This concept was demonstrated by the use of a Support Vector Machine (SVM) classifier to distinguish through the cellular phone between heart and kidney tissue via the non-invasive multi-frequency electrical measurements acquired around the tissues. After the measurements were performed at a remote site, the raw data were transmitted through the cellular phone to a central computational site and the classifier was applied to the raw data. The results of the tissue analysis were returned to the remote data measurement site. The classifiers correctly determined the tissue type with a specificity of over 90%. When used for the detection of malignant tumors, classifiers can be designed to produce false positives in order to ensure that no tumors will be missed. This mode of operation has applications in remote non-invasive tissue diagnostics in situ in the body, in combination with medical imaging, as well as in remote diagnostics of biopsy samples in vitro. PMID:19365554

Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics.

PubMed

Sweeney, Carol; Bernard, Philip S; Factor, Rachel E; Kwan, Marilyn L; Habel, Laurel A; Quesenberry, Charles P; Shakespear, Kaylynn; Weltzien, Erin K; Stijleman, Inge J; Davis, Carole A; Ebbert, Mark T W; Castillo, Adrienne; Kushi, Lawrence H; Caan, Bette J

2014-05-01

Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups. We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase PCR was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier. In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% luminal A, 20.5% luminal B, 13.0% HER2-enriched, 9.8% basal-like, and 3.6% normal-like. Among low-risk endocrine-positive tumors (i.e., estrogen and progesterone receptor positive by immunohistochemistry, HER2 negative, and low histologic grade), only 76.5% were categorized as luminal A by PAM50. Continuous-scale luminal A, luminal B, HER2-enriched, and normal-like scores from PAM50 were mutually positively correlated. Basal-like score was inversely correlated with other subtypes. The proportion with non-luminal A subtype decreased with older age at diagnosis, P Trend < 0.0001. Compared with non-Hispanic Whites, African American women were more likely to have basal-like tumors, age-adjusted OR = 4.4 [95% confidence intervals (CI), 2.3-8.4], whereas Asian and Pacific Islander women had reduced odds of basal-like subtype, OR = 0.5 (95% CI, 0.3-0.9). Our data indicate that over 50% of breast cancers treated in the community have luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathologic criteria to higher-risk subtypes. Subtyping in a population-based cohort revealed distinct profiles by age and race. ©2014 AACR.

Alterations of the genes involved in the PI3K and estrogen-receptor pathways influence outcome in human epidermal growth factor receptor 2-positive and hormone receptor-positive breast cancer patients treated with trastuzumab-containing neoadjuvant chemotherapy

PubMed Central

2013-01-01

Background Chemotherapy with trastuzumab is widely used for patients with human epidermal growth factor receptor 2-positive (HER2+) breast cancer, but a significant number of patients with the tumor fail to respond, or relapse. The mechanisms of recurrence and biomarkers that indicate the response to the chemotherapy and outcome are not fully investigated. Methods Genomic alterations were analyzed using single-nucleotide polymorphism arrays in 46 HER2 immunohistochemistry (IHC) 3+ or 2+/fluorescent in situ hybridization (FISH)+ breast cancers that were treated with neoadjuvant chemotherapy with paclitaxel, cyclophosphamid, epirubicin, fluorouracil, and trastuzumab. Patients were classified into two groups based on presence or absence of alterations of 65 cancer-associated genes, and the two groups were further classified into four groups based on genomic HER2 copy numbers or hormone receptor status (HR+/−). Pathological complete response (pCR) and relapse-free survival (RFS) rates were compared between any two of the groups. Results and discussion The pCR rate was 54% in 37 patients, and the RFS rate at 3 years was 72% (95% CI, 0.55-0.89) in 42 patients. The analysis disclosed 8 tumors with nonamplified HER2 and 38 tumors with HER2 amplification, indicating the presence of discordance in tumors diagnosed using current HER2 testing. The 8 patients showed more difficulty in achieving pCR (P=0.019), more frequent relapse (P=0.018), and more frequent alterations of genes in the PI3K pathway (P=0.009) than the patients with HER2 amplification. The alterations of the PI3K and estrogen receptor (ER) pathway genes generally indicated worse RFS rates. The prognostic significance of the alterations was shown in patients with a HR+ tumor, but not in patients with a HR- tumor when divided. Alterations of the PI3K and ER pathway genes found in patients with a HR+ tumor with poor outcome suggested that crosstalk between the two pathways may be involved in resistance to the current chemotherapy with trastuzumab. Conclusions We recommend FISH analysis as a primary HER2 testing because patients with IHC 2+/3+ and nonamplified HER2 had poor outcome. We also support concurrent use of trastuzumab, lapatinib, and cytotoxic and anti-hormonal agents for patients having HR+ tumors with alterations of the PI3K and ER pathway genes. PMID:23679233

Validation of Biomarkers of the Tumor Microenvironment

DTIC Science & Technology

2014-10-01

A ., Yao, H., Rahmatpanah, F., Xia, X. Q., Xu, Q., Pio, R., Turan , T., Koziol, J. A ., Goodison, S., Carpenter, P., Wang-Rodriguez, J., Simoneau, A ...to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE...embedded) prostate cancer biopsy tissue in order validate the accuracy of a stroma-based classifier for diagnosis of prostate cancer using FFPE

Evaluation of the diagnostic power of thermography in breast cancer using Bayesian network classifiers.

PubMed

Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

2013-01-01

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Poor concordance among nine immunohistochemistry classifiers of cell-of-origin for diffuse large B-cell lymphoma: implications for therapeutic strategies.

PubMed

Coutinho, Rita; Clear, Andrew James; Owen, Andrew; Wilson, Andrew; Matthews, Janet; Lee, Abigail; Alvarez, Rute; Gomes da Silva, Maria; Cabeçadas, José; Calaminici, Maria; Gribben, John G

2013-12-15

The opportunity to improve therapeutic choices on the basis of molecular features of the tumor cells is on the horizon in diffuse large B-cell lymphoma (DLBCL). Agents such as bortezomib exhibit selective activity against the poor outcome activated B-cell type (ABC) DLBCL. In order for targeted therapies to succeed in this disease, robust strategies that segregate patients into molecular groups with high reliability are needed. Although molecular studies are considered gold standard, several immunohistochemistry (IHC) algorithms have been published that claim to be able to stratify patients according to their cell-of-origin and to be relevant for patient outcome. However, results are poorly reproducible by independent groups. We investigated nine IHC algorithms for molecular classification in a dataset of DLBCL diagnostic biopsies, incorporating immunostaining for CD10, BCL6, BCL2, MUM1, FOXP1, GCET1, and LMO2. IHC profiles were assessed and agreed among three expert observers. A consensus matrix based on all scoring combinations and the number of subjects for each combination allowed us to assess reliability. The survival impact of individual markers and classifiers was evaluated using Kaplan-Meier curves and the log-rank test. The concordance in patient's classification across the different algorithms was low. Only 4% of the tumors have been classified as germinal center B-cell type (GCB) and 21% as ABC/non-GCB by all methods. None of the algorithms provided prognostic information in the R-CHOP (rituximab plus cyclophosphamide-adriamycin-vincristine-prednisone)-treated cohort. Further work is required to standardize IHC algorithms for DLBCL cell-of-origin classification for these to be considered reliable alternatives to molecular-based methods to be used for clinical decisions. ©2013 AACR.

Genome-wide methylomic and transcriptomic analyses identify subtype-specific epigenetic signatures commonly dysregulated in glioma stem cells and glioblastoma.

PubMed

Pangeni, Rajendra P; Zhang, Zhou; Alvarez, Angel A; Wan, Xuechao; Sastry, Namratha; Lu, Songjian; Shi, Taiping; Huang, Tianzhi; Lei, Charles X; James, C David; Kessler, John A; Brennan, Cameron W; Nakano, Ichiro; Lu, Xinghua; Hu, Bo; Zhang, Wei; Cheng, Shi-Yuan

2018-06-21

Glioma stem cells (GSCs), a subpopulation of tumor cells, contribute to tumor heterogeneity and therapy resistance. Gene expression profiling classified glioblastoma (GBM) and GSCs into four transcriptomically-defined subtypes. Here, we determined the DNA methylation signatures in transcriptomically pre-classified GSC and GBM bulk tumors subtypes. We hypothesized that these DNA methylation signatures correlate with gene expression and are uniquely associated either with only GSCs or only GBM bulk tumors. Additional methylation signatures may be commonly associated with both GSCs and GBM bulk tumors, i.e., common to non-stem-like and stem-like tumor cell populations and correlating with the clinical prognosis of glioma patients. We analyzed Illumina 450K methylation array and expression data from a panel of 23 patient-derived GSCs. We referenced these results with The Cancer Genome Atlas (TCGA) GBM datasets to generate methylomic and transcriptomic signatures for GSCs and GBM bulk tumors of each transcriptomically pre-defined tumor subtype. Survival analyses were carried out for these signature genes using publicly available datasets, including from TCGA. We report that DNA methylation signatures in proneural and mesenchymal tumor subtypes are either unique to GSCs, unique to GBM bulk tumors, or common to both. Further, dysregulated DNA methylation correlates with gene expression and clinical prognoses. Additionally, many previously identified transcriptionally-regulated markers are also dysregulated due to DNA methylation. The subtype-specific DNA methylation signatures described in this study could be useful for refining GBM sub-classification, improving prognostic accuracy, and making therapeutic decisions.

Reconstruction of the cranial base in surgery for jugular foramen tumors.

PubMed

Ramina, Ricardo; Maniglia, Joao J; Paschoal, Jorge R; Fernandes, Yvens B; Neto, Mauricio Coelho; Honorato, Donizeti C

2005-04-01

The surgical removal of a jugular foramen (JF) tumor presents the neurosurgeon with a complex management problem that requires an understanding of the natural history, diagnosis, surgical approaches, and postoperative complications. Cerebrospinal fluid (CSF) leakage is one of the most common complications of this surgery. Different surgical approaches and management concepts to avoid this complication have been described, mainly in the ear, nose, and throat literature. The purpose of this study was to review the results of CSF leakage prevention in a series of 66 patients with JF tumors operated on by a multidisciplinary cranial base team using a new technique for cranial base reconstruction. We retrospectively studied 66 patients who had JF tumors with intracranial extension and who underwent surgical treatment in our institutions from January 1987 to December 2001. Paragangliomas were the most frequent lesions, followed by schwannomas and meningiomas. All patients were operated on using the same multidisciplinary surgical approach (neurosurgeons and ear, nose, and throat surgeons). A surgical strategy for reconstruction of the cranial base using vascularized flaps was carried out. The closure of the surgical wound was performed in three layers. A specially developed myofascial flap (temporalis fascia, cervical fascia, and sternocleidomastoid muscle) associated to the inferior rotation of the posterior portion of the temporalis muscle was used to reconstruct the cranial base with vascularized flaps. In this series of 66 patients, postoperative CSF leakage developed in three cases. These patients presented with very large or recurrent tumors, and the postoperative CSF fistulae were surgically closed. The cosmetic result obtained with this reconstruction was classified as excellent or good in all patients. Our results compare favorably with those reported in the literature. The surgical strategy used for cranial base reconstruction presented in this article has several advantages over the current surgical techniques used in cases of JF tumors.

Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients

NASA Astrophysics Data System (ADS)

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.

2018-02-01

Predicting mutation/loss of alpha-thalassemia/mental retardation syndrome X-linked (ATRX) gene utilizing MR imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare a deep neural network approach based on a residual deep neural network (ResNet) architecture and one based on a classical machine learning approach and evaluate their ability in predicting ATRX mutation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture, pre trained on ImageNet data was the best performing model, achieving an accuracy of 0.91 for the test set (classification of a slice as no tumor, ATRX mutated, or mutated) in terms of f1 score in a test set of 35 cases. The SVM classifier achieved 0.63 for differentiating the Flair signal abnormality regions from the test patients based on their mutation status. We report a method that alleviates the need for extensive preprocessing and acts as a proof of concept that deep neural network architectures can be used to predict molecular biomarkers from routine medical images.

Grading system for blood vessel tumor emboli of invasive ductal carcinoma of the breast.

PubMed

Sugiyama, Michiko; Hasebe, Takahiro; Shimada, Hiroko; Takeuchi, Hideki; Shimizu, Kyoko; Shimizu, Michio; Yasuda, Masanori; Ueda, Shigeto; Shigekawa, Takashi; Osaki, Akihiko; Saeki, Toshiaki

2015-06-01

We previously reported that the number of mitotic and apoptotic figures in tumor cells in blood vessel tumor emboli had the greatest significant power for the accurate prediction of the outcome of patients with invasive ductal carcinoma of the breast. The purpose of the present study was to devise a grading system for blood vessel tumor emboli based on the mitotic and apoptotic figures of tumor cells in blood vessel tumor emboli, enabling accurate prediction of the outcome of patients with invasive ductal carcinoma of the breast. We classified 263 invasive ductal carcinomas into the following 3 grades according to the numbers of mitotic and apoptotic figures in tumor cells located in blood vessels within 1 high-power field: grade 0, no blood vessel invasion; grade 1, absence of mitotic figures and presence of any number of apoptotic figures, or 1 mitotic figure and 0 to 2 apoptotic figures; and grade 2, 1 mitotic figure and 3 or more apoptotic figures, or 2 or more mitotic figures and 1 or more apoptotic figures. Multivariate analyses with well-known prognostic factors demonstrated that grade 2 blood vessel tumor emboli significantly increased the hazard ratios for tumor recurrence independent of the nodal status, pathological TNM stage, hormone receptor status, or HER2 status. The presently reported grading system for blood vessel tumor emboli is the strongest histologic factor for accurate prediction of the outcome of patients with invasive ductal carcinoma of the breast. Copyright © 2015 Elsevier Inc. All rights reserved.

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.

PubMed

Shiradkar, Rakesh; Podder, Tarun K; Algohary, Ahmad; Viswanath, Satish; Ellis, Rodney J; Madabhushi, Anant

2016-11-10

Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text]) which is the current clinical standard, radiomics based focal ([Formula: see text]), and whole gland with a radiomics based focal boost ([Formula: see text]). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text]. A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text]. A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.

Gliomatosis cerebri: clinicopathologic study of 33 cases and comparison of mass forming and diffuse types.

PubMed

Park, S; Suh, Y-L; Nam, D-H; Kim, S T

2009-01-01

Gliomatosis cerebri (GC) is defined as a diffuse neoplastic glial cell infiltration of the brain with the preservation of anatomical architecture and the sparing of neurons and can be classified into Type 1 (diffuse) and Type 2 (mass forming) GCs macroscopically. There is little information on subtypes of GC. The aim of this study was to evaluate the clinicopathologic findings of GCs and to compare the clinicopathologic findings between Type 1 and Type 2 GCs. A total of 33 cases of GC were obtained from pathology file of Samsung Medical Center. The diagnosis was based on magnetic resonance imaging findings and histological confirmation for all patients. Fifteen cases were classified into Type 1 and 18 were Type 2 based on the MR images. Clinical information included patients' age, sex, tumor extent, treatment modality and survival. Pathologic features included the amount of rod cells and cytologic anaplasia such as multinucleated tumor giant cells, endothelial cell proliferation, or mitosis. Immunohistochemical study was performed for GFAP, O1, Gal-C, Ki-67, and p53. Clinicopathologic comparison between subtypes and statistical analysis were performed. Median age at diagnosis was older (56 years) in Type 1 than in Type 2 (44 years). Male to female ratio was about 1.54:1. Mean survival time was shorter (21 months) in Type 2 than in Type 1 GCs (24 months) (p = 0.0447). Histologically, 33 cases of GC were classified into two histologic grades (low and high grade) by cytologic anaplasia. High-grade GC was more common in Type 2 than Type 1 (p = 0.027). Immunohistochemical results demonstrated that the infiltrating tumor cells were undifferentiated cells with astrocytic or oligodendroglial differentiation. Ki-67 labeling index was correlated with subtypes (p = 0.0096). Pathologic features were not correlated with survival. Type 1 and 2 GCs are somewhat different in clinical presentation and pathologic features. The age group, survival time, histologic grade, and Ki-67 labeling index were significantly correlated with subtypes ofGCs. Type 2 GC was correlated with poor survival but histologic grade was not.

A Minimum Spanning Forest Based Method for Noninvasive Cancer Detection with Hyperspectral Imaging

PubMed Central

Pike, Robert; Lu, Guolan; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-01-01

Goal The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine (SVM) classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. PMID:26285052

Diagnostic accuracy of T stage of gastric cancer from the view point of application of laparoscopic proximal gastrectomy.

PubMed

Kouzu, Keita; Tsujimoto, Hironori; Hiraki, Shuichi; Nomura, Shinsuke; Yamamoto, Junji; Ueno, Hideki

2018-06-01

The preoperative diagnosis of T stage is important in selecting limited treatments, such as laparoscopic proximal gastrectomy (LPG), which lacks the ability to palpate the tumor. Therefore, the present study examined the accuracy of preoperative diagnosis of the depth of tumor invasion in early gastric cancer from the view point of the indication for LPG. A total of 193 patients with cT1 gastric cancer underwent LPG with gastrointestinal endoscopic examinations and a series of upper gastrointestinal radiographs. The patients with pT1 were classified into the correctly diagnosed group (163 patients, 84.5%), and those with pT2 or deeper were classified into the underestimated group (30 patients, 15.5%). Factors that were associated with underestimation of tumor depth were analyzed. Tumor size in the underestimated group was significantly larger; the lesions were more frequently located in the upper third of the stomach and were more histologically diffuse, scirrhous, with infiltrative growth, and more frequent lymphatic and venous invasion. For upper third lesions, in univariate analysis, histology (diffuse type) was associated with underestimation of tumor depth. Multivariate analysis found that tumor size (≥20 mm) and histology (diffuse type) were independently associated with underestimation of tumor depth. gastric cancer in the upper third of the stomach with diffuse type histology and >20 mm needs particular attention when considering the application of LPG.

Diagnostic accuracy of T stage of gastric cancer from the view point of application of laparoscopic proximal gastrectomy

PubMed Central

Kouzu, Keita; Tsujimoto, Hironori; Hiraki, Shuichi; Nomura, Shinsuke; Yamamoto, Junji; Ueno, Hideki

2018-01-01

The preoperative diagnosis of T stage is important in selecting limited treatments, such as laparoscopic proximal gastrectomy (LPG), which lacks the ability to palpate the tumor. Therefore, the present study examined the accuracy of preoperative diagnosis of the depth of tumor invasion in early gastric cancer from the view point of the indication for LPG. A total of 193 patients with cT1 gastric cancer underwent LPG with gastrointestinal endoscopic examinations and a series of upper gastrointestinal radiographs. The patients with pT1 were classified into the correctly diagnosed group (163 patients, 84.5%), and those with pT2 or deeper were classified into the underestimated group (30 patients, 15.5%). Factors that were associated with underestimation of tumor depth were analyzed. Tumor size in the underestimated group was significantly larger; the lesions were more frequently located in the upper third of the stomach and were more histologically diffuse, scirrhous, with infiltrative growth, and more frequent lymphatic and venous invasion. For upper third lesions, in univariate analysis, histology (diffuse type) was associated with underestimation of tumor depth. Multivariate analysis found that tumor size (≥20 mm) and histology (diffuse type) were independently associated with underestimation of tumor depth. gastric cancer in the upper third of the stomach with diffuse type histology and >20 mm needs particular attention when considering the application of LPG. PMID:29844908

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

PubMed

Ferreira Junior, José Raniery; Koenigkam-Santos, Marcel; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

2018-06-01

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. Copyright © 2018 Elsevier B.V. All rights reserved.

Applying a radiomics approach to predict prognosis of lung cancer patients

NASA Astrophysics Data System (ADS)

Emaminejad, Nastaran; Yan, Shiju; Wang, Yunzhi; Qian, Wei; Guan, Yubao; Zheng, Bin

2016-03-01

Radiomics is an emerging technology to decode tumor phenotype based on quantitative analysis of image features computed from radiographic images. In this study, we applied Radiomics concept to investigate the association among the CT image features of lung tumors, which are either quantitatively computed or subjectively rated by radiologists, and two genomic biomarkers namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting disease-free survival (DFS) of lung cancer patients after surgery. An image dataset involving 94 patients was used. Among them, 20 had cancer recurrence within 3 years, while 74 patients remained DFS. After tumor segmentation, 35 image features were computed from CT images. Using the Weka data mining software package, we selected 10 non-redundant image features. Applying a SMOTE algorithm to generate synthetic data to balance case numbers in two DFS ("yes" and "no") groups and a leave-one-case-out training/testing method, we optimized and compared a number of machine learning classifiers using (1) quantitative image (QI) features, (2) subjective rated (SR) features, and (3) genomic biomarkers (GB). Data analyses showed relatively lower correlation among the QI, SR and GB prediction results (with Pearson correlation coefficients < 0.5 including between ERCC1 and RRM1 biomarkers). By using area under ROC curve as an assessment index, the QI, SR and GB based classifiers yielded AUC = 0.89+/-0.04, 0.73+/-0.06 and 0.76+/-0.07, respectively, which showed that all three types of features had prediction power (AUC>0.5). Among them, using QI yielded the highest performance.

Adenocarcinoma arising in warthin tumor of the parotid gland.

PubMed

Sayar, Hamide; Öztarakçi, Hüseyin; Sayar, Çağdaş; Ağirbaş, Şule

2012-01-01

Warthin tumor is a well-defined benign salivary gland neoplasm consisting of both epithelial and lymphoid components. The tumor is the second most common benign tumor next to pleomorphic adenoma. We present a case of adenocarcinoma, not otherwise classified, arising in unilateral Warthin tumor of the parotid gland in a 63-year-old male patient. Carcinomas arising in or from the epithelial component of a preexisting parotid Warthin tumor are rare and differential diagnosis of metastasis from an adenocarcinoma in Warthin tumor is important. The patient underwent a complete and thorough work-up, and no other primary malignant lesion was found. No other primary malignant lesion had manifested at the last one year follow-up period.

Association Between Molecular Subtypes of Colorectal Cancer and Patient Survival

PubMed Central

Phipps, Amanda I.; Limburg, Paul J.; Baron, John A.; Burnett-Hartman, Andrea N.; Weisenberger, Daniel J.; Laird, Peter W.; Sinicrope, Frank A.; Rosty, Christophe; Buchanan, Daniel D.; Potter, John D.; Newcomb, Polly A.

2014-01-01

Background and Aims. Colorectal cancer (CRC) is a heterogeneous disease that can develop via several pathways. Different CRC subtypes, identified based on tumor markers, have been proposed to reflect these pathways. We evaluated the significance of these previously proposed classifications to survival. Methods. Participants in the population-based Seattle Colon Cancer Family Registry were diagnosed with invasive CRC from 1998 through 2007 in western Washington State (n=2706), and followed for survival through 2012. Tumor samples were collected from 2050 participants and classified into 5 subtypes based on combinations of tumor markers: type 1 (microsatellite instability [MSI] high, CpG island methylator phenotype [CIMP] positive, positive for BRAF mutation, negative for KRAS mutation); type 2 (microsatellite stable [MSS] or MSI-low, CIMP-positive, positive for BRAF mutation, negative for KRAS mutation); type 3 (MSS or MSI-low, non-CIMP, negative for BRAF mutation, positive for KRAS mutation); type 4 (MSS or MSI-low, non-CIMP, negative for mutations in BRAF and KRAS); and type 5 (MSI-high, non-CIMP, negative for mutations in BRAF and KRAS). Multiple imputation was used to impute tumor markers for those missing data on 1-3 markers. We used Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations of subtypes with disease-specific and overall mortality, adjusting for age, sex, body mass, diagnosis year, and smoking history. Results. Compared to participants with type 4 tumors (the most predominant), participants with type 2 tumors had the highest disease-specific mortality (HR=2.20, 95% CI: 1.47-3.31); subjects with type 3 tumors also had higher disease-specific mortality (HR=1.32, 95% CI: 1.07-1.63). Subjects with type 5 tumors had the lowest disease-specific mortality (HR=0.30, 95% CI: 0.14-0.66). Associations with overall mortality were similar to those with disease-specific mortality. Conclusions. Based on a large, population-based study, CRC subtypes, defined by proposed etiologic pathways, are associated with marked differences in survival. These findings indicate the clinical importance of studies into the molecular heterogeneity of CRC. PMID:25280443

Association between molecular subtypes of colorectal cancer and patient survival.

PubMed

Phipps, Amanda I; Limburg, Paul J; Baron, John A; Burnett-Hartman, Andrea N; Weisenberger, Daniel J; Laird, Peter W; Sinicrope, Frank A; Rosty, Christophe; Buchanan, Daniel D; Potter, John D; Newcomb, Polly A

2015-01-01

Colorectal cancer (CRC) is a heterogeneous disease that can develop via several pathways. Different CRC subtypes, identified based on tumor markers, have been proposed to reflect these pathways. We evaluated the significance of these previously proposed classifications to survival. Participants in the population-based Seattle Colon Cancer Family Registry were diagnosed with invasive CRC from 1998 through 2007 in western Washington State (N = 2706), and followed for survival through 2012. Tumor samples were collected from 2050 participants and classified into 5 subtypes based on combinations of tumor markers: type 1 (microsatellite instability [MSI]-high, CpG island methylator phenotype [CIMP] -positive, positive for BRAF mutation, negative for KRAS mutation); type 2 (microsatellite stable [MSS] or MSI-low, CIMP-positive, positive for BRAF mutation, negative for KRAS mutation); type 3 (MSS or MSI low, non-CIMP, negative for BRAF mutation, positive for KRAS mutation); type 4 (MSS or MSI-low, non-CIMP, negative for mutations in BRAF and KRAS); and type 5 (MSI-high, non-CIMP, negative for mutations in BRAF and KRAS). Multiple imputation was used to impute tumor markers for those missing data on 1-3 markers. We used Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations of subtypes with disease-specific and overall mortality, adjusting for age, sex, body mass, diagnosis year, and smoking history. Compared with participants with type 4 tumors (the most predominant), participants with type 2 tumors had the highest disease-specific mortality (HR = 2.20, 95% CI: 1.47-3.31); subjects with type 3 tumors also had higher disease-specific mortality (HR = 1.32, 95% CI: 1.07-1.63). Subjects with type 5 tumors had the lowest disease-specific mortality (HR = 0.30, 95% CI: 0.14-0.66). Associations with overall mortality were similar to those with disease-specific mortality. Based on a large, population-based study, CRC subtypes, defined by proposed etiologic pathways, are associated with marked differences in survival. These findings indicate the clinical importance of studies into the molecular heterogeneity of CRC. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

TumorNext: A comprehensive tumor profiling assay that incorporates high resolution copy number analysis and germline status to improve testing accuracy

PubMed Central

Gray, Phillip N.; Vuong, Huy; Tsai, Pei; Lu, Hsaio-Mei; Mu, Wenbo; Hsuan, Vickie; Hoo, Jayne; Shah, Swati; Uyeda, Lisa; Fox, Susanne; Patel, Harshil; Janicek, Mike; Brown, Sandra; Dobrea, Lavinia; Wagman, Lawrence; Plimack, Elizabeth; Mehra, Ranee; Golemis, Erica A.; Bilusic, Marijo; Zibelman, Matthew; Elliott, Aaron

2016-01-01

The development of targeted therapies for both germline and somatic DNA mutations has increased the need for molecular profiling assays to determine the mutational status of specific genes. Moreover, the potential of off-label prescription of targeted therapies favors classifying tumors based on DNA alterations rather than traditional tissue pathology. Here we describe the analytical validation of a custom probe-based NGS tumor panel, TumorNext, which can detect single nucleotide variants, small insertions and deletions in 142 genes that are frequently mutated in somatic and/or germline cancers. TumorNext also detects gene fusions and structural variants, such as tandem duplications and inversions, in 15 frequently disrupted oncogenes and tumor suppressors. The assay uses a matched control and custom bioinformatics pipeline to differentiate between somatic and germline mutations, allowing precise variant classification. We tested 170 previously characterized samples, of which > 95% were formalin-fixed paraffin embedded tissue from 8 different cancer types, and highlight examples where lack of germline status may have led to the inappropriate prescription of therapy. We also describe the validation of the Affymetrix OncoScan platform, an array technology for high resolution copy number variant detection for use in parallel with the NGS panel that can detect single copy amplifications and hemizygous deletions. We analyzed 80 previously characterized formalin-fixed paraffin-embedded specimens and provide examples of hemizygous deletion detection in samples with known pathogenic germline mutations. Thus, the TumorNext combined approach of NGS and OncoScan potentially allows for the identification of the “second hit” in hereditary cancer patients. PMID:27626691

Management of gastric and duodenal neuroendocrine tumors

PubMed Central

Sato, Yuichi; Hashimoto, Satoru; Mizuno, Ken-ichi; Takeuchi, Manabu; Terai, Shuji

2016-01-01

Gastrointestinal neuroendocrine tumors (GI-NETs) are rare neoplasms, like all NETs. However, the incidence of GI-NETS has been increasing in recent years. Gastric NETs (G-NETs) and duodenal NETs (D-NETs) are the common types of upper GI-NETs based on tumor location. G-NETs are classified into three distinct subgroups: type I, II, and III. Type I G-NETs, which are the most common subtype (70%-80% of all G-NETs), are associated with chronic atrophic gastritis, including autoimmune gastritis and Helicobacter pylori associated atrophic gastritis. Type II G-NETs (5%-6%) are associated with multiple endocrine neoplasia type 1 and Zollinger-Ellison syndrome (MEN1-ZES). Both type I and II G-NETs are related to hypergastrinemia, are small in size, occur in multiple numbers, and are generally benign. In contrast, type III G-NETs (10%-15%) are not associated with hypergastrinemia, are large-sized single tumors, and are usually malignant. Therefore, surgical resection and chemotherapy are generally necessary for type III G-NETs, while endoscopic resection and follow-up, which are acceptable for the treatment of most type I and II G-NETs, are only acceptable for small and well differentiated type III G-NETs. D-NETs include gastrinomas (50%-60%), somatostatin-producing tumors (15%), nonfunctional serotonin-containing tumors (20%), poorly differentiated neuroendocrine carcinomas (< 3%), and gangliocytic paragangliomas (< 2%). Most D-NETs are located in the first or second part of the duodenum, with 20% occurring in the periampullary region. Therapy for D-NETs is based on tumor size, location, histological grade, stage, and tumor type. While endoscopic resection may be considered for small nonfunctional D-NETs (G1) located in the higher papilla region, surgical resection is necessary for most other D-NETs. However, there is no consensus regarding the ideal treatment of D-NETs. PMID:27570419

HGNET-BCOR Tumors of the Cerebellum: Clinicopathologic and Molecular Characterization of 3 Cases.

PubMed

Appay, Romain; Macagno, Nicolas; Padovani, Laetitia; Korshunov, Andrey; Kool, Marcel; André, Nicolas; Scavarda, Didier; Pietsch, Torsten; Figarella-Branger, Dominique

2017-09-01

The central nervous system (CNS) high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR) is a recently described molecular entity. We report 3 new CNS HGNET-BCOR cases sharing common clinical presentation and pathologic features. The 3 cases concerned children aged 3 to 7 years who presented with a voluminous mass of the cerebellum. Pathologic features included proliferation of uniform spindle to ovoid cells with fine chromatin associated with a rich arborizing capillary network. Methylation profiling classified these cases as CNS HGNET-BCOR tumors. Polymerase chain reaction analysis confirmed the presence of internal tandem duplications in the C-terminus of BCOR (BCOR-ITD), a characteristic of these tumors, in all 3 cases. Immunohistochemistry showed a strong nuclear BCOR expression. In 2 cases, local recurrence occurred within 6 months. The third case, a patient who received a craniospinal irradiation after total surgical removal followed by a metronomics maintenance with irinotecan, temozolomide, and itraconazole, is still free of disease 14 months after diagnosis. In summary, CNS HGNET-BCOR represents a rare tumor occurring in young patients with dismal prognosis. BCOR nuclear immunoreactivity is highly suggestive of a BCOR-ITD. Whether CNS HGNET-BCOR should be classified among the category of "embryonal tumors" or within the category of "mesenchymal, nonmeningothelial tumors" remains to be clarified. Because CNS HGNET-BCOR share pathologic features and characteristic BCOR-ITD with clear cell sarcoma of the kidney, these tumors may represent local variants of the same entity.

External-beam Co-60 radiotherapy for canine nasal tumors: a comparison of survival by treatment protocol.

PubMed

Yoon, J H; Feeney, D A; Jessen, C R; Walter, P A

2008-02-01

A retrospective analysis of survival times in dogs with intranasal tumors was performed comparing those treated using hypofractionated or full course Co-60 radiotherapy protocols alone or with surgical adjuvant therapy and those receiving no radiation treatment. One hundred thirty-nine dogs presented to the University of Minnesota Veterinary Medical Center for treatment of histologically-confirmed nasal neoplasia between July 1983 and October 2001 met the criteria for review. Statistically analyzed parameters included age at diagnosis, tumor histologic classification, fractionation schedule (number of treatments, and number of treatment days/week) (classified as hypofractionated if 2 or less treatments/week); calculated minimum tumor dose/fraction; calculated total minimum tumor dose (classified as hypofractionated if less than 37 Gy in six or fewer fractions); number of radiotherapy portals, a treatment gap of more than 7 days in a full course (3-5 treatments/week, 3-3.5 week treatment time) radiotherapy protocol, the influence of eye shields on survival following single portal DV fields, the survey radiographic extent of the disease, and the presence or absence of cytoreductive surgery. There was a significant relationship only between protocols using 3 or more treatments/week and at least 37 Gy cumulative minimum tumor dose and survival. However, there was no significant relationship between either total minimum tumor dose or dose/fraction and survival and there were no significant relationships between survival and any of the other variables analyzed including tumor histologic type.

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors.

PubMed

Bisarro Dos Reis, Mariana; Barros-Filho, Mateus Camargo; Marchi, Fábio Albuquerque; Beltrami, Caroline Moraes; Kuasne, Hellen; Pinto, Clóvis Antônio Lopes; Ambatipudi, Srikant; Herceg, Zdenko; Kowalski, Luiz Paulo; Rogatto, Silvia Regina

2017-11-01

Even though the majority of well-differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis. To identify a prognostic epigenetic signature in thyroid cancer. Genome-wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneoplastic thyroid tissues (NTs), 17 benign thyroid lesions (BTLs), and 74 thyroid carcinomas (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated, and 3 anaplastic). A prognostic classifier for WDTC was developed via diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database. A specific epigenetic profile was detected according to each histological subtype. BTLs and follicular carcinomas showed a greater number of methylated CpG in comparison with NTs, whereas hypomethylation was predominant in papillary and undifferentiated carcinomas. A prognostic classifier based on 21 DNA methylation probes was able to predict poor outcome in patients with WDTC (sensitivity 63%, specificity 92% for internal data; sensitivity 64%, specificity 88% for TCGA data). High-risk score based on the classifier was considered an independent factor of poor outcome (Cox regression, P < 0.001). The methylation profile of thyroid lesions exhibited a specific signature according to the histological subtype. A meaningful algorithm composed of 21 probes was capable of predicting the recurrence in WDTC. Copyright © 2017 Endocrine Society

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.

PubMed

Ehteshami Bejnordi, Babak; Mullooly, Maeve; Pfeiffer, Ruth M; Fan, Shaoqi; Vacek, Pamela M; Weaver, Donald L; Herschorn, Sally; Brinton, Louise A; van Ginneken, Bram; Karssemeijer, Nico; Beck, Andrew H; Gierach, Gretchen L; van der Laak, Jeroen A W M; Sherman, Mark E

2018-06-13

The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40-65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project. Using deep convolutional neural networks, we trained an algorithm to discriminate between stroma surrounding invasive cancer and stroma from benign biopsies. In test sets (928 whole-slide images from 330 patients), this algorithm could distinguish biopsies diagnosed as invasive cancer from benign biopsies solely based on the stromal characteristics (area under the receiver operator characteristics curve = 0.962). Furthermore, without being trained specifically using ductal carcinoma in situ as an outcome, the algorithm detected tumor-associated stroma in greater amounts and at larger distances from grade 3 versus grade 1 ductal carcinoma in situ. Collectively, these results suggest that algorithms based on deep convolutional neural networks that evaluate only stroma may prove useful to classify breast biopsies and aid in understanding and evaluating the biology of breast lesions.

Modeling of nanotherapeutics delivery based on tumor perfusion

PubMed Central

van de Ven, Anne L.; Abdollahi, Behnaz; Martinez, Carlos J.; Burey, Lacey A.; Landis, Melissa D.; Chang, Jenny C.; Ferrari, Mauro; Frieboes, Hermann B.

2013-01-01

Heterogeneities in the perfusion of solid tumors prevent optimal delivery of nanotherapeutics. Clinical imaging protocols to obtain patient-specific data have proven difficult to implement. It is challenging to determine which perfusion features hold greater prognostic value and to relate measurements to vessel structure and function. With the advent of systemically administered nanotherapeutics, whose delivery is dependent on overcoming diffusive and convective barriers to transport, such knowledge is increasingly important. We describe a framework for the automated evaluation of vascular perfusion curves measured at the single vessel level. Primary tumor fragments, collected from triple-negative breast cancer patients and grown as xenografts in mice, were injected with fluorescence contrast and monitored using intravital microscopy. The time to arterial peak and venous delay, two features whose probability distributions were measured directly from time-series curves, were analyzed using a Fuzzy C-mean (FCM) supervised classifier in order to rank individual tumors according to their perfusion characteristics. The resulting rankings correlated inversely with experimental nanoparticle accumulation measurements, enabling modeling of nanotherapeutics delivery without requiring any underlying assumptions about tissue structure or function, or heterogeneities contained within. With additional calibration, these methodologies may enable the study of nanotherapeutics delivery strategies in a variety of tumor models. PMID:24039540

Modeling of nanotherapeutics delivery based on tumor perfusion

NASA Astrophysics Data System (ADS)

van de Ven, Anne L.; Abdollahi, Behnaz; Martinez, Carlos J.; Burey, Lacey A.; Landis, Melissa D.; Chang, Jenny C.; Ferrari, Mauro; Frieboes, Hermann B.

2013-05-01

Heterogeneities in the perfusion of solid tumors prevent optimal delivery of nanotherapeutics. Clinical imaging protocols for obtaining patient-specific data have proven difficult to implement. It is challenging to determine which perfusion features hold greater prognostic value and to relate measurements to vessel structure and function. With the advent of systemically administered nanotherapeutics whose delivery is dependent on overcoming diffusive and convective barriers to transport, such knowledge is increasingly important. We describe a framework for the automated evaluation of vascular perfusion curves measured at the single vessel level. Primary tumor fragments, collected from triple-negative breast cancer patients and grown as xenografts in mice, were injected with fluorescence contrast and monitored using intravital microscopy. The time to arterial peak and venous delay, two features whose probability distributions were measured directly from time-series curves, were analyzed using a fuzzy c-mean supervised classifier in order to rank individual tumors according to their perfusion characteristics. The resulting rankings correlated inversely with experimental nanoparticle accumulation measurements, enabling the modeling of nanotherapeutics delivery without requiring any underlying assumptions about tissue structure or function, or heterogeneities contained therein. With additional calibration, these methodologies may enable the investigation of nanotherapeutics delivery strategies in a variety of tumor models.

Principles of treatment for soft tissue sarcoma.

PubMed

Dernell, W S; Withrow, S J; Kuntz, C A; Powers, B E

1998-02-01

Soft tissue sarcomas (STS) are mesenchymal tumors arising from connective tissue elements and are grouped together based on a common biologic behavior. The most common histologic types include malignant peripheral nerve sheath tumors (schwannoma and neurofibrosarcoma) "hemangiopericytoma," fibrosarcoma, and malignant fibrous histiocytoma. These tumors are relatively slow growing yet locally invasive with a high rate of recurrence following conservative management. Appropriate preoperative planning and aggressive surgical resection often result in long-term remission or cure. Identification and evaluation of resection margins are paramount in appropriate case management. The addition of radiotherapy after surgical resection can aid in remission for incompletely resected masses. Systemic chemotherapy for STS should be considered for high-grade tumors with a moderate metastatic potential. Potential prognostic factors include grade, resection margins, size, location, histologic type, and previous treatment, with grade and margins being the most important. Tumor types classified as STS that differ slightly in their presentation or treatment, including synovial cell sarcoma, rhabdomyosarcoma, liposarcoma, and vaccine-associated STS in cats, are discussed. Soft tissue sarcomas can be a frustrating disease to treat, but adherence to solid surgical oncology principles can greatly increase the odds of good disease control.

Remarkable difference of somatic mutation patterns between oncogenes and tumor suppressor genes.

PubMed

Liu, Haoxuan; Xing, Yuhang; Yang, Sihai; Tian, Dacheng

2011-12-01

Cancers arise owing to mutations that confer selective growth advantages on the cells in a subset of tumor suppressor and/or oncogenes. To understand oncogenesis and diagnose cancers, it is crucial to discriminate these two groups of genes by using the difference in their mutation patterns. Here, we investigated>120,000 mutation samples in 66 well-known tumor suppressor genes and oncogenes of the COSMIC database, and found a set of significant differences in mutation patterns (e.g., non-3n-indel, non-sense SNP and mutation hotspot) between them. By screening the best measurement, we developed indices to readily distinguish one from another and predict clearly the unknown oncogenesis genes as tumor suppressors (e.g., ASXL1, HNF1A and KDM6A) or oncogenes (e.g., FOXL2, MYD88 and TSHR). Based on our results, a third gene group can be classified, which has a mutational pattern between tumor suppressors and oncogenes. The concept of the third gene group could help to understand gene function in different cancers or individual patients and to know the exact function of genes in oncogenesis. In conclusion, our study provides further insights into cancer-related genes and identifies several potential therapeutic targets.

A nationwide study of ovarian serous borderline tumors in Denmark 1978-2002. Risk of recurrence, and development of ovarian serous carcinoma.

PubMed

Hannibal, Charlotte Gerd; Vang, Russell; Junge, Jette; Frederiksen, Kirsten; Kurman, Robert J; Kjaer, Susanne K

2017-01-01

Absolute risk and risk factors for recurrence and ovarian serous carcinoma following ovarian serous borderline tumors (SBTs) is not well-established. We included all women with SBTs in Denmark, 1978-2002. Diagnoses were confirmed by centralized pathology review and classified as atypical proliferative serous tumor (APST) or noninvasive low-grade serous carcinoma (LGSC). Implants were classified as noninvasive or invasive. Medical records were collected and reviewed, and follow-up was obtained. Subsequent diagnoses were also confirmed by centralized pathology review. We examined absolute risk and risk factors for recurrent APST and serous carcinoma using Cox regression. The absolute serous carcinoma risk after, respectively, 5 and 20years was 5.0% and 13.9% for noninvasive LGSC, and 0.9% and 3.7% for APST. Serous carcinoma risk was significantly higher following noninvasive LGSC compared with APST among stage I patients/patients without implants (HR=5.3; 95% CI: 1.7-16.3), whereas no significant association with tumor type was found in advanced stage patients/patients with implants. Advanced stage - notably invasive implants - bilaterality, surface involvement, and residual disease increased serous carcinoma risk. However, women with stage I APST also had a higher risk than the general population. This largest population-based cohort of verified SBTs revealed that women with noninvasive LGSC are significantly more likely to develop serous carcinoma than women with APST, which could not entirely be explained by invasive implants. Although invasive implants was a strong risk factor for serous carcinoma, even women with stage I APST were at increased risk compared with the general population. Copyright © 2016 Elsevier Inc. All rights reserved.

Site-specific tumor grading system in colorectal cancer: multicenter pathologic review of the value of quantifying poorly differentiated clusters.

PubMed

Ueno, Hideki; Hase, Kazuo; Hashiguchi, Yojiro; Shimazaki, Hideyuki; Tanaka, Masafumi; Miyake, Ohki; Masaki, Tadahiko; Shimada, Yoshifumi; Kinugasa, Yusuke; Mori, Yoshiyuki; Kishimoto, Mitsuo; Kameoka, Shingo; Sato, Yu; Matsuda, Keiji; Nakadoi, Koichi; Shinto, Eiji; Nakamura, Takahiro; Sugihara, Kenichi

2014-02-01

The study aimed to determine the value of a novel site-specific grading system based on quantifying poorly differentiated clusters (PDC; Grade(PDC)) in colorectal cancer (CRC). A multicenter pathologic review involving 12 institutions was performed on 3243 CRC cases (stage I, 583; II, 1331; III, 1329). Cancer clusters of ≥5 cancer cells and lacking a gland-like structure (PDCs) were counted under a ×20 objective lens in a field containing the maximum clusters. Tumors with <5, 5 to 9, and ≥10 PDCs were classified as grades G1, G2, and G3, respectively. According to Grade(PDC), 1594, 1005, and 644 tumors were classified as G1, G2, and G3 and had 5-year recurrence-free survival rates of 91.6%, 75.4%, and 59.6%, respectively (P<0.0001). Multivariate analysis showed that Grade exerted an influence on prognostic outcome independently of TNM staging; approximately 20% and 46% of stage I and II patients, respectively, were selected by Grade(PDC) as a population whose survival estimate was comparable to or even worse than that of stage III patients. Grade(PDC) surpassed TNM staging in the ability to stratify patients by recurrence-free survival (Akaike information criterion, 2915.6 vs. 2994.0) and had a higher prognostic value than American Joint Committee on Cancer (AJCC) grading (Grade(AJCC)) at all stages. Regarding judgment reproducibility of grading tumors, weighted κ among the 12 institutions was 0.40 for Grade(AJCC) and 0.52 for Grade(PDC). Grade(PDC) has a robust prognostic power and promises to be of sufficient clinical value to merit implementation as a site-specific grading system in CRC.

Expression of plakophilin 3 in diffuse malignant pleural mesothelioma.

PubMed

Mašić, Silvija; Brčić, Luka; Krušlin, Božo; Šepac, Ana; Pigac, Biserka; Stančić-Rokotov, Dinko; Jakopović, Marko; Seiwerth, Sven

2018-05-03

Diffuse malignant pleural mesothelioma (DMPM) is the most common primary malignant pleural neoplasm still posing major diagnostic, prognostic and therapeutic challenges. Plakophilins are structural proteins considered to be important for cell stability and adhesion in both tumor and normal tissues. Plakophilin 3 is a protein present in desmosomes of stratified and simple epithelia of normal tissues with presence in malignant cells of various tumors where it participates in the process of tumorigenesis. The aim of this study was to investigate the expression of plakophilin 3 protein in DMPM, but also to study its prognostic significance and relation to histologically accessible parameters of aggressive growth. Archival samples of tissue with established diagnosis of DMPM and samples of normal pleural tissue were used. Tumor samples were classified into three histological types of DMPM (epithelioid, sarcomatoid and biphasic). Additional subclassification of epithelioid mesotheliomas into nine patterns based on the prevalent histological component of the tumor was then performed. After immunohistochemical staining, cytoplasmic and membrane immunopositivity of tumor cells was assesed by scoring the intensity of the staining from 0 (no staining) to 4 (very strong staining). Prognostic value and expression of plakophilin 3 with consideration to histologically estimated aggression in tumor growth were then statistically analyzed using non- parametric tests. The results demonstrated higher level of plakophilin 3 expression in tumor samples with histologically more aggressive tumor growth, but no significant prognostic value. According to our study, plakophilin 3 appears to be involved in tumor invasion in malignant mesothelioma.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2012-01-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2011-12-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Comparing oncologic outcomes after minimally invasive and open surgery for pediatric neuroblastoma and Wilms tumor.

PubMed

Ezekian, Brian; Englum, Brian R; Gulack, Brian C; Rialon, Kristy L; Kim, Jina; Talbot, Lindsay J; Adibe, Obinna O; Routh, Jonathan C; Tracy, Elisabeth T; Rice, Henry E

2018-01-01

Minimally invasive surgery (MIS) has been widely adopted for common operations in pediatric surgery; however, its role in childhood tumors is limited by concerns about oncologic outcomes. We compared open and MIS approaches for pediatric neuroblastoma and Wilms tumor (WT) using a national database. The National Cancer Data Base from 2010 to 2012 was queried for cases of neuroblastoma and WT in children ≤21 years old. Children were classified as receiving open or MIS surgery for definitive resection, with clinical outcomes compared using a propensity matching methodology (two open:one MIS). For children with neuroblastoma, 17% (98 of 579) underwent MIS, while only 5% of children with WT (35 of 695) had an MIS approach for tumor resection. After propensity matching, there was no difference between open and MIS surgery for either tumor for 30-day mortality, readmissions, surgical margin status, and 1- and 3-year survival. However, in both tumors, open surgery more often evaluated lymph nodes and had larger lymph node harvest. Our retrospective review suggests that the use of MIS appears to be a safe method of oncologic resection for select children with neuroblastoma and WT. Further research should clarify which children are the optimal candidates for this approach. © 2017 Wiley Periodicals, Inc.

Metabolic profiles are principally different between cancers of the liver, pancreas and breast.

PubMed

Budhu, Anuradha; Terunuma, Atsushi; Zhang, Geng; Hussain, S Perwez; Ambs, Stefan; Wang, Xin Wei

2014-01-01

Molecular profiling of primary tumors may facilitate the classification of patients with cancer into more homogenous biological groups to aid clinical management. Metabolomic profiling has been shown to be a powerful tool in characterizing the biological mechanisms underlying a disease but has not been evaluated for its ability to classify cancers by their tissue of origin. Thus, we assessed metabolomic profiling as a novel tool for multiclass cancer characterization. Global metabolic profiling was employed to identify metabolites in paired tumor and non-tumor liver (n=60), breast (n=130) and pancreatic (n=76) tissue specimens. Unsupervised principal component analysis showed that metabolites are principally unique to each tissue and cancer type. Such a difference can also be observed even among early stage cancers, suggesting a significant and unique alteration of global metabolic pathways associated with each cancer type. Our global high-throughput metabolomic profiling study shows that specific biochemical alterations distinguish liver, pancreatic and breast cancer and could be applied as cancer classification tools to differentiate tumors based on tissue of origin.

The 2017 World Health Organization classification of tumors of the pituitary gland: a summary.

PubMed

Lopes, M Beatriz S

2017-10-01

The 4th edition of the World Health Organization (WHO) classification of endocrine tumors has been recently released. In this new edition, major changes are recommended in several areas of the classification of tumors of the anterior pituitary gland (adenophypophysis). The scope of the present manuscript is to summarize these recommended changes, emphasizing a few significant topics. These changes include the following: (1) a novel approach for classifying pituitary neuroendocrine tumors according to pituitary adenohypophyseal cell lineages; (2) changes to the histological grading of pituitary neuroendocrine tumors with the elimination of the term "atypical adenoma;" and (3) introduction of new entities like the pituitary blastoma and re-definition of old entities like the null-cell adenoma. This new classification is very practical and mostly based on immunohistochemistry for pituitary hormones, pituitary-specific transcription factors, and other immunohistochemical markers commonly used in pathology practice, not requiring routine ultrastructural analysis of the tumors. Evaluation of tumor proliferation potential, by mitotic count and Ki-67 labeling index, and tumor invasion is strongly recommended on individual case basis to identify clinically aggressive adenomas. In addition, the classification offers the treating clinical team information on tumor prognosis by identifying specific variants of adenomas associated with an elevated risk for recurrence. Changes in the classification of non-neuroendocrine tumors are also proposed, in particular those tumors arising in the posterior pituitary including pituicytoma, granular cell tumor of the posterior pituitary, and spindle cell oncocytoma. These changes endorse those previously published in the 2016 WHO classification of CNS tumors. Other tumors arising in the sellar region are also reviewed in detail including craniopharyngiomas, mesenchymal and stromal tumors, germ cell tumors, and hematopoietic tumors. It is hoped that the 2017 WHO classification of pituitary tumors will establish more biologically and clinically uniform groups of tumors, make it possible for practicing pathologists to better diagnose these tumors, and contribute to our understanding of clinical outcomes for patients harboring pituitary tumors.

Modeling Efficacy of Bevacizumab Treatment for Metastatic Colon Cancer

PubMed Central

Islam, Rezwan; Chyou, Po-Huang; Burmester, James K

2013-01-01

Purpose: Bevacizumab, an FDA-approved adjuvant treatment for metastatic colon cancer, has extended survival for many patients. However, factors predicting response to treatment remain undefined. Patients and Methods: Relevant clinical and environmental data were abstracted from medical records of 149 evaluable patients treated with bevacizumab for metastatic colon cancer at a multi-specialty clinic. Tumor response was calculated from radiologic reports using Response Evaluation Criteria in Solid Tumors (RECIST) criteria and verified by oncologist review. Patients with at least one occurrence of complete or partial response or stable disease were classified as responders; those exhibiting progressive disease were classified as non-responders. Results: Univariate analysis demonstrated that blood in stool (P<0.05), unexplained weight loss (P<0.05), primary colon cancer site (P<0.05), chemotherapy treatment of primary tumor site (P<0.05), and adenocarcinoma versus adenoma subtype (P<0.05) was associated with tumor responsiveness. Factors remaining statistically significant following multivariate modeling included adenocarcinoma as tumor cell type versus other adenocarcinoma subtypes (OR=6.35, 95% CI: 1.08-37.18), chemotherapy treatment applied to primary tumor (OR= 0.07, 95% CI: 0.0-0.76,), tumor localization to cecal/ascending colon (OR=0.061, 95% CI: 0.006-0.588,), and unexplained weight loss (OR=0.1, 95% CI: 0.02-0.56,). Chemotherapy treatment of primary tumor, unexplained weight loss, and cecal/ascending localization of the tumor were associated with poorer outcomes. Adenocarcinoma as cell type compared to other adenocarcinoma subtypes was associated with better response to bevacizumab treatment. Conclusion: Results suggest that response to bevacizumab therapy may be predicted by modeling clinical factors including symptomology on presentation, tumor location and type, and initial response to chemotherapy. PMID:23678369

[Expert systems and automatic diagnostic systems in histopathology--a review].

PubMed

Tamai, S

1999-02-01

In this decade, the pathological information system has gradually been settled in many hospitals in Japan. Pathological reports and images are now digitized and managed in the database, and are referred by clinicians at the peripherals. Tele-pathology is also developing; and its users are increasing. However, in many occasions, the problem solving in diagnostic pathology is completely dependent on the solo-pathologist. Considering the need for timely and efficient supports to the solo-pathologist, I reviewed the papers on the knowledge-based interactive expert systems. The interpretations of the histopathological images are dependent on the pathologist, and these expert systems have been evaluated as "educational". With the view of the success in the cytological screening, the development of "image-analysis-based" automatic "histopathological image" classifier has been on ongoing challenges. Our 3 years experience of the development of the pathological image classifier using the artificial neural networks technology is briefly presented. This classifier provides us a "fitting rate" for the individual diagnostic pattern of the breast tumors, such as "fibroadenoma pattern". The diagnosis assisting system with computer technology should provide pathologists, especially solo-pathologists, a useful tool for the quality assurance and improvement of pathological diagnosis.

TH-A-BRF-02: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Modeling Tumor Evolution for Adaptive Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Lee, CG; Chan, TCY

2014-06-15

Purpose: To develop mathematical models of tumor geometry changes under radiotherapy that may support future adaptive paradigms. Methods: A total of 29 cervical patients were scanned using MRI, once for planning and weekly thereafter for treatment monitoring. Using the tumor volumes contoured by a radiologist, three mathematical models were investigated based on the assumption of a stochastic process of tumor evolution. The “weekly MRI” model predicts tumor geometry for the following week from the last two consecutive MRI scans, based on the voxel transition probability. The other two models use only the first pair of consecutive MRI scans, and themore » transition probabilities were estimated via tumor type classified from the entire data set. The classification is based on either measuring the tumor volume (the “weekly volume” model), or implementing an auxiliary “Markov chain” model. These models were compared to a constant volume approach that represents the current clinical practice, using various model parameters; e.g., the threshold probability β converts the probability map into a tumor shape (larger threshold implies smaller tumor). Model performance was measured using volume conformity index (VCI), i.e., the union of the actual target and modeled target volume squared divided by product of these two volumes. Results: The “weekly MRI” model outperforms the constant volume model by 26% on average, and by 103% for the worst 10% of cases in terms of VCI under a wide range of β. The “weekly volume” and “Markov chain” models outperform the constant volume model by 20% and 16% on average, respectively. They also perform better than the “weekly MRI” model when β is large. Conclusion: It has been demonstrated that mathematical models can be developed to predict tumor geometry changes for cervical cancer undergoing radiotherapy. The models can potentially support adaptive radiotherapy paradigm by reducing normal tissue dose. This research was supported in part by the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF) and the MITACS Accelerate Internship Program.« less

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets.

PubMed

Faruki, Hawazin; Mayhew, Gregory M; Fan, Cheng; Wilkerson, Matthew D; Parker, Scott; Kam-Morgan, Lauren; Eisenberg, Marcia; Horten, Bruce; Hayes, D Neil; Perou, Charles M; Lai-Goldman, Myla

2016-06-01

Context .- A histologic classification of lung cancer subtypes is essential in guiding therapeutic management. Objective .- To complement morphology-based classification of lung tumors, a previously developed lung subtyping panel (LSP) of 57 genes was tested using multiple public fresh-frozen gene-expression data sets and a prospectively collected set of formalin-fixed, paraffin-embedded lung tumor samples. Design .- The LSP gene-expression signature was evaluated in multiple lung cancer gene-expression data sets totaling 2177 patients collected from 4 platforms: Illumina RNAseq (San Diego, California), Agilent (Santa Clara, California) and Affymetrix (Santa Clara) microarrays, and quantitative reverse transcription-polymerase chain reaction. Gene centroids were calculated for each of 3 genomic-defined subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine, the latter of which encompassed both small cell carcinoma and carcinoid. Classification by LSP into 3 subtypes was evaluated in both fresh-frozen and formalin-fixed, paraffin-embedded tumor samples, and agreement with the original morphology-based diagnosis was determined. Results .- The LSP-based classifications demonstrated overall agreement with the original clinical diagnosis ranging from 78% (251 of 322) to 91% (492 of 538 and 869 of 951) in the fresh-frozen public data sets and 84% (65 of 77) in the formalin-fixed, paraffin-embedded data set. The LSP performance was independent of tissue-preservation method and gene-expression platform. Secondary, blinded pathology review of formalin-fixed, paraffin-embedded samples demonstrated concordance of 82% (63 of 77) with the original morphology diagnosis. Conclusions .- The LSP gene-expression signature is a reproducible and objective method for classifying lung tumors and demonstrates good concordance with morphology-based classification across multiple data sets. The LSP panel can supplement morphologic assessment of lung cancers, particularly when classification by standard methods is challenging.

Mechanism-based classification of PAH mixtures to predict carcinogenic potential

DOE PAGES

Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; ...

2015-04-22

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[ a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[ def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profilesmore » measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential.

PubMed

Tilton, Susan C; Siddens, Lisbeth K; Krueger, Sharon K; Larkin, Andrew J; Löhr, Christiane V; Williams, David E; Baird, William M; Waters, Katrina M

2015-07-01

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC > BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system.

PubMed

Jiang, Weiqin; Shen, Yifei; Ding, Yongfeng; Ye, Chuyu; Zheng, Yi; Zhao, Peng; Liu, Lulu; Tong, Zhou; Zhou, Linfu; Sun, Shuo; Zhang, Xingchen; Teng, Lisong; Timko, Michael P; Fan, Longjiang; Fang, Weijia

2018-01-15

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment. © 2017 UICC.

Characterization of 1,577 Primary Prostate Cancers Reveals Novel Biological and Clinicopathological Insights into Molecular Subtypes

PubMed Central

Tomlins, Scott A.; Alshalalfa, Mohammed; Davicioni, Elai; Erho, Nicholas; Yousefi, Kasra; Zhao, Shuang; Haddad, Zaid; Den, Robert B.; Dicker, Adam P.; Trock, Bruce; DeMarzo, Angelo; Ross, Ashley; Schaeffer, Edward M.; Klein, Eric A.; Magi-Galluzzi, Cristina; Karnes, Jeffery R.; Jenkins, Robert B.; Feng, Felix Y.

2015-01-01

Background Prostate cancer (PCa) molecular subtypes have been defined by essentially mutually exclusive events, including ETS gene fusions (most commonly involving ERG) and SPINK1 over-expression. Clinical assessment may aid in disease stratification, complementing available prognostic tests. Objective To determine the analytical validity and clinicopatholgical associations of microarray-based molecular subtyping. Design, Setting and Participants We analyzed Affymetrix GeneChip expression profiles for 1,577 patients from eight radical prostatectomy (RP) cohorts, including 1,351 cases assessed using the Decipher prognostic assay (performed in a CLIA-certified laboratory). A microarray-based (m-) random forest ERG classification model was trained and validated. Outlier expression analysis was used to predict other mutually exclusive non-ERG ETS gene rearrangements (ETS+) or SPINK1 over-expression (SPINK1+). Outcome Measurements Associations with clinical features and outcomes by multivariable logistic regression analysis and receiver operating curves. Results and Limitations The m-ERG classifier showed 95% accuracy in an independent validation subset (n=155 samples). Across cohorts, 45%, 9%, 8% and 38% of PCa were classified as m-ERG+, m-ETS+, m-SPINK1+, and triple negative (m-ERG−/m-ETS−/m-SPINK1−), respectively. Gene expression profiling supports three underlying molecularly defined groups (m-ERG+, m-ETS+ and m-SPINK1+/triple negative). On multivariable analysis, m-ERG+ tumors were associated with lower preoperative serum PSA and Gleason scores, but enriched for extraprostatic extension (p<0.001). m-ETS+ tumors were associated with seminal vesicle invasion (p=0.01), while m-SPINK1+/triple negative tumors had higher Gleason scores and were more frequent in Black/African American patients (p<0.001). Clinical outcomes were not significantly different between subtypes. Conclusions A clinically available prognostic test (Decipher) can also assess PCa molecular subtypes, obviating the need for additional testing. Clinicopathological differences were found among subtypes based on global expression patterns. PMID:25964175

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

PubMed Central

Gangeh, Mehrdad; Tadayyon, Hadi; Sadeghi-Naini, Ali; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Tran, William; Czarnota, Gregory J.

2018-01-01

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients. PMID:29298305

Multicolor microRNA FISH effectively differentiates tumor types

PubMed Central

Renwick, Neil; Cekan, Pavol; Masry, Paul A.; McGeary, Sean E.; Miller, Jason B.; Hafner, Markus; Li, Zhen; Mihailovic, Aleksandra; Morozov, Pavel; Brown, Miguel; Gogakos, Tasos; Mobin, Mehrpouya B.; Snorrason, Einar L.; Feilotter, Harriet E.; Zhang, Xiao; Perlis, Clifford S.; Wu, Hong; Suárez-Fariñas, Mayte; Feng, Huichen; Shuda, Masahiro; Moore, Patrick S.; Tron, Victor A.; Chang, Yuan; Tuschl, Thomas

2013-01-01

MicroRNAs (miRNAs) are excellent tumor biomarkers because of their cell-type specificity and abundance. However, many miRNA detection methods, such as real-time PCR, obliterate valuable visuospatial information in tissue samples. To enable miRNA visualization in formalin-fixed paraffin-embedded (FFPE) tissues, we developed multicolor miRNA FISH. As a proof of concept, we used this method to differentiate two skin tumors, basal cell carcinoma (BCC) and Merkel cell carcinoma (MCC), with overlapping histologic features but distinct cellular origins. Using sequencing-based miRNA profiling and discriminant analysis, we identified the tumor-specific miRNAs miR-205 and miR-375 in BCC and MCC, respectively. We addressed three major shortcomings in miRNA FISH, identifying optimal conditions for miRNA fixation and ribosomal RNA (rRNA) retention using model compounds and high-pressure liquid chromatography (HPLC) analyses, enhancing signal amplification and detection by increasing probe-hapten linker lengths, and improving probe specificity using shortened probes with minimal rRNA sequence complementarity. We validated our method on 4 BCC and 12 MCC tumors. Amplified miR-205 and miR-375 signals were normalized against directly detectable reference rRNA signals. Tumors were classified using predefined cutoff values, and all were correctly identified in blinded analysis. Our study establishes a reliable miRNA FISH technique for parallel visualization of differentially expressed miRNAs in FFPE tumor tissues. PMID:23728175

A loss of profilin-1 in late-stage oral squamous cell carcinoma.

PubMed

Adami, Guy R; O'Callaghan, Thomas N; Kolokythas, Antonia; Cabay, Robert J; Zhou, Yalu; Schwartz, Joel L

2017-08-01

The genes for PFN1 and TMSB4 are both highly expressed in oral tissue and both encode actin monomer binding proteins thought to play a role in cell motility and possibly other crucial parts of tumor progression. Oral brush cytology of epithelium from oral squamous cell carcinoma (OSCC) was used to measure PFN1 and TMSB4 mRNA in OSCC, while immunohistochemical analysis of tissue was used to check protein levels. High but variable expression of mRNAs encoding these two proteins was observed suggesting they may contribute to tumor characteristics in a subset of OSCCs. Both proteins were highly expressed in normal appearing basal epithelium, in the cytoplasm, and perinuclear area, while expression was minimal in upper epithelial layers. In OSCCs, expression of these proteins varied. In tumors classified as later stage, based on size and/or lymph node involvement, PFN1 levels were lower in tumor epithelium. A control gene, KRT13, showed expression in normal differentiated basal and suprabasal oral mucosa epithelial cells and as reported was lost in OSCC cells. Loss of PFN1 in tumor cells has been associated with lymph node invasion and metastasis in other tumor types, strengthening the argument that the protein has the potential to be a tumor suppressor in late-stage OSCC. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images.

PubMed

Ikushima, Koujiro; Arimura, Hidetaka; Jin, Ze; Yabu-Uchi, Hidetake; Kuwazuru, Jumpei; Shioyama, Yoshiyuki; Sasaki, Tomonari; Honda, Hiroshi; Sasaki, Masayuki

2017-01-01

We have proposed a computer-assisted framework for machine-learning-based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV contours (determined based on the knowledge of radiation oncologists) into a machine-learning classifier during the training step, after which the classifier produces the 'degree of GTV' for each voxel in the testing step. Initial GTV regions were extracted using a support vector machine (SVM) that learned the image features inside and outside each tumor region (determined by radiation oncologists). The leave-one-out-by-patient test was employed for training and testing the steps of the proposed framework. The final GTV regions were determined using the OCS method that can be used to select a global optimum object contour based on multiple active delineations with a LSM around the GTV. The efficacy of the proposed framework was evaluated in 14 lung cancer cases [solid: 6, ground-glass opacity (GGO): 4, mixed GGO: 4] using the 3D Dice similarity coefficient (DSC), which denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those determined using the proposed framework. The proposed framework achieved an average DSC of 0.777 for 14 cases, whereas the OCS-based framework produced an average DSC of 0.507. The average DSCs for GGO and mixed GGO were 0.763 and 0.701, respectively, obtained by the proposed framework. The proposed framework can be employed as a tool to assist radiation oncologists in delineating various GTV regions. © The Author 2016. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

The IASLC Lung Cancer Staging Project: Background Data and Proposals for the Classification of Lung Cancer with Separate Tumor Nodules in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer.

PubMed

Detterbeck, Frank C; Bolejack, Vanessa; Arenberg, Douglas A; Crowley, John; Donington, Jessica S; Franklin, Wilbur A; Girard, Nicolas; Marom, Edith M; Mazzone, Peter J; Nicholson, Andrew G; Rusch, Valerie W; Tanoue, Lynn T; Travis, William D; Asamura, Hisao; Rami-Porta, Ramón

2016-05-01

Separate tumor nodules with the same histologic appearance occur in the lungs in a small proportion of patients with primary lung cancer. This article addresses how such tumors can be classified to inform the eighth edition of the anatomic classification of lung cancer. Separate tumor nodules should be distinguished from second primary lung cancer, multifocal ground glass/lepidic tumors, and pneumonic-type lung cancer, which are addressed in separate analyses. Survival of patients with separate tumor nodules in the International Association for the Study of Lung Cancer database were analyzed. This was compared with a systematic literature review. Survival of clinically staged patients decreased according to the location of the separate tumor nodule relative to the index tumor (same lobe > same side > other side) in N0 and N-any cohorts (all M0 except possible other-side nodules). However, there was also a decrease in the proportion of patients resected; among only surgically resected or among nonresected patients no survival differences were noted. There were no survival differences between patients with same-lobe nodules and those with other T3 tumors, between patients with same-side nodules and those with T4 tumors, and patients with other-side nodules and those with other M1a tumors. The data correlated with those identified in a literature review. Tumors with same-lobe separate tumor nodules (with the same histologic appearance) are recommended to be classified as T3, same-side nodules as T4, and other-side nodules as M1a. Thus, there is no recommended change between the seventh and eighth edition of the TNM classification of lung cancer. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

PubMed

Nguyen, Anthony N; Lawley, Michael J; Hansen, David P; Bowman, Rayleen V; Clarke, Belinda E; Duhig, Edwina E; Colquist, Shoni

2010-01-01

To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors.

PubMed

Vajapeyam, S; Stamoulis, C; Ricci, K; Kieran, M; Poussaint, T Young

2017-01-01

Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade. Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated. Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (K trans ), rate constant from extracellular extravascular space back into blood plasma (K ep ), and extracellular extravascular volume fraction (V e ) were all significantly correlated with tumor grade; high-grade tumors showed higher K trans , higher K ep , and lower V e . Although all 3 parameters had high specificity (range, 82%-100%), K ep had the highest specificity for both grades. Optimal sensitivity was achieved for V e , with a combined sensitivity of 76% (compared with 71% for K trans and K ep ). Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors. © 2017 by American Journal of Neuroradiology.

Glomeruloid Microvascular Proliferation, Desmoplasia, and High Proliferative Index as Potential Indicators of High Grade Canine Choroid Plexus Tumors.

PubMed

Muscatello, Luisa Vera; Avallone, Giancarlo; Serra, Fabienne; Seuberlich, Torsten; Mandara, Maria Teresa; Sisó, Silvia; Brunetti, Barbara; Oevermann, Anna

2018-05-01

Choroid plexus tumors (CPT) are intraventricular neoplasms accounting for 10% of all primary central nervous system tumors in dogs. They are frequently classified according to the human WHO classification into choroid plexus papilloma (CPP, grade I), atypical CPP (aCPP, grade II), and choroid plexus carcinoma (CPC, grade III). Histological features observed in canine CPT such as increased vascular density (IVD) and glomeruloid microvascular proliferation (GMVP) are not part of the WHO classification. This multi-centric study aimed to investigate tumor-associated vascular hyperplasia in dogs by determining the prevalence of GMVP and IVD in 52 canine CPT and their association with tumor grade. In addition, the expression of angiogenic factors was assessed by immunohistochemistry in 25 tumors to investigate the pathogenesis of tumor-associated vascular hyperplasia. Based on the classical histological hallmarks, this study of 52 CPT identified 22 (42%) CPP (grade I) and 30 of (58%) CPC (grade III). GMVP was more prevalent in CPC (13/30; 43%) than CPP (1/22; 4%), whereas IVD occurred to a similar extent in CPP and CPC. Desmoplasia was more common in CPC (19/30; 63%) than CPP (2/22; 9%), and similarly, the proliferative index (PI) of neoplastic epithelium was significantly higher in CPC (5.14%) than CPP (0.94%). The majority of CPT expressed platelet-derived growth factor (PDGF), PDGFRα, PDGFRβ, and vascular endothelial growth factor (VEGF) irrespective of tumor grade or tumor-associated vascular hyperplasia. These results suggest that tumor-associated GMVP, desmoplasia, and PI may serve as histological indicators of malignancy in CPT.

[Two Cases of Urachal Carcinoma Treated by TS-1/CDDP as Adjuvant Chemotherapy].

PubMed

Ozawa, Michinobu; Kuromoto, Akito; Morozumi, Kento; Satou, Masahiko; Hoshi, Senji; Numahata, Kenji

2017-10-01

Case 1 : A 48-year-old man presenting with gross hematuria was suspected to have a tumor located in the bladder dome. He was referred to our department for further examination and treatment. Cystoscopy showed a dome-shaped mass in the supravesical region. Computed tomography and magnetic resonance imaging indicated the possibility of urachal carcinoma and peritoneal dissemination. Therefore, partial cystectomy with urachal resection was performed. The intraoperative findings were disseminated peritoneal nodules and mucus entering the peritoneal cavity from the tumor. On pathological examination, the tumor was classified as a mucinous-type adenocarcinoma, and 6 courses of TS-1/cisplatin (CDDP) therapy were administered to the patient as adjuvant chemotherapy. To date (10 months since the surgery), there has been no disease progression. Case 2 : A 76-year-old woman was referred to our department with a finding of a tumor in the bladder dome during her detailed examination for lung tumors. Cystoscopy showed nodular tumors, indicating lung metastases of the urachal carcinoma. Therefore, partial cystectomy with urachal resection was performed. On pathological examination, the tumor was classified as an enteric-type adenocarcinoma, and 2 courses of TS-1/CDDP therapy were administered to the patient as adjuvant chemotherapy. However, due to the development of marked bone marrow depression, the drugs had to be discontinued. Nonetheless, the lung metastases markedly diminished in size. To date (9 months since the discontinuation of chemotherapy), there has been no disease progression.

Use of a 2-tier histologic grading system for canine cutaneous mast cell tumors on cytology specimens.

PubMed

Hergt, Franziska; von Bomhard, Wolf; Kent, Michael S; Hirschberger, Johannes

2016-09-01

Mast cell tumors (MCT) represent the most common malignant skin tumor in the dog. Diagnosis of an MCT can be achieved through cytologic examination of a fine-needle aspirate. However, the grade of the tumor is an important prognostic marker and currently requires histologic assessment. Recently a 2-tier histologic grading system based on nuclear features including number of mitoses, multinucleated cells, bizarre nuclei, and karyomegaly was proposed. The aim of this study was to assess if the cytomorphologic criteria proposed in the 2-tier histologic grading system are applicable to cytology specimens. A total of 141 MCT specimens reported as grade I, II, or III according to the Patnaik system with both histologic specimens and fine-needle aspirates available were histologically and cytologically reevaluated in a retrospective study. According to the 2-tier grading system, 38 cases were diagnosed histologically as high-grade and 103 as low-grade MCT. Cytologic grading resulted in 36 high-grade and 105 low-grade tumors. Agreement between histologic and cytologic grading based on the 2-tier grading system was achieved in 133 cases (sensitivity 86.8%, specificity 97.1%, kappa value 0.853), but 5 high-grade tumors on histology were classified as low-grade on cytology. Cytologic grading of MCT in the dog is helpful for initial assessment. However, the reliability of cytology using the 2-tier grading system is considered inadequate at this point. Prospective studies including clinical outcome should be pursued to further determine diagnostic accuracy of cytologic mast cell grading. © 2016 American Society for Veterinary Clinical Pathology.

Comparison of plasma amino acid profile-based index and CA125 in the diagnosis of epithelial ovarian cancers and borderline malignant tumors.

PubMed

Miyagi, Etsuko; Maruyama, Yasuyo; Mogami, Tae; Numazaki, Reiko; Ikeda, Atsuko; Yamamoto, Hiroshi; Hirahara, Fumiki

2017-02-01

We previously developed a new plasma amino acid profile-based index (API) to detect ovarian, cervical, and endometrial cancers. Here, we compared API to serum cancer antigen 125 (CA125) for distinguishing epithelial ovarian malignant tumors from benign growths. API and CA125 were measured preoperatively in patients with ovarian tumors, which were later classified into 59 epithelial ovarian cancers, 21 epithelial borderline malignant tumors, and 97 benign tumors including 40 endometriotic cysts. The diagnostic accuracy and cutoff points of API were evaluated using receiver operating characteristic (ROC) curves. The area under the ROC curves showed the equivalent performance of API and CA125 to discriminate between malignant/borderline malignant and benign tumors (both 0.77), and API was superior to CA125 for discrimination between malignant/borderline malignant lesions and endometriotic cysts (API, 0.75 vs. CA125, 0.59; p < 0.05). At the API cutoff level of 6.0, API and CA125 had equal positive rates of detecting cancers and borderline malignancies (API, 0.71 vs. CA125, 0.74; p = 0.84) or cancers alone (API, 0.73 vs. CA125, 0.85; p = 0.12). However, API had a significantly lower detection rate of benign endometriotic cysts (0.35; 95 % CI, 0.21-0.52) compared with that of CA125 (0.65; 95 % CI, 0.48-0.79) (p < 0.05). API is an effective new tumor marker to detect ovarian cancers and borderline malignancies with a low false-positive rate for endometriosis. A large-scale prospective clinical study using the cutoff value of API determined in this study is warranted to validate API for practical clinical use.

A Cross-Species Analysis in Pancreatic Neuroendocrine Tumors Reveals Molecular Subtypes with Distinctive Clinical, Metastatic, Developmental, and Metabolic Characteristics

PubMed Central

Sadanandam, Anguraj; Wullschleger, Stephan; Lyssiotis, Costas A.; Grötzinger, Carsten; Barbi, Stefano; Bersani, Samantha; Körner, Jan; Wafy, Ismael; Mafficini, Andrea; Lawlor, Rita T.; Simbolo, Michele; Asara, John M.; Bläker, Hendrik; Cantley, Lewis C.; Wiedenmann, Bertram; Scarpa, Aldo; Hanahan, Douglas

2016-01-01

Seeking to assess the representative and instructive value of an engineered mouse model of pancreatic neuroendocrine tumors (PanNET) for its cognate human cancer, we profiled and compared mRNA and miRNA transcriptomes of tumors from both. Mouse PanNET tumors could be classified into two distinctive subtypes, well-differentiated islet/insulinoma tumors (IT) and poorly differentiated tumors associated with liver metastases, dubbed metastasis-like primary (MLP). Human PanNETs were independently classified into these same two subtypes, along with a third, specific gene mutation–enriched subtype. The MLP subtypes in human and mouse were similar to liver metastases in terms of miRNA and mRNA transcriptome profiles and signature genes. The human/mouse MLP subtypes also similarly expressed genes known to regulate early pancreas development, whereas the IT subtypes expressed genes characteristic of mature islet cells, suggesting different tumorigenesis pathways. In addition, these subtypes exhibit distinct metabolic profiles marked by differential pyruvate metabolism, substantiating the significance of their separate identities. SIGNIFICANCE This study involves a comprehensive cross-species integrated analysis of multi-omics profiles and histology to stratify PanNETs into subtypes with distinctive characteristics. We provide support for the RIP1-TAG2 mouse model as representative of its cognate human cancer with prospects to better understand PanNET heterogeneity and consider future applications of personalized cancer therapy. PMID:26446169

A Cross-Species Analysis in Pancreatic Neuroendocrine Tumors Reveals Molecular Subtypes with Distinctive Clinical, Metastatic, Developmental, and Metabolic Characteristics.

PubMed

Sadanandam, Anguraj; Wullschleger, Stephan; Lyssiotis, Costas A; Grötzinger, Carsten; Barbi, Stefano; Bersani, Samantha; Körner, Jan; Wafy, Ismael; Mafficini, Andrea; Lawlor, Rita T; Simbolo, Michele; Asara, John M; Bläker, Hendrik; Cantley, Lewis C; Wiedenmann, Bertram; Scarpa, Aldo; Hanahan, Douglas

2015-12-01

Seeking to assess the representative and instructive value of an engineered mouse model of pancreatic neuroendocrine tumors (PanNET) for its cognate human cancer, we profiled and compared mRNA and miRNA transcriptomes of tumors from both. Mouse PanNET tumors could be classified into two distinctive subtypes, well-differentiated islet/insulinoma tumors (IT) and poorly differentiated tumors associated with liver metastases, dubbed metastasis-like primary (MLP). Human PanNETs were independently classified into these same two subtypes, along with a third, specific gene mutation-enriched subtype. The MLP subtypes in human and mouse were similar to liver metastases in terms of miRNA and mRNA transcriptome profiles and signature genes. The human/mouse MLP subtypes also similarly expressed genes known to regulate early pancreas development, whereas the IT subtypes expressed genes characteristic of mature islet cells, suggesting different tumorigenesis pathways. In addition, these subtypes exhibit distinct metabolic profiles marked by differential pyruvate metabolism, substantiating the significance of their separate identities. This study involves a comprehensive cross-species integrated analysis of multi-omics profiles and histology to stratify PanNETs into subtypes with distinctive characteristics. We provide support for the RIP1-TAG2 mouse model as representative of its cognate human cancer with prospects to better understand PanNET heterogeneity and consider future applications of personalized cancer therapy. ©2015 American Association for Cancer Research.

Testicular seminomatous mixed germ cell tumor with choriocarcinoma and teratoma with secondary somatic malignancy: a case report.

PubMed

Aneja, Amandeep; Bhattacharyya, Siddharth; Mydlo, Jack; Inniss, Susan

2014-01-01

Testicular tumors are a heterogeneous group of neoplasms exhibiting diverse histopathology and can be classified as seminomatous and non-seminomatous germ cell tumor types. Mixed germ cell tumors contain more than one germ cell component and various combinations have been reported. Here, we present a rare case of a mixed germ cell tumor composed of seminoma, choriocarcinoma and teratoma with a secondary somatic malignancy. A 31-year-old Caucasian man presented with splenic rupture to our hospital. A right-sided testicular swelling had been present for 6 months and his alpha-fetoprotein, beta-human chorionic gonadotropin, and lactose dehydrogenase were increased. An ultrasound of his scrotum revealed an enlarged right testis with heterogeneous echogenicity. Multiple hypervascular lesions were noted in his liver and spleen. He underwent transcatheter embolization therapy of his splenic artery followed by splenectomy and right-sided orchiectomy. A computed tomography scan also showed metastasis to both lungs. During his last follow up after four cycles of cisplatin-based chemotherapy, the level of tumor markers had decreased, decreases in the size of his liver and pulmonary lesions were noted but new sclerotic lesions were evident in his thoracolumbar region raising concern for bony metastasis. Prognosis of testicular tumor depends mainly on the clinical stage, but emergence of a sarcomatous component presents a challenge in the treatment of germ cell tumors and the histological subtype of this component can be used as a guide to specific chemotherapy in these patients.

Limitations of three-dimensional power Doppler angiography in preoperative evaluation of ovarian tumors.

PubMed

Silvestre, Liliane; Martins, Wellington P; Candido-Dos-Reis, Francisco J

2015-07-29

This study describes the accuracy of three-dimensional power Doppler (3D-PD) angiography as secondary method for differential diagnosis of ovarian tumors. Seventy-five women scheduled for surgical removal of adnexal masses were assessed by transvaginal ultrasound. Ovarian tumors were classified by IOTA simple rules and two three-dimensional blocks were recorded. In a second step analyses, a 4 cm(3) spherical sample was obtained from the highest vascularized solid area of each stored block. Vascularization index (VI), flow index (FI) and vascularization-flow index (VFI) were calculated. The repeatability was assessed by concordance correlation coefficient (CCC) and limits of agreement (LoA), and diagnostic accuracy by area under ROC curve. IOTA simple rules classified 26 cases as benign, nine as inconclusive and 40 as malignant. There were eight false positive and no false negative. Among the masses classified as inconclusive or malignant by IOTA simple rules, the CCCs were 0.91 for VI, 0.70 for FI, and 0.86 for VFI. The areas under ROC curve were 0.82 for VI, 0.67 for FI and 0.81 for VFI. 3D-PD angiography presented considerable intraobserver variability and low accuracy for identifying false positive results of IOTA simple rules.

Isolation of circulating tumor cells in pancreatic cancer patients by immunocytochemical assay.

PubMed

Yang, Jing; Zhou, Ying; Zhao, Bin

2018-01-01

The patients diagnosed with pancreatic cancer have the possibilities of getting the cancer again even after resection. The tumor cells identified from blood can be related to different stages of tumor. In this study, we used an immunoassay to detect circulating tumor cells in blood and bone marrow samples. About 120 patients' blood and bone marrow samples were used in this study along with controls. The presence of tumor cells was evaluated with different stages of cancer classified by UICC. The survival rate at each stages of tumor was also analyzed. The tumor cells were isolated both in blood (29%) and bone marrow samples (25%). The prevalence of tumor cells increased with increase in stages of tumor in blood samples. The survival of the patients considerably related to different stages of tumor but it cannot be taken a parameter alone for the patients' survival. © 2017 Wiley Periodicals, Inc.

MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma.

PubMed

Ramkumar, S; Ranjbar, S; Ning, S; Lal, D; Zwart, C M; Wood, C P; Weindling, S M; Wu, T; Mitchell, J R; Li, J; Hoxworth, J M

2017-05-01

Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging-based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review. Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus. The inverted papilloma ( n = 24) and squamous cell carcinoma ( n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger ( P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively ( P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists' ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists' review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748). MR imaging-based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist. © 2017 by American Journal of Neuroradiology.

Performance of the IOTA ADNEX model in preoperative discrimination of adnexal masses in a gynecological oncology center.

PubMed

Araujo, K G; Jales, R M; Pereira, P N; Yoshida, A; de Angelo Andrade, L; Sarian, L O; Derchain, S

2017-06-01

To evaluate the performance of the International Ovarian Tumor Analysis (IOTA) ADNEX model in the preoperative discrimination between benign ovarian (including tubal and para-ovarian) tumors, borderline ovarian tumors (BOT), Stage I ovarian cancer (OC), Stage II-IV OC and ovarian metastasis in a gynecological oncology center in Brazil. This was a diagnostic accuracy study including 131 women with an adnexal mass invited to participate between February 2014 and November 2015. Before surgery, pelvic ultrasound examination was performed and serum levels of tumor marker CA 125 were measured in all women. Adnexal masses were classified according to the IOTA ADNEX model. Histopathological diagnosis was the gold standard. Receiver-operating characteristics (ROC) curve analysis was used to determine the diagnostic accuracy of the model to classify tumors into different histological types. Of 131 women, 63 (48.1%) had a benign ovarian tumor, 16 (12.2%) had a BOT, 17 (13.0%) had Stage I OC, 24 (18.3%) had Stage II-IV OC and 11 (8.4%) had ovarian metastasis. The area under the ROC curve (AUC) was 0.92 (95% CI, 0.88-0.97) for the basic discrimination between benign vs malignant tumors using the IOTA ADNEX model. Performance was high for the discrimination between benign vs Stage II-IV OC, BOT vs Stage II-IV OC and Stage I OC vs Stage II-IV OC, with AUCs of 0.99, 0.97 and 0.94, respectively. Performance was poor for the differentiation between BOT vs Stage I OC and between Stage I OC vs ovarian metastasis with AUCs of 0.64. The majority of adnexal masses in our study were classified correctly using the IOTA ADNEX model. On the basis of our findings, we would expect the model to aid in the management of women with an adnexal mass presenting to a gynecological oncology center. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

The latest animal models of ovarian cancer for novel drug discovery.

PubMed

Magnotti, Elizabeth; Marasco, Wayne A

2018-03-01

Epithelial ovarian cancer is a heterogeneous disease classified into five subtypes, each with a different molecular profile. Most cases of ovarian cancer are diagnosed after metastasis of the primary tumor and are resistant to traditional platinum-based chemotherapeutics. Mouse models of ovarian cancer have been utilized to discern ovarian cancer tumorigenesis and the tumor's response to therapeutics. Areas covered: The authors provide a review of mouse models currently employed to understand ovarian cancer. This article focuses on advances in the development of orthotopic and patient-derived tumor xenograft (PDX) mouse models of ovarian cancer and discusses current humanized mouse models of ovarian cancer. Expert opinion: The authors suggest that humanized mouse models of ovarian cancer will provide new insight into the role of the human immune system in combating and augmenting ovarian cancer and aid in the development of novel therapeutics. Development of humanized mouse models will take advantage of the NSG and NSG-SGM3 strains of mice as well as new strains that are actively being derived.

Analysis of the Fibrinogen and Neutrophil–Lymphocyte Ratio in Esophageal Squamous Cell Carcinoma

PubMed Central

Arigami, Takaaki; Okumura, Hiroshi; Matsumoto, Masataka; Uchikado, Yasuto; Uenosono, Yoshikazu; Kita, Yoshiaki; Owaki, Tetsuhiro; Mori, Shinichiro; Kurahara, Hiroshi; Kijima, Yuko; Ishigami, Sumiya; Natsugoe, Shoji

2015-01-01

Abstract Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignancies in gastrointestinal tract cancers and even patients with early ESCC have a high metastatic potential. Difficulties are associated with clinically predicting tumor progression and prognosis based on conventional tumor markers determined from preoperative blood examinations. The aim of the present study was to measure plasma fibrinogen levels and the neutrophil–lymphocyte ratio (NLR) in blood and compare the clinical impacts of their combined values (fibrinogen and neutrophil–lymphocyte ratio score—F-NLR score) and the modified Glasgow Prognostic Score (mGPS) in patients with ESCC. We classified 238 patients with ESCC based on cut-off values for hyperfibrinogenemia (>400 mg/dL) and high NLR (>3.0) as F-NLR scores of 2 (both of these hematological abnormalities), 1 (one of these abnormalities), or 0 (neither abnormality). We also categorized patients based on cut-off values for high C-reactive protein (CRP) (>0.5 mg/dL) and hypoalbuminemia (<3.8 g/dL) as mGPS of 2 (elevated CRP and hypoalbuminemia), 1 (either elevated CRP or hypoalbuminemia), or 0 (neither elevated CRP nor hypoalbuminemia). The F-NLR score correlated with the depth of tumor invasion, lymph node metastasis, lymphovascular invasion, tumor size, and stage (all P < 0.05). Prognoses among the groups based on the F-NLR score and mGPS significantly differed (all P < 0.001). A multivariate analysis identified the depth of tumor invasion, lymph node metastasis, and F-NLR score as independent prognostic factors (P = 0.002, P = 0.007, and P = 0.037, respectively). The results of the present study showed that the F-NLR score is a promising blood predictor for tumor progression and outcomes in patients with ESCC. PMID:26496280

Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: Differences by age, race, and tumor characteristics

PubMed Central

Sweeney, Carol; Bernard, Philip S.; Factor, Rachel E.; Kwan, Marilyn L.; Habel, Laurel A.; Quesenberry, Charles P.; Shakespear, Kaylynn; Weltzien, Erin K.; Stijleman, Inge J.; Davis, Carole A.; Ebbert, Mark T.W.; Castillo, Adrienne; Kushi, Lawrence H.; Caan, Bette J.

2014-01-01

Background Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups. Methods We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology (LACE) and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR) was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier. Results In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% Luminal A, 20.5% Luminal B, 13.0% HER2-enriched, 9.8% Basal-like, and 3.6% Normal-like. Among low-risk endocrine positive tumors (i.e. estrogen and progesterone receptor positive by immunohistochemistry, Her2 negative, and low histologic grade), only 76.5% were categorized as Luminal A by PAM50. Continuous-scale Luminal A, Luminal B, HER2-enriched, and Normal-like scores from PAM50 were mutually positively correlated; Basal-like score was inversely correlated with other subtypes. The proportion with non-Luminal A subtype decreased with older age at diagnosis, p trend < 0.0001. Compared with non-Hispanic whites, African-American women were more likely to have Basal-like tumors, age-adjusted odds ratio (OR) 4.4 (95% CI 2.3,8.4), whereas Asian and Pacific Islander women had reduced odds of Basal-like subtype, OR 0.5 (95% CI 0.3,0.9). Conclusions Our data indicate that over 50% of breast cancers treated in the community have Luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathological criteria to higher-risk subtypes. Impact Subtyping in a population-based cohort revealed distinct profiles by age and race. PMID:24521995

Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

PubMed Central

Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-01

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55.km, 87.56.Fc PMID:26894358

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

PubMed

Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-08

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers.

A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

NASA Astrophysics Data System (ADS)

Judd, Nicolas; Smith, Jason; Jain, Manu; Mukherjee, Sushmita; Icaza, Michael; Gallagher, Ryan; Szeligowski, Richard; Wu, Binlin

2018-02-01

A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification.

Overview of Pediatric Testicular Tumors in Korea

PubMed Central

Chung, Jae Min

2014-01-01

Prepubertal testicular tumors are rare compared with postpubertal testicular tumors. The incidence of prepubertal testicular tumors peaks at 2 years of age, tapers off after 4 years of age, and then begins to rise again at puberty. Prepubertal and postpubertal testicular tumors show many differences, including the typical tumor histology, molecular biological differences, and the malignant potential of tumors at different ages. Pediatric testicular tumors are classified as benign or malignant on the basis of their clinical behavior and histologically are divided into germ cell and gonadal stromal (nongerm cell) tumors. Many histological and biological studies have further confirmed the distinct nature of prepubertal and postpubertal testicular tumors. These differences have led to various management strategies for prepubertal and postpubertal tumors. Because overall about 75% of prepubertal testicular tumors are benign, a testis-sparing approach is becoming more common in children. Orchiectomy and observation with very selective use of chemotherapy has become the standard approach when a malignant tumor is identified. Retroperitoneal lymph node dissection and radiation therapy play very limited roles. PMID:25512812

Incidence and survival of children with central nervous system primitive tumors in the French National Registry of Childhood Solid Tumors.

PubMed

Desandes, Emmanuel; Guissou, Sandra; Chastagner, Pascal; Lacour, Brigitte

2014-07-01

Central nervous system (CNS) tumors are the second most common childhood malignancy. The French National Registry of Childhood Solid Tumors (NRCST) makes it possible to describe this variety of distinct tumor types and to provide incidence and survival data in France on a nationwide basis. All children aged 0-14 years, who were registered with a primary CNS tumor in the NRCST of France between 2000 and 2008, were identified. Tumors were classified according to the International Classification of Childhood Cancer, third edition. Approximately 57% of pediatric CNS tumors were gliomas, with astrocytomas of the pilocytic type predominating. Distributions of subtypes by age showed that primitive neuroectodermal tumors and ependymomas mainly occurred in children aged <5 years. The mean annual incidence rate of CNS tumors was 39 per million. No statistically significant change in time trends of incidence rate was observed during 2000-2008. For all tumors combined, overall survival was 84.8% (95% CI, 83.7%-85.9%) at 1 year and 72.9% (95% CI, 71.5%-74.3%) at 5 years. Survival time trends were studied in a multivariate analysis observing a reduction in the risk of death in periods of diagnosis 2003-2005 (HR = 0.8; 95% CI, 0.7–0.9) and 2006-2008 (HR = 0.7; 95% CI, 0.6-0.9) compared with 2000-2002. The stable incidence rates during the last 10 years could indicate that major changes in environmental risk factors are unlikely, but the ongoing need for population-based surveillance remains relevant. Results indicate a positive trend in the survival probability still persistent in the 2000s.

Soft tissue sarcomas in the African hedgehog (Atelerix albiventris): microscopic and immunohistologic study of three cases.

PubMed

Ramos-Vara, J A

2001-09-01

Three soft tissue tumors from 2 female hedgehogs were examined microscopically and immunohistochemically. Two tumors involved haired skin and the third one was vaginal. Microscopically, the cutaneous tumors had features of malignant peripheral nerve sheath tumor (MPNST), whereas the vaginal tumor was classified only as a spindle cell sarcoma. Immunohistochemically, all 3 tumors were strongly positive for vimentin and strongly to moderately positive for CD10 and neuron-specific enolase but did not stain with antibody to S100 protein, an antigen typically present in human MPNST The cutaneous tumor from hedgehog no. 1 was examined ultrastructurally and the neoplastic cells resembled fibroblasts. Hedgehog no. 1 was euthanized at the time of the biopsy. The outcome of the other hedgehog was unknown.

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

PubMed

Cario, Clinton L; Witte, John S

2018-03-15

As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

NASA Astrophysics Data System (ADS)

Hao, Hongxia; Zhou, Zhiguo; Li, Shulong; Maquilan, Genevieve; Folkert, Michael R.; Iyengar, Puneeth; Westover, Kenneth D.; Albuquerque, Kevin; Liu, Fang; Choy, Hak; Timmerman, Robert; Yang, Lin; Wang, Jing

2018-05-01

Distant failure is the main cause of human cancer-related mortalities. To develop a model for predicting distant failure in non-small cell lung cancer (NSCLC) and cervix cancer (CC) patients, a shell feature, consisting of outer voxels around the tumor boundary, was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The utility of the shell was evaluated by the support vector machine classifier in comparison with intensity, geometry, gray level co-occurrence matrix-based texture, neighborhood gray tone difference matrix-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and AUC. Collectively, the shell feature showed better predictive performance than all the other features for distant failure prediction in both NSCLC and CC cohorts.

Melanocytoma-like melanoma may be the missing link between benign and malignant uveal melanocytic lesions in humans and dogs: a comparative study.

PubMed

Zoroquiain, Pablo; Mayo-Goldberg, Erin; Alghamdi, Sarah; Alhumaid, Sulaiman; Perlmann, Eduardo; Barros, Paulo; Mayo, Nancy; Burnier, Miguel N

2016-12-01

The cutoff presented in the current classification of canine melanocytic lesions by Wilcock and Pfeiffer is based on the clinical outcome rather than morphological concepts. Classification of tumors based on morphology or molecular signatures is the key to identifying new therapies or prognostic factors. Therefore, the aim of this study was to analyze morphological findings in canine melanocytic lesions based on classic malignant morphologic principles of neoplasia and to compare these features with human uveal melanoma (HUM) samples. In total, 64 canine and 111 human morphologically malignant melanocytic lesions were classified into two groups (melanocytoma-like or classic melanoma) based on the presence or absence of M cells, respectively. Histopathological characteristics were compared between the two groups using the χ-test, t-test, and multivariate discriminant analysis. Among the 64 canine tumors, 28 (43.7%) were classic and 36 (56.3%) were melanocytoma-like melanomas. Smaller tumor size, a higher degree of pigmentation, and lower mitotic activity distinguished melanocytoma-like from classic tumors with an accuracy of 100% for melanocytoma-like lesions. From the human series, only one case showed melanocytoma-like features and had a low risk for metastasis characteristics. Canine uveal melanoma showed a morphological spectrum with features similar to the HUM counterpart (classic melanoma) and overlapped features between uveal melanoma and melanocytoma (melanocytoma-like melanoma). Recognition that the subgroup of melanocytoma-like melanoma may represent the missing link between benign and malignant lesions could help explain the progression of uveal melanoma in dogs; these findings can potentially be translated to HUM.

Pediatric Sarcomas Are Targetable by MR-Guided High Intensity Focused Ultrasound (MR-HIFU): Anatomical Distribution and Radiological Characteristics.

PubMed

Shim, Jenny; Staruch, Robert M; Koral, Korgun; Xie, Xian-Jin; Chopra, Rajiv; Laetsch, Theodore W

2016-10-01

Despite intensive therapy, children with metastatic and recurrent sarcoma or neuroblastoma have a poor prognosis. Magnetic resonance guided high intensity focused ultrasound (MR-HIFU) is a noninvasive technique allowing the delivery of targeted ultrasound energy under MR imaging guidance. MR-HIFU may be used to ablate tumors without ionizing radiation or target chemotherapy using hyperthermia. Here, we evaluated the anatomic locations of tumors to assess the technical feasibility of MR-HIFU therapy for children with solid tumors. Patients with sarcoma or neuroblastoma with available cross-sectional imaging were studied. Tumors were classified based on the location and surrounding structures within the ultrasound beam path as (i) not targetable, (ii) completely or partially targetable with the currently available MR-HIFU system, and (iii) potentially targetable if a respiratory motion compensation technique was used. Of the 121 patients with sarcoma and 61 patients with neuroblastoma, 64% and 25% of primary tumors were targetable at diagnosis, respectively. Less than 20% of metastases at diagnosis or relapse were targetable for both sarcoma and neuroblastoma. Most targetable lesions were located in extremities or in the pelvis. Respiratory motion compensation may increase the percentage of targetable tumors by 4% for sarcomas and 10% for neuroblastoma. Many pediatric sarcomas are localized at diagnosis and are targetable by current MR-HIFU technology. Some children with neuroblastoma have bony tumors targetable by MR-HIFU at relapse, but few newly diagnosed children with neuroblastoma have tumors amenable to MR-HIFU therapy. Clinical trials of MR-HIFU should focus on patients with anatomically targetable tumors. © 2016 Wiley Periodicals, Inc.

Pancreatic neuroendocrine tumor with splenic vein tumor thrombus: A case report

PubMed Central

Rodriguez, Rodrigo A.; Overton, Heidi; Morris, Katherine T.

2014-01-01

INTRODUCTION Pancreatic neuroendocrine tumors (PNET) are rare, often indolent malignancies. PNET are classified as functional or nonfunctional based on the secretion of hormones without a negative feedback loop; the latter account for up to 60% of PNET. Although PNET are associated with a better prognosis compared to pancreatic adenocarcinomas, they are often diagnosed in advanced stages, making them a significant source of morbidity for patients. Here we present a rare case of venous tumor thrombus arising from a nonfunctional PNET. PRESENTATION OF CASE A 44-year-old woman was referred for evaluation and treatment of a possible tail of pancreas PNET discovered during work-up for a 9 year history of intermittent subcostal pain. Previous endoscopic ultrasound with fine needle aspiration revealed a 3.5 cm × 3 cm mass, with cytological diagnosis of neuroendocrine tumor. Patient was scheduled for laparoscopic distal pancreatectomy. During surgery the mass was found to encase the splenic vein leading the surgeon to perform an en bloc distal pancreatectomy and splenectomy. Pathologic analysis revealed a 1.8 cm × 5 cm tumor thrombus lodged in the splenic vein. DISCUSSION Nonfunctional PNET usually present in advanced stages and can be associated with venous tumor thrombi. Preoperative imaging may not accurately predict the presence of venous tumor thrombi. CONCLUSION En bloc resection of primary tumor, involved organs and thrombus is the recommended treatment option and often results in long term survival. New multi-modality strategies are needed for detection of venous involvement in nonfunctional PNET to better assist with preoperative planning and counseling. PMID:25460491

Pancreatic neuroendocrine tumor with splenic vein tumor thrombus: A case report.

PubMed

Rodriguez, Rodrigo A; Overton, Heidi; Morris, Katherine T

2014-01-01

Pancreatic neuroendocrine tumors (PNET) are rare, often indolent malignancies. PNET are classified as functional or nonfunctional based on the secretion of hormones without a negative feedback loop; the latter account for up to 60% of PNET. Although PNET are associated with a better prognosis compared to pancreatic adenocarcinomas, they are often diagnosed in advanced stages, making them a significant source of morbidity for patients. Here we present a rare case of venous tumor thrombus arising from a nonfunctional PNET. A 44-year-old woman was referred for evaluation and treatment of a possible tail of pancreas PNET discovered during work-up for a 9 year history of intermittent subcostal pain. Previous endoscopic ultrasound with fine needle aspiration revealed a 3.5cm×3cm mass, with cytological diagnosis of neuroendocrine tumor. Patient was scheduled for laparoscopic distal pancreatectomy. During surgery the mass was found to encase the splenic vein leading the surgeon to perform an en bloc distal pancreatectomy and splenectomy. Pathologic analysis revealed a 1.8cm×5cm tumor thrombus lodged in the splenic vein. Nonfunctional PNET usually present in advanced stages and can be associated with venous tumor thrombi. Preoperative imaging may not accurately predict the presence of venous tumor thrombi. En bloc resection of primary tumor, involved organs and thrombus is the recommended treatment option and often results in long term survival. New multi-modality strategies are needed for detection of venous involvement in nonfunctional PNET to better assist with preoperative planning and counseling. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Case of Desmoplastic Small Round Cell Tumor.

PubMed

Reisner, David; Brahee, Deborah; Patel, Shweta; Hartman, Matthew

2015-08-01

Desmoplastic small round cell tumor is a rare, aggressive tumor primarily affecting young males. It is considered a childhood cancer, and is characterized by a unique chromosomal translocation which leads to failure to suppress tumor growth. It is classified as a soft tissue sarcoma, sharing some features with other small round cell tumors such as Ewing's Sarcoma and primitive neuroectodermal tumor. Typical imaging findings include multiple heterogeneous, lobular abdominal masses, which can grow very large. Often there is a dominant mass with additional peritoneal, omental, retroperitoneal and retrovesical masses. Prognosis is relatively poor with a 3 year survival rate of 50% in those treated aggressively with surgical resection, chemotherapy, and radiation therapy. The clinical presentation, imaging characteristics and pathology are discussed in regards to a recent case.

Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

PubMed Central

Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

2013-01-01

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

Effects of the length of central cancer registry operations on identification of subsequent cancers and on survival estimates.

PubMed

Qiao, Baozhen; Schymura, Maria J; Kahn, Amy R

2016-10-01

Population-based cancer survival analyses have traditionally been based on the first primary cancer. Recent studies have brought this practice into question, arguing that varying registry reference dates affect the ability to identify earlier cancers, resulting in selection bias. We used a theoretical approach to evaluate the extent to which the length of registry operations affects the classification of first versus subsequent cancers and consequently survival estimates. Sequence number central was used to classify tumors from the New York State Cancer Registry, diagnosed 2001-2010, as either first primaries (value=0 or 1) or subsequent primaries (≥2). A set of three sequence numbers, each based on an assumed reference year (1976, 1986 or 1996), was assigned to each tumor. Percent of subsequent cancers was evaluated by reference year, cancer site and age. 5-year relative survival estimates were compared under four different selection scenarios. The percent of cancer cases classified as subsequent primaries was 15.3%, 14.3% and 11.2% for reference years 1976, 1986 and 1996, respectively; and varied by cancer site and age. When only the first primary was included, shorter registry operation time was associated with slightly lower 5-year survival estimates. When all primary cancers were included, survival estimates decreased, with the largest decreases seen for the earliest reference year. Registry operation length affected the identification of subsequent cancers, but the overall effect of this misclassification on survival estimates was small. Survival estimates based on all primary cancers were slightly lower, but might be more comparable across registries. Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecular classification of gastric cancer: a new paradigm.

PubMed

Shah, Manish A; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y; Klimstra, David S; Gerdes, Hans; Kelsen, David P

2011-05-01

Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. ©2011 AACR.

Molecular Classification of Gastric Cancer: A new paradigm

PubMed Central

Shah, Manish A.; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y.; Klimstra, David S.; Gerdes, Hans; Kelsen, David P.

2011-01-01

Purpose Gastric cancer may be subdivided into three distinct subtypes –proximal, diffuse, and distal gastric cancer– based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Experimental Design Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (NCI 5917) underwent endoscopic biopsy for fresh tumor procurement. 4–6 targeted biopsies of the primary tumor were obtained. Macrodissection was performed to ensure >80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Results Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the three gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross validation error was 0.14, suggesting that >85% of samples were classified correctly. Gene set analysis with the False Discovery Rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Conclusions Subtypes of gastric cancer that have epidemiologic and histologic distinction are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. PMID:21430069

Interobserver variability for the WHO classification of pulmonary carcinoids.

PubMed

Swarts, Dorian R A; van Suylen, Robert-Jan; den Bakker, Michael A; van Oosterhout, Matthijs F M; Thunnissen, Frederik B J M; Volante, Marco; Dingemans, Anne-Marie C; Scheltinga, Marc R M; Bootsma, Gerben P; Pouwels, Harry M M; van den Borne, Ben E E M; Ramaekers, Frans C S; Speel, Ernst-Jan M

2014-10-01

Pulmonary carcinoids are neuroendocrine tumors histopathologically subclassified into typical (TC; no necrosis, <2 mitoses per 2 mm) and atypical (AC; necrosis or 2 to 10 mitoses per 2 mm). The reproducibility of lung carcinoid classification, however, has not been extensively studied and may be hampered by the presence of pyknotic apoptosis mimicking mitotic figures. Furthermore, prediction of prognosis based on histopathology varies, especially for ACs. We examined the presence of interobserver variation between 5 experienced pulmonary pathologists who reviewed 123 originally diagnosed pulmonary carcinoid cases. The tumors were subsequently redistributed over 3 groups: unanimously classified cases, consensus cases (4/5 pathologists rendered identical diagnosis), and disagreement cases (divergent diagnosis by ≥2 assessors). κ-values were calculated, and results were correlated with clinical follow-up and molecular data. When focusing on the 114/123 cases unanimously classified as pulmonary carcinoids, the interobserver agreement was only fair (κ=0.32). Of these 114 cases, 55% were unanimously classified, 25% reached consensus classification, and for 19% there was no consensus. ACs were significantly more often in the latter category (P=0.00038). The designation of TCs and ACs by ≥3 assessors was not associated with prognosis (P=0.11). However, when disagreement cases were allocated on the basis of Ki-67 proliferative index (<5%; ≥5%) or nuclear orthopedia homeobox immunostaining (+; -), correlation with prognosis improved significantly (P=0.00040 and 0.0024, respectively). In conclusion, there is a considerable interobserver variation in the histopathologic classification of lung carcinoids, in particular concerning ACs. Additional immunomarkers such as Ki-67 or orthopedia homeobox may improve classification and prediction of prognosis.

Targeted Screening With Combined Age- and Morphology-Based Criteria Enriches Detection of Lynch Syndrome in Endometrial Cancer.

PubMed

Lin, Douglas I; Hecht, Jonathan L

2016-06-01

Endometrial cancer is associated with Lynch syndrome in 2% to 6% of cases. Adequate screening may prevent of a second cancer and incident cancers in family members via risk-reducing strategies. The goal of the study was to evaluate the detection rate of Lynch syndrome via a targeted screening approach. In 2009, we incorporated targeted Lynch syndrome screening via immunohistochemistry for MLH1, PMS2, MSH2, and MSH6, followed by MLH1 promoter hypermethylation, in select cases of endometrial carcinoma. Criteria for patient selection included (1) all patients <50 years; (2) patients of any age with tumors showing features of microsatellite instability (lower uterine segment-centered tumors, hard to classify carcinomas, increased peritumoral or tumor infiltrating lymphocytes and cases with synchronous ovarian carcinomas); (3) clinician's request based on family or personal history; and (4) ad hoc retrospective testing based on the established criteria on patients discovered on follow-up visits. By using a targeted screening approach in a 4.5-year period, approximately 2.1% of endometrial cancers (7 of 328) were potentially associated with Lynch syndrome. Therefore, targeted screening with combined age and morphology based criteria enriches detection of Lynch syndrome in endometrial cancer. However, the detection rate is lower than the rates from published series that offer universal screening. © The Author(s) 2016.

Using Public Data for Comparative Proteome Analysis in Precision Medicine Programs.

PubMed

Hughes, Christopher S; Morin, Gregg B

2018-03-01

Maximizing the clinical utility of information obtained in longitudinal precision medicine programs would benefit from robust comparative analyses to known information to assess biological features of patient material toward identifying the underlying features driving their disease phenotype. Herein, the potential for utilizing publically deposited mass-spectrometry-based proteomics data to perform inter-study comparisons of cell-line or tumor-tissue materials is investigated. To investigate the robustness of comparison between MS-based proteomics studies carried out with different methodologies, deposited data representative of label-free (MS1) and isobaric tagging (MS2 and MS3 quantification) are utilized. In-depth quantitative proteomics data acquired from analysis of ovarian cancer cell lines revealed the robust recapitulation of observable gene expression dynamics between individual studies carried out using significantly different methodologies. The observed signatures enable robust inter-study clustering of cell line samples. In addition, the ability to classify and cluster tumor samples based on observed gene expression trends when using a single patient sample is established. With this analysis, relevant gene expression dynamics are obtained from a single patient tumor, in the context of a precision medicine analysis, by leveraging a large cohort of repository data as a comparator. Together, these data establish the potential for state-of-the-art MS-based proteomics data to serve as resources for robust comparative analyses in precision medicine applications. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Remote intracranial recurrence of IDH mutant gliomas is associated with TP53 mutations and an 8q gain

PubMed Central

Nakae, Shunsuke; Kato, Takema; Murayama, Kazuhiro; Sasaki, Hikaru; Abe, Masato; Kumon, Masanobu; Kumai, Tadashi; Yamashiro, Kei; Inamasu, Joji; Hasegawa, Mitsuhiro; Kurahashi, Hiroki; Hirose, Yuichi

2017-01-01

Most IDH mutant gliomas harbor either 1p/19q co-deletions or TP53 mutation; 1p/19q co-deleted tumors have significantly better prognoses than tumors harboring TP53 mutations. To investigate the clinical factors that contribute to differences in tumor progression of IDH mutant gliomas, we classified recurrent tumor patterns based on MRI and correlated these patterns with their genomic characterization. Accordingly, in IDH mutant gliomas (N = 66), 1p/19 co-deleted gliomas only recurred locally, whereas TP53 mutant gliomas recurred both locally and in remote intracranial regions. In addition, diffuse tensor imaging suggested that remote intracranial recurrence in the astrocytomas, IDH-mutant with TP53 mutations may occur along major fiber bundles. Remotely recurrent tumors resulted in a higher mortality and significantly harbored an 8q gain; astrocytomas with an 8q gain resulted in significantly shorter overall survival than those without an 8q gain. OncoScan® arrays and next-generation sequencing revealed specific 8q regions (i.e., between 8q22 and 8q24) show a high copy number. In conclusion, only tumors with TP53 mutations showed patterns of remote recurrence in IDH mutant gliomas. Furthermore, an 8q gain was significantly associated with remote intracranial recurrence and can be considered a poor prognostic factor in astrocytomas, IDH-mutant. PMID:29156679

Spreaders and Sponges define metastasis in lung cancer: A Markov chain Monte Carlo Mathematical Model

PubMed Central

Newton, Paul K.; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Norton, Larry; Kuhn, Peter

2013-01-01

The classic view of metastatic cancer progression is that it is a unidirectional process initiated at the primary tumor site, progressing to variably distant metastatic sites in a fairly predictable, though not perfectly understood, fashion. A Markov chain Monte Carlo mathematical approach can determine a pathway diagram that classifies metastatic tumors as ‘spreaders’ or ‘sponges’ and orders the timescales of progression from site to site. In light of recent experimental evidence highlighting the potential significance of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large autopsy data sets, to quantify the stochastic, systemic, and often multi-directional aspects of cancer progression. We quantify three types of multi-directional mechanisms of progression: (i) self-seeding of the primary tumor; (ii) re-seeding of the primary tumor from a metastatic site (primary re-seeding); and (iii) re-seeding of metastatic tumors (metastasis re-seeding). The model shows that the combined characteristics of the primary and the first metastatic site to which it spreads largely determine the future pathways and timescales of systemic disease. For lung cancer, the main ‘spreaders’ of systemic disease are the adrenal gland and kidney, whereas the main ‘sponges’ are regional lymph nodes, liver, and bone. Lung is a significant self-seeder, although it is a ‘sponge’ site with respect to progression characteristics. PMID:23447576

The incidence rate and mortality of malignant brain tumors after 10 years of intensive cell phone use in Taiwan.

PubMed

Hsu, Min-Huei; Syed-Abdul, Shabbir; Scholl, Jeremiah; Jian, Wen-Shan; Lee, Peisan; Iqbal, Usman; Li, Yu-Chuan

2013-11-01

The issue of whether cell phone usage can contribute toward the development of brain tumors has recently been reignited with the International Agency for Research on Cancer classifying radiofrequency electromagnetic fields as 'possibly' carcinogenic to humans in a WHO report. To our knowledge, this is the largest study reporting on the incidence and mortality of malignant brain tumors after long-term use of the cell phone by more than 23 million users. A population-based study was carried out the numbers of cell phone users were collected from the official statistics provided by the National Communication Commission. According to National Cancer Registry, there were 4 incidences and 4 deaths due to malignant neoplasms in Taiwan during the period 2000-2009. The 10 years of observational data show that the intensive user rate of cell phones has had no significant effect on the incidence rate or on the mortality of malignant brain tumors in Taiwan. In conclusion, we do not detect any correlation between the morbidity/mortality of malignant brain tumors and cell phone use in Taiwan. We thus urge international agencies to publish only confirmatory reports with more applicable conclusions in public. This will help spare the public from unnecessary worries.

Morphological and immunohistochemical diversity of endometrial stromal sarcoma in rats.

PubMed

Kumabe, Shino; Sato, Junko; Tomonari, Yuki; Takahashi, Miwa; Inoue, Kaoru; Yoshida, Midori; Doi, Takuya; Wako, Yumi; Tsuchitani, Minoru

2018-04-01

To clarify the histopathological characteristics of rat endometrial stromal sarcoma (ESS), we morphologically reviewed 12 malignant uterine tumors protruding into the lumen in previous rat carcinogenicity studies. The 12 cases were classified into the following 6 types based on their morphological features: spindle cell and collagen rich type, pleomorphic/spindle cell and compact type, decidual alteration type, histiocytic and multinucleated giant cell mixture type, Antoni A-type schwannoma type, and Antoni B-type schwannoma type. Immunohistochemically, tumor cells in all cases exhibited focal or diffuse positive reactions for vimentin, and 11 of the 12 cases were positive for S-100. Interestingly, 9 cases were positive for desmin or αSMA, indicating tumor cells expressing smooth muscle properties. Both Antoni A- and B-type schwannoma types showed low reactions for both muscle markers. Positive results for estrogen receptor α in the 11 cases suggested that they were derived from endometrial stromal cells. On the basis of their immunohistochemical profiles, they were considered to be derived from endometrial stromal cells while they showed morphological variation. The detection of a basement membrane surrounding tumor cells might not be a definitive indicator for differential diagnosis of ESS from malignant schwannoma. In conclusion, ESS could exhibit wide morphological and immunohistochemical variation including features of schwannoma or smooth muscle tumor.

Melanoma recognition framework based on expert definition of ABCD for dermoscopic images.

PubMed

Abbas, Qaisar; Emre Celebi, M; Garcia, Irene Fondón; Ahmad, Waqar

2013-02-01

Melanoma Recognition based on clinical ABCD rule is widely used for clinical diagnosis of pigmented skin lesions in dermoscopy images. However, the current computer-aided diagnostic (CAD) systems for classification between malignant and nevus lesions using the ABCD criteria are imperfect due to use of ineffective computerized techniques. In this study, a novel melanoma recognition system (MRS) is presented by focusing more on extracting features from the lesions using ABCD criteria. The complete MRS system consists of the following six major steps: transformation to the CIEL*a*b* color space, preprocessing to enhance the tumor region, black-frame and hair artifacts removal, tumor-area segmentation, quantification of feature using ABCD criteria and normalization, and finally feature selection and classification. The MRS system for melanoma-nevus lesions is tested on a total of 120 dermoscopic images. To test the performance of the MRS diagnostic classifier, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed classifier achieved a sensitivity of 88.2%, specificity of 91.3%, and AUC of 0.880. The experimental results show that the proposed MRS system can accurately distinguish between malignant and benign lesions. The MRS technique is fully automatic and can easily integrate to an existing CAD system. To increase the classification accuracy of MRS, the CASH pattern recognition technique, visual inspection of dermatologist, contextual information from the patients, and the histopathological tests can be included to investigate the impact with this system. © 2012 John Wiley & Sons A/S.

Stromal-Based Signatures for the Classification of Gastric Cancer.

PubMed

Uhlik, Mark T; Liu, Jiangang; Falcon, Beverly L; Iyer, Seema; Stewart, Julie; Celikkaya, Hilal; O'Mahony, Marguerita; Sevinsky, Christopher; Lowes, Christina; Douglass, Larry; Jeffries, Cynthia; Bodenmiller, Diane; Chintharlapalli, Sudhakar; Fischl, Anthony; Gerald, Damien; Xue, Qi; Lee, Jee-Yun; Santamaria-Pang, Alberto; Al-Kofahi, Yousef; Sui, Yunxia; Desai, Keyur; Doman, Thompson; Aggarwal, Amit; Carter, Julia H; Pytowski, Bronislaw; Jaminet, Shou-Ching; Ginty, Fiona; Nasir, Aejaz; Nagy, Janice A; Dvorak, Harold F; Benjamin, Laura E

2016-05-01

Treatment of metastatic gastric cancer typically involves chemotherapy and monoclonal antibodies targeting HER2 (ERBB2) and VEGFR2 (KDR). However, reliable methods to identify patients who would benefit most from a combination of treatment modalities targeting the tumor stroma, including new immunotherapy approaches, are still lacking. Therefore, we integrated a mouse model of stromal activation and gastric cancer genomic information to identify gene expression signatures that may inform treatment strategies. We generated a mouse model in which VEGF-A is expressed via adenovirus, enabling a stromal response marked by immune infiltration and angiogenesis at the injection site, and identified distinct stromal gene expression signatures. With these data, we designed multiplexed IHC assays that were applied to human primary gastric tumors and classified each tumor to a dominant stromal phenotype representative of the vascular and immune diversity found in gastric cancer. We also refined the stromal gene signatures and explored their relation to the dominant patient phenotypes identified by recent large-scale studies of gastric cancer genomics (The Cancer Genome Atlas and Asian Cancer Research Group), revealing four distinct stromal phenotypes. Collectively, these findings suggest that a genomics-based systems approach focused on the tumor stroma can be used to discover putative predictive biomarkers of treatment response, especially to antiangiogenesis agents and immunotherapy, thus offering an opportunity to improve patient stratification. Cancer Res; 76(9); 2573-86. ©2016 AACR. ©2016 American Association for Cancer Research.

Rethinking the Stalk Effect: A New Hypothesis Explaining Suprasellar Tumor-Induced Hyperprolactinemia

PubMed Central

Skinner, Donal C.

2009-01-01

The pars tuberalis is a distinct subdivision of the pituitary gland but its function remains poorly understood. Suprasellar tumors in this pars tuberalis region are frequently accompanied by hyperprolactinemia. As these tumors do not immunoreact for any of the established pituitary hormones, they are classified as non-secretory. It has been postulated that these suprasellar tumors induce hyperprolactinemia by compressing the pituitary stalk, resulting in impaired dopamine delivery to the pituitary and, consequently, disinhibition of the lactotropes. An alternative hypothesis proposed is that suprasellar tumors secrete a specific pars tuberalis factor that stimulates prolactin secretion. Hypothesized candidates are the preprotachykinin A derived tachykinins, substance P and/or neurokinin A. PMID:19028420

Hepatoblastoma and Abernethy Malformation Type I: Case Report.

PubMed

Correa, Catalina; Luengas, Juan P; Howard, Scott C; Veintemilla, Galo

2017-03-01

A 2-year-old boy presented with pneumonia and an abdominal mass was noted incidentally. A right lobe hepatic mass classified as PRETEXT III and congenital absence of the portal vein with drainage of the superior mesenteric vein to the inferior vena cava (Abernethy malformation type I) were confirmed by computed tomography and angiography. After a clinical diagnosis of hepatoblastoma had been made, he was treated with 4 cycles of doxorubicin and cisplatin and hepatic arterial chemoembolization with doxorubicin, after which the tumor was classified as POSTEXT III. He underwent a right extended hepatic lobectomy with resection of the caudate lobe but died on postoperative day 4 due to hepatic failure. The Abernethy malformation type I is associated with the development of hepatic tumors, and the abnormal blood flow might predispose to hepatic failure after liver resection. Extensive study of the hepatic vasculature is warranted in patients with suspected malformations. Liver transplant could be considered in patients with congenital portosystemic shunt and malignant liver tumors.

Invasive endocervical adenocarcinoma: proposal for a new pattern-based classification system with significant clinical implications: a multi-institutional study.

PubMed

Diaz De Vivar, Andrea; Roma, Andres A; Park, Kay J; Alvarado-Cabrero, Isabel; Rasty, Golnar; Chanona-Vilchis, Jose G; Mikami, Yoshiki; Hong, Sung R; Arville, Brent; Teramoto, Norihiro; Ali-Fehmi, Rouba; Rutgers, Joanne K L; Tabassum, Farah; Barbuto, Denise; Aguilera-Barrantes, Irene; Shaye-Brown, Alexandra; Daya, Dean; Silva, Elvio G

2013-11-01

The management of endocervical adenocarcinoma is largely based on tumor size and depth of invasion (DOI); however, DOI is difficult to measure accurately. The surgical treatment includes resection of regional lymph nodes, even though most lymph nodes are negative and lymphadenectomies can cause significant morbidity. We have investigated alternative parameters to better identify patients at risk of node metastases. Cases of invasive endocervical adenocarcinoma from 12 institutions were reviewed, and clinical/pathologic features assessed: patients' age, tumor size, DOI, differentiation, lymph-vascular invasion, lymph node metastases, recurrences, and stage. Cases were classified according to a new pattern-based system into Pattern A (well-demarcated glands), B (early destructive stromal invasion arising from well-demarcated glands), and C (diffuse destructive invasion). In total, 352 cases (FIGO Stages I-IV) were identified. Patients' age ranged from 20 to 83 years (mean 45), DOI ranged from 0.2 to 27 mm (mean 6.73), and lymph-vascular invasion was present in 141 cases. Forty-nine (13.9%) demonstrated lymph node metastases. Using this new system, 73 patients (20.7%) with Pattern A tumors (all Stage I) were identified. None had lymph node metastases and/or recurrences. Ninety patients (25.6%) had Pattern B tumors, of which 4 (4.4%) had positive nodes; whereas 189 (53.7%) had Pattern C tumors, of which 45 (23.8%) had metastatic nodes. The proposed classification system can spare 20.7% of patients (Pattern A) of unnecessary lymphadenectomy. Patients with Pattern B rarely present with positive nodes. An aggressive approach is justified in patients with Pattern C. This classification system is simple, easy to apply, and clinically significant.

Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development

PubMed Central

Sertel, O.; Kong, J.; Shimada, H.; Catalyurek, U.V.; Saltz, J.H.; Gurcan, M.N.

2009-01-01

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%. PMID:20161324

Correlation of histological grade of dedifferentiation with clinical outcome in 55 patients with dedifferentiated liposarcomas.

PubMed

Dantey, Kossivi; Schoedel, Karen; Yergiyev, Oleksandr; Bartlett, David; Rao, Uma N M

2017-08-01

In this study the histologic grade of dedifferentiated liposarcomas was correlated with outcome in surgically resected specimens in 55 patients over a 19-year period at the University of Pittsburgh Medical Center. The tumors were located in the retroperitoneum (N=35); the extremities and thigh (N=16), and the remainder involved the spermatic cord and head and neck. Most tumors were large (mean=21 cm.) Follow-up was available in all 55 patients (median=36 months). Forty-one tumors classified as high-grade dedifferentiated liposarcoma (HG-DDLPS) had mitotically active pleomorphic and spindle cells and foci of necrosis. They included tumors with foci of smooth muscle differentiation (N=12), osteosarcoma (N=4), and myxoid areas (N=9). Fourteen tumors classified as low-grade dedifferentiated liposarcoma (LG-DDLPS) displayed a predominantly bland, monomorphic, spindle cell population with few mitoses and scant necrosis. The Kaplan-Meier method and log-rank test were used for statistical analysis. All tumors had unequivocal foci of well-differentiated liposarcoma (WDLPS). Fluorescence in situ hybridization (FISH) detected amplification of MDM2 in 29 cases. Twenty of 41 patients (49%) with HG-DDLPS died of tumor, and two patients died with LG-DDLPS (14%). The overall survival of patients with LG-DDLPS was significantly longer (P=.02). The median survival was 113 months for the LG-DDLPS and 48 months for the HG-DDLPS. Metastases (N=4) occurred only in the high-grade tumors and were independent of the type of heterologous differentiation. Patients with HG-DDLPS were at a greater risk of earlier death. Distinction between the two groups is important for patient selection for possible adjuvant therapy. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting Outcome and Therapy Response in mCRC Patients Using an Indirect Method for CTCs Detection by a Multigene Expression Panel: A Multicentric Prospective Validation Study

PubMed Central

Vidal Insua, Yolanda; De La Cámara, Juan; Brozos Vázquez, Elena; Fernández, Ana; Vázquez Rivera, Francisca; Villanueva Silva, Mª José; Barbazán, Jorge; Muinelo-Romay, Laura; Candamio Folgar, Sonia; Abalo, Alicia; López-López, Rafael; Abal, Miguel; Alonso-Alconada, Lorena

2017-01-01

Colorectal cancer (CRC) is one of the major causes of cancer-related deaths. Early detection of tumor relapse is crucial for determining the most appropriate therapeutic management. In clinical practice, computed tomography (CT) is routinely used, but small tumor changes are difficult to visualize, and reliable blood-based prognostic and monitoring biomarkers are urgently needed. The aim of this study was to prospectively validate a gene expression panel (composed of GAPDH, VIL1, CLU, TIMP1, TLN1, LOXL3 and ZEB2) for detecting circulating tumor cells (CTCs) as prognostic and predictive tool in blood samples from 94 metastatic CRC (mCRC) patients. Patients with higher gene panel expression before treatment had a reduced progression-free survival (PFS) and overall-survival (OS) rates compared with patients with low expression (p = 0.003 and p ≤ 0.001, respectively). Patients with increased expression of CTCs markers during treatment presented PFS and OS times of 8.95 and 11.74 months, respectively, compared with 14.41 and 24.7 for patients presenting decreased expression (PFS; p = 0.020; OS; p ≤ 0.001). Patients classified as non-responders by CTCs with treatment, but classified as responders by CT scan, showed significantly shorter survival times (PFS: 8.53 vs. 11.70; OS: 10.37 vs. 24.13; months). In conclusion, our CTCs detection panel demonstrated efficacy for early treatment response assessment in mCRC patients, and with increased reliability compared to CT scan. PMID:28608814

Evaluation of chemotherapy response in ovarian cancer treatment using quantitative CT image biomarkers: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Tan, Maxine; McMeekin, Scott; Thai, Theresa; Moore, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to identify and apply quantitative image biomarkers for early prediction of the tumor response to the chemotherapy among the ovarian cancer patients participated in the clinical trials of testing new drugs. In the experiment, we retrospectively selected 30 cases from the patients who participated in Phase I clinical trials of new drug or drug agents for ovarian cancer treatment. Each case is composed of two sets of CT images acquired pre- and post-treatment (4-6 weeks after starting treatment). A computer-aided detection (CAD) scheme was developed to extract and analyze the quantitative image features of the metastatic tumors previously tracked by the radiologists using the standard Response Evaluation Criteria in Solid Tumors (RECIST) guideline. The CAD scheme first segmented 3-D tumor volumes from the background using a hybrid tumor segmentation scheme. Then, for each segmented tumor, CAD computed three quantitative image features including the change of tumor volume, tumor CT number (density) and density variance. The feature changes were calculated between the matched tumors tracked on the CT images acquired pre- and post-treatments. Finally, CAD predicted patient's 6-month progression-free survival (PFS) using a decision-tree based classifier. The performance of the CAD scheme was compared with the RECIST category. The result shows that the CAD scheme achieved a prediction accuracy of 76.7% (23/30 cases) with a Kappa coefficient of 0.493, which is significantly higher than the performance of RECIST prediction with a prediction accuracy and Kappa coefficient of 60% (17/30) and 0.062, respectively. This study demonstrated the feasibility of analyzing quantitative image features to improve the early predicting accuracy of the tumor response to the new testing drugs or therapeutic methods for the ovarian cancer patients.

Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of radiation therapy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Dong, Lixin; Kudrimoti, Mahesh; Irwin, Daniel; Chen, Li; Shang, Yu; Li, Xingzhe; Stevens, Scott D.; Shelton, Brent J.; Yu, Guoqiang

2016-03-01

Radiation therapy is a principal modality for head and neck cancers and its efficacy depends on tumor hemodynamics. Our laboratory developed a hybrid diffuse optical instrument allowing for simultaneous measurements of tumor blood flow and oxygenation. In this study, the clinically involved cervical lymph node was monitored by the hybrid instrument once a week over the treatment period of seven weeks. Based on treatment outcomes within one year, patients were classified into a complete response group (CR) and an incomplete response group (IR) with remote metastasis and/or local recurrence. A linear mixed models was used to compare tumor hemodynamic responses to the treatment between the two groups. Interestingly, we found that human papilloma virus (HPV-16) status largely affected tumor hemodynamic responses. For HPV-16 negative tumors, significant differences in blood flow index (BFI, p = 0.007) and reduced scattering coefficient (μs', p = 0.0005) were observed between the two groups; IR tumors exhibited higher μs' values and a continuous increase in BFI over the treatment period. For HPV-16 positive tumors, oxygenated hemoglobin concentration ([HbO2]) and blood oxygen saturation (StO2) were significant different (p = 0.003 and 0.01, respectively); IR group showed lower [HbO2] and StO2. Our results imply HPV-16 negative tumors with higher density of vasculature (μs') and higher blood flow show poor responses to radiotherapy and HPV-16 positive tumors with lower tissue oxygenation level (lower StO2 and [HbO2]) exhibit poor treatment outcomes. Our diffuse optical measurements show the great potential for early prediction of radiotherapy in head and neck cancers.

Collagen type IV alpha 1 (COL4A1) and collagen type XIII alpha 1 (COL13A1) produced in cancer cells promote tumor budding at the invasion front in human urothelial carcinoma of the bladder

PubMed Central

Miyake, Makito; Hori, Shunta; Morizawa, Yosuke; Tatsumi, Yoshihiro; Toritsuka, Michihiro; Ohnishi, Sayuri; Shimada, Keiji; Furuya, Hideki; Khadka, Vedbar S.; Deng, Youping; Ohnishi, Kenta; Iida, Kota; Gotoh, Daisuke; Nakai, Yasushi; Inoue, Takeshi; Anai, Satoshi; Torimoto, Kazumasa; Aoki, Katsuya; Tanaka, Nobumichi; Konishi, Noboru; Fujimoto, Kiyohide

2017-01-01

Current knowledge of the molecular mechanism driving tumor budding is limited. Here, we focused on elucidating the detailed mechanism underlying tumor budding in urothelial cancer of the bladder. Invasive urothelial cancer was pathologically classified into three groups as follows: nodular, trabecular, and infiltrative (tumor budding). Pathohistological analysis of the orthotopic tumor model revealed that human urothelial cancer cell lines MGH-U3, UM-UC-14, and UM-UC-3 displayed typical nodular, trabecular, and infiltrative patterns, respectively. Based on the results of comprehensive gene expression analysis using microarray (25 K Human Oligo chip), we identified two collagens, COL4A1 and COL13A1, which may contribute to the formation of the infiltrative pattern. Visualization of protein interaction networks revealed that proteins associated with connective tissue disorders, epithelial-mesenchymal transition, growth hormone, and estrogen were pivotal factors in tumor cells. To evaluate the invasion pattern of tumor cells in vitro, 3-D collective cell invasion assay using Matrigel was performed. Invadopodial formation was evaluated using Gelatin Invadopodia Assay. Knockdown of collagens with siRNA led to dramatic changes in invasion patterns and a decrease in invasion capability through decreased invadopodia. The in vivo orthotopic experimental model of bladder tumors showed that intravesical treatment with siRNA targeting COL4A1 and COL13A1 inhibited the formation of the infiltrative pattern. COL4A1 and COL13A1 production by cancer cells plays a pivotal role in tumor invasion through the induction of tumor budding. Blocking of these collagens may be an attractive therapeutic approach for treatment of human urothelial cancer of the bladder. PMID:28415608

Identification of immunophenotypic subtypes with different prognoses in extranodal natural killer/T-cell lymphoma, nasal type.

PubMed

Yu, Jian-Bo; Zuo, Zhuo; Zhang, Wen-Yan; Yang, Qun-Pei; Zhang, Ying-Chun; Tang, Yuan; Zhao, Sha; Mo, Xian-Ming; Liu, Wei-Ping

2014-11-01

To analyze the differentiation characteristics of extranodal natural killer/T-cell lymphoma, nasal type, one nude mouse model, cell lines SNK6 and SNT8, and 16 fresh human samples were analyzed by flow cytometry immunophenotyping and immunohistochemistry staining; and 115 archived cases were used for phenotypic detection and prognostic analysis. We found that CD25 was expressed by most tumor cells in all samples, and CD56(+)CD25(+) cells were the predominant population in the mouse model, the 2 cell lines, and 10 of the 16 fresh tumor samples; in the other 6 fresh tumor samples, the predominant cell population was of the CD16(+)CD25(+) phenotype, and only a minor population showed the CD56(+)CD25(+) phenotype. The phenotype detected by immunohistochemistry staining generally was consistent with the phenotype found by flow cytometry immunophenotyping. According to the expression of CD56 and CD16, 115 cases could be classified into 3 phenotypic subtypes: CD56(-)CD16(-), CD56(+)CD16(-), and CD56(dim/-)CD16(+). Patients with tumors of the CD56(dim/-)CD16(+) phenotype had a poorer prognosis than patients with tumors of the other phenotypes. Differentiation of extranodal natural killer/T-cell lymphoma, nasal type apparently resembles the normal natural killer cell developmental pattern, and these tumors can be classified into 3 phenotypic subtypes of different aggressiveness. Expression of CD56(dim/-)CD16(+) implies a poorer prognosis. Copyright © 2014 Elsevier Inc. All rights reserved.

A consensus prognostic gene expression classifier for ER positive breast cancer

PubMed Central

Teschendorff, Andrew E; Naderi, Ali; Barbosa-Morais, Nuno L; Pinder, Sarah E; Ellis, Ian O; Aparicio, Sam; Brenton, James D; Caldas, Carlos

2006-01-01

Background A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. Results Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. Conclusion The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors. PMID:17076897

Location of subventricular zone recurrence and its radiation dose predicts survival in patients with glioblastoma.

PubMed

Weinberg, Brent D; Boreta, Lauren; Braunstein, Steve; Cha, Soonmee

2018-07-01

Glioblastomas are aggressive brain tumors that frequently recur in the subventricular zone (SVZ) despite maximal treatment. The purpose of this study was to evaluate imaging patterns of subventricular progression and impact of recurrent subventricular tumor involvement and radiation dose to patient outcome. Retrospective review of 50 patients diagnosed with glioblastoma and treated with surgery, radiation, and concurrent temozolomide from January 2012 to June 2013 was performed. Tumors were classified based on location, size, and cortical and subventricular zone involvement. Survival was compared based on recurrence type, distance from the initial enhancing tumor (local ≤ 2 cm, distant > 2 cm), and the radiation dose at the recurrence site. Progression of enhancing subventricular tumor was common at both local (58%) and distant (42%) sites. Median survival was better after local SVZ recurrence than distant SVZ recurrence (8.7 vs. 4.3 months, p = 0.04). Radiation doses at local SVZ recurrence sites recurrence averaged 57.0 ± 4.0 Gy compared to 44.7 ± 6.7 Gy at distant SVZ recurrence sites (p = 0.008). Distant subventricular progression at a site receiving ≤ 45 Gy predicted worse subsequent survival (p = 0.05). Glioblastomas frequently recurred in the subventricular zone, and patient survival was worse when enhancing tumor occurred at sites that received lower radiation doses. This recurrent disease may represent disease undertreated at the time of diagnosis, and further study is needed to determine if improved treatment strategies, such as including the subventricular zone in radiation fields, could improve clinical outcomes.

Similar Squamous Cell Carcinoma Epithelium microRNA Expression in Never Smokers and Ever Smokers

PubMed Central

Kolokythas, Antonia; Zhou, Yalu; Schwartz, Joel L.; Adami, Guy R.

2015-01-01

The incidence of oral tumors in patients who never used mutagenic agents such as tobacco is increasing. In an effort to better understand these tumors we studied microRNA (miRNA) expression in tumor epithelium of never tobacco users, tumor epithelium of ever tobacco users, and nonpathological control oral epithelium. A comparison of levels among 372 miRNAs in 12 never tobacco users with oral squamous cell carcinoma (OSCC) versus 10 healthy controls was made using the reverse transcription quantitative polymerase chain reaction. A similar analysis was done with 8 ever tobacco users with OSCC. These comparisons revealed miR-10b-5p, miR-196a-5p, and miR-31-5p as enriched in the tumor epithelium in OSCC of both never and ever tobacco users. Examination of The Cancer Genome Atlas (TCGA) project miRNA data on 305 OSCCs and 30 controls revealed 100% of those miRNAs enriched in never smoker OSCCs in this patient group were also enriched in ever smoker OSCCs. Nonsupervised clustering of TCGA OSCCs was suggestive of two or four subgroups of tumors based on miRNA levels with limited evidence for differences in tobacco exposure among the groups. Results from both patient groups together stress the importance of miR196a-5p in OSCC malignancy in both never and ever smokers, and emphasize the overall similarity of miRNA expression in OSCCs in these two risk groups. It implies that there may be great similarity in etiology of OSCC in never and ever smokers and that classifying OSCC based on tobacco exposure may not be helpful in the clinic. PMID:26544609

Towards the development of a spring-based continuum robot for neurosurgery

NASA Astrophysics Data System (ADS)

Kim, Yeongjin; Cheng, Shing Shin; Desai, Jaydev P.

2015-03-01

Brain tumor is usually life threatening due to the uncontrolled growth of abnormal cells native to the brain or the spread of tumor cells from outside the central nervous system to the brain. The risks involved in carrying out surgery within such a complex organ can cause severe anxiety in cancer patients. However, neurosurgery, which remains one of the more effective ways of treating brain tumors focused in a confined volume, can have a tremendously increased success rate if the appropriate imaging modality is used for complete tumor removal. Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and is the imaging modality of choice for brain tumor imaging. MRI combined with continuum soft robotics has immense potential to be the revolutionary treatment technique in the field of brain cancer. It eliminates the concern of hand tremor and guarantees a more precise procedure. One of the prototypes of Minimally Invasive Neurosurgical Intracranial Robot (MINIR-II), which can be classified as a continuum soft robot, consists of a snake-like body made of three segments of rapid prototyped plastic springs. It provides improved dexterity with higher degrees of freedom and independent joint control. It is MRI-compatible, allowing surgeons to track and determine the real-time location of the robot relative to the brain tumor target. The robot was manufactured in a single piece using rapid prototyping technology at a low cost, allowing it to disposable after each use. MINIR-II has two DOFs at each segment with both joints controlled by two pairs of MRI-compatible SMA spring actuators. Preliminary motion tests have been carried out using vision-tracking method and the robot was able to move to different positions based on user commands.

Expression of AID, P53, and Mlh1 proteins in endoscopically resected differentiated-type early gastric cancer

PubMed Central

Takeda, Yohei; Yashima, Kazuo; Hayashi, Akihiro; Sasaki, Shuji; Kawaguchi, Koichiro; Harada, Kenichi; Murawaki, Yoshikazu; Ito, Hisao

2012-01-01

AIM: To analyze the expression of the tumor-related proteins in differentiated-type early gastric carcinoma (DEGC) samples. METHODS: Tumor specimens were obtained from 102 patients (75 males and 27 females) who had received an endoscopic tumor resection at Tottori University Hospital between 2007 and 2009. Ninety-one cancer samples corresponded to noninvasive or intramucosal carcinoma according to the Vienna classification system, and 11 samples were submucosal invasive carcinomas. All of the EGCs were histologically differentiated carcinomas. All patients were classified as having Helicobacter pylori (H. pylori) infections by endoscopic atrophic changes or by testing seropositive for H. pylori IgG. All of the samples were histopathologically classified as either tubular or papillary adenocarcinoma according to their structure. The immunohistochemical staining was performed in a blinded manner with respect to the clinical information. Two independent observers evaluated protein expression. All data were statistically analyzed then. RESULTS: The rates of aberrant activation-induced cytidine deaminase (AID) expression and P53 overexpression were both 34.3% in DEGCs. The expression of Mlh1 was lost in 18.6% of DEGCs. Aberrant AID expression was not significantly associated with P53 overexpression in DEGCs. However, AID expression was associated with the severity of mononuclear cell activity in the non-cancerous mucosa adjacent to the tumor (P = 0.064). The rate of P53 expression was significantly greater in flat or depressed tumors than in elevated tumors. The frequency of Mlh1 loss was significantly increased in distal tumors, elevated gross-type tumors, papillary histological-type tumors, and tumors with a severe degree of endoscopic atrophic gastritis (P < 0.05). CONCLUSION: Aberrant AID expression, P53 overexpression, and the loss of Mlh1 were all associated with clinicopathological features and gastric mucosal alterations in DEGCs. The aberrant expression of AID protein may partly contribute to the induction of nuclear P53 expression. PMID:22737274

Intrinsic Subtype and Therapeutic Response Among HER2-Positive Breast Tumors from the NCCTG (Alliance) N9831 Trial

PubMed Central

Perez, Edith A.; Ballman, Karla V.; Mashadi-Hossein, Afshin; Tenner, Kathleen S.; Kachergus, Jennifer M.; Norton, Nadine; Necela, Brian M.; Carr, Jennifer M.; Ferree, Sean; Perou, Charles M.; Baehner, Frederick; Cheang, Maggie Chon U.

2017-01-01

Background: Genomic data from human epidermal growth factor receptor 2–positive (HER2+) tumors were analyzed to assess the association between intrinsic subtype and clinical outcome in a large, well-annotated patient cohort. Methods: Samples from the NCCTG (Alliance) N9831 trial were analyzed using the Prosigna algorithm on the NanoString platform to define intrinsic subtype, risk of recurrence scores, and risk categories for 1392 HER2+ tumors. Subtypes were evaluated for recurrence-free survival (RFS) using Kaplan-Meier and Cox model analysis following adjuvant chemotherapy (n = 484) or chemotherapy plus trastuzumab (n = 908). All statistical tests were two-sided. Results: Patients with HER2+ tumors from N9831 were primarily scored as HER2-enriched (72.1%). These individuals received statistically significant benefit from trastuzumab (hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.52 to 0.89, P = .005), as did the patients (291 of 1392) with luminal-type tumors (HR = 0.52, 95% CI = 0.32 to 0.85, P = .01). Patients with basal-like tumors (97 of 1392) did not have statistically significantly better RFS when treated with trastuzumab and chemotherapy compared with chemotherapy alone (HR = 1.06, 95% CI = 0.53 to 2.13, P = .87). Conclusions: The majority of clinically defined HER2-positive tumors were classified as HER2-enriched or luminal using the Prosigna algorithm. Intrinsic subtype alone cannot replace conventional histopathological evaluation of HER2 status because many tumors that are classified as luminal A or luminal B will benefit from adjuvant trastuzumab if that subtype is accompanied by HER2 overexpression. However, among tumors that overexpress HER2, we speculate that assessment of intrinsic subtype may influence treatment, particularly with respect to evaluating alternative therapeutic approaches for that subset of HER2-positive tumors of the basal-like subtype. PMID:27794124

Intrinsic Subtype and Therapeutic Response Among HER2-Positive Breaty st Tumors from the NCCTG (Alliance) N9831 Trial.

PubMed

Perez, Edith A; Ballman, Karla V; Mashadi-Hossein, Afshin; Tenner, Kathleen S; Kachergus, Jennifer M; Norton, Nadine; Necela, Brian M; Carr, Jennifer M; Ferree, Sean; Perou, Charles M; Baehner, Frederick; Cheang, Maggie Chon U; Thompson, E Aubrey

2017-02-01

Genomic data from human epidermal growth factor receptor 2-positive (HER2+) tumors were analyzed to assess the association between intrinsic subtype and clinical outcome in a large, well-annotated patient cohort. Samples from the NCCTG (Alliance) N9831 trial were analyzed using the Prosigna algorithm on the NanoString platform to define intrinsic subtype, risk of recurrence scores, and risk categories for 1392 HER2+ tumors. Subtypes were evaluated for recurrence-free survival (RFS) using Kaplan-Meier and Cox model analysis following adjuvant chemotherapy (n = 484) or chemotherapy plus trastuzumab (n = 908). All statistical tests were two-sided. Patients with HER2+ tumors from N9831 were primarily scored as HER2-enriched (72.1%). These individuals received statistically significant benefit from trastuzumab (hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.52 to 0.89, P = .005), as did the patients (291 of 1392) with luminal-type tumors (HR = 0.52, 95% CI = 0.32 to 0.85, P = .01). Patients with basal-like tumors (97 of 1392) did not have statistically significantly better RFS when treated with trastuzumab and chemotherapy compared with chemotherapy alone (HR = 1.06, 95% CI = 0.53 to 2.13, P = .87). The majority of clinically defined HER2-positive tumors were classified as HER2-enriched or luminal using the Prosigna algorithm. Intrinsic subtype alone cannot replace conventional histopathological evaluation of HER2 status because many tumors that are classified as luminal A or luminal B will benefit from adjuvant trastuzumab if that subtype is accompanied by HER2 overexpression. However, among tumors that overexpress HER2, we speculate that assessment of intrinsic subtype may influence treatment, particularly with respect to evaluating alternative therapeutic approaches for that subset of HER2-positive tumors of the basal-like subtype. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

[A graph cuts-based interactive method for segmentation of magnetic resonance images of meningioma].

PubMed

Li, Shuan-qiang; Feng, Qian-jin; Chen, Wu-fan; Lin, Ya-zhong

2011-06-01

For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.

Technical challenges in the isolation and analysis of circulating tumor cells.

PubMed

van der Toom, Emma E; Verdone, James E; Gorin, Michael A; Pienta, Kenneth J

2016-09-20

Increasing evidence suggests that cancer cells display dynamic molecular changes in response to systemic therapy. Circulating tumor cells (CTCs) in the peripheral blood represent a readily available source of cancer cells with which to measure this dynamic process. To date, a large number of strategies to isolate and characterize CTCs have been described. These techniques, however, each have unique limitations in their ability to sensitively and specifically detect these rare cells. In this review we focus on the technical limitations and pitfalls of the most common CTC isolation and detection strategies. Additionally, we emphasize the difficulties in correctly classifying rare cells as CTCs using common biomarkers. As for assays developed in the future, the first step must be a uniform and clear definition of the criteria for assigning an object as a CTC based on disease-specific biomarkers.

Differences in microRNA expression during tumor development in the transition and peripheral zones of the prostate

PubMed Central

2013-01-01

Background The prostate is divided into three glandular zones, the peripheral zone (PZ), the transition zone (TZ), and the central zone. Most prostate tumors arise in the peripheral zone (70-75%) and in the transition zone (20-25%) while only 10% arise in the central zone. The aim of this study was to investigate if differences in miRNA expression could be a possible explanation for the difference in propensity of tumors in the zones of the prostate. Methods Patients with prostate cancer were included in the study if they had a tumor with Gleason grade 3 in the PZ, the TZ, or both (n=16). Normal prostate tissue was collected from men undergoing cystoprostatectomy (n=20). The expression of 667 unique miRNAs was investigated using TaqMan low density arrays for miRNAs. Student’s t-test was used in order to identify differentially expressed miRNAs, followed by hierarchical clustering and principal component analysis (PCA) to study the separation of the tissues. The ADtree algorithm was used to identify markers for classification of tissues and a cross-validation procedure was used to test the generality of the identified miRNA-based classifiers. Results The t-tests revealed that the major differences in miRNA expression are found between normal and malignant tissues. Hierarchical clustering and PCA based on differentially expressed miRNAs between normal and malignant tissues showed perfect separation between samples, while the corresponding analyses based on differentially expressed miRNAs between the two zones showed several misplaced samples. A classification and cross-validation procedure confirmed these results and several potential miRNA markers were identified. Conclusions The results of this study indicate that the major differences in the transcription program are those arising during tumor development, rather than during normal tissue development. In addition, tumors arising in the TZ have more unique differentially expressed miRNAs compared to the PZ. The results also indicate that separate miRNA expression signatures for diagnosis might be needed for tumors arising in the different zones. MicroRNA signatures that are specific for PZ and TZ tumors could also lead to more accurate prognoses, since tumors arising in the PZ tend to be more aggressive than tumors arising in the TZ. PMID:23890084

Childhood malignant blue nevus of the ear associated with two intracranial melanocytic tumors-metastases or neurocutaneous melanosis?

PubMed

Popović, Mara; Dolenc-Strazar, Zvezdana; Anzic, Jozica; Luzar, Bostjan

2004-10-01

Blue nevus is an uncommon pigmented tumor of dermal melanocytes that has traditionally been classified into common and cellular variant. It is usually a skin tumor in adults but can become apparent in early childhood or even be present at birth. Malignant blue nevus is a rare melanocytic tumor of the skin arising from a preexisting cellular blue nevus. We report a multinodular blue nevus of the left ear in an 11-year-old girl who also had 2 intracranial melanocytic lesions. Differential diagnosis between metastases from malignant blue nevus and neurocutaneous melanosis is discussed.

International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma

PubMed Central

Travis, William D.; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G.; Geisinger, Kim R.; Yatabe, Yasushi; Beer, David G.; Powell, Charles A.; Riely, Gregory J.; Van Schil, Paul E.; Garg, Kavita; Austin, John H. M.; Asamura, Hisao; Rusch, Valerie W.; Hirsch, Fred R.; Scagliotti, Giorgio; Mitsudomi, Tetsuya; Huber, Rudolf M.; Ishikawa, Yuichi; Jett, James; Sanchez-Cespedes, Montserrat; Sculier, Jean-Paul; Takahashi, Takashi; Tsuboi, Masahiro; Vansteenkiste, Johan; Wistuba, Ignacio; Yang, Pan-Chyr; Aberle, Denise; Brambilla, Christian; Flieder, Douglas; Franklin, Wilbur; Gazdar, Adi; Gould, Michael; Hasleton, Philip; Henderson, Douglas; Johnson, Bruce; Johnson, David; Kerr, Keith; Kuriyama, Keiko; Lee, Jin Soo; Miller, Vincent A.; Petersen, Iver; Roggli, Victor; Rosell, Rafael; Saijo, Nagahiro; Thunnissen, Erik; Tsao, Ming; Yankelewitz, David

2015-01-01

Introduction Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies. Methods An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach. Results The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100% disease-specific survival, respectively. AIS and MIA are usually nonmucinous but rarely may be mucinous. Invasive adenocarcinomas are classified by predominant pattern after using comprehensive histologic subtyping with lepidic (formerly most mixed subtype tumors with nonmucinous BAC), acinar, papillary, and solid patterns; micropapillary is added as a new histologic subtype. Variants include invasive mucinous adenocarcinoma (formerly mucinous BAC), colloid, fetal, and enteric adenocarcinoma. This classification provides guidance for small biopsies and cytology specimens, as approximately 70% of lung cancers are diagnosed in such samples. Non-small cell lung carcinomas (NSCLCs), in patients with advanced-stage disease, are to be classified into more specific types such as adenocarcinoma or squamous cell carcinoma, whenever possible for several reasons: (1) adenocarcinoma or NSCLC not otherwise specified should be tested for epidermal growth factor receptor (EGFR) mutations as the presence of these mutations is predictive of responsiveness to EGFR tyrosine kinase inhibitors, (2) adenocarcinoma histology is a strong predictor for improved outcome with pemetrexed therapy compared with squamous cell carcinoma, and (3) potential life-threatening hemorrhage may occur in patients with squamous cell carcinoma who receive bevacizumab. If the tumor cannot be classified based on light microscopy alone, special studies such as immunohistochemistry and/or mucin stains should be applied to classify the tumor further. Use of the term NSCLC not otherwise specified should be minimized. Conclusions This new classification strategy is based on a multidisciplinary approach to diagnosis of lung adenocarcinoma that incorporates clinical, molecular, radiologic, and surgical issues, but it is primarily based on histology. This classification is intended to support clinical practice, and research investigation and clinical trials. As EGFR mutation is a validated predictive marker for response and progression-free survival with EGFR tyrosine kinase inhibitors in advanced lung adenocarcinoma, we recommend that patients with advanced adenocarcinomas be tested for EGFR mutation. This has implications for strategic management of tissue, particularly for small biopsies and cytology samples, to maximize high-quality tissue available for molecular studies. Potential impact for tumor, node, and metastasis staging include adjustment of the size T factor according to only the invasive component (1) pathologically in invasive tumors with lepidic areas or (2) radiologically by measuring the solid component of part-solid nodules. PMID:21252716

International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma.

PubMed

Travis, William D; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G; Geisinger, Kim R; Yatabe, Yasushi; Beer, David G; Powell, Charles A; Riely, Gregory J; Van Schil, Paul E; Garg, Kavita; Austin, John H M; Asamura, Hisao; Rusch, Valerie W; Hirsch, Fred R; Scagliotti, Giorgio; Mitsudomi, Tetsuya; Huber, Rudolf M; Ishikawa, Yuichi; Jett, James; Sanchez-Cespedes, Montserrat; Sculier, Jean-Paul; Takahashi, Takashi; Tsuboi, Masahiro; Vansteenkiste, Johan; Wistuba, Ignacio; Yang, Pan-Chyr; Aberle, Denise; Brambilla, Christian; Flieder, Douglas; Franklin, Wilbur; Gazdar, Adi; Gould, Michael; Hasleton, Philip; Henderson, Douglas; Johnson, Bruce; Johnson, David; Kerr, Keith; Kuriyama, Keiko; Lee, Jin Soo; Miller, Vincent A; Petersen, Iver; Roggli, Victor; Rosell, Rafael; Saijo, Nagahiro; Thunnissen, Erik; Tsao, Ming; Yankelewitz, David

2011-02-01

Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies. An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach. The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤ 5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100% disease-specific survival, respectively. AIS and MIA are usually nonmucinous but rarely may be mucinous. Invasive adenocarcinomas are classified by predominant pattern after using comprehensive histologic subtyping with lepidic (formerly most mixed subtype tumors with nonmucinous BAC), acinar, papillary, and solid patterns; micropapillary is added as a new histologic subtype. Variants include invasive mucinous adenocarcinoma (formerly mucinous BAC), colloid, fetal, and enteric adenocarcinoma. This classification provides guidance for small biopsies and cytology specimens, as approximately 70% of lung cancers are diagnosed in such samples. Non-small cell lung carcinomas (NSCLCs), in patients with advanced-stage disease, are to be classified into more specific types such as adenocarcinoma or squamous cell carcinoma, whenever possible for several reasons: (1) adenocarcinoma or NSCLC not otherwise specified should be tested for epidermal growth factor receptor (EGFR) mutations as the presence of these mutations is predictive of responsiveness to EGFR tyrosine kinase inhibitors, (2) adenocarcinoma histology is a strong predictor for improved outcome with pemetrexed therapy compared with squamous cell carcinoma, and (3) potential life-threatening hemorrhage may occur in patients with squamous cell carcinoma who receive bevacizumab. If the tumor cannot be classified based on light microscopy alone, special studies such as immunohistochemistry and/or mucin stains should be applied to classify the tumor further. Use of the term NSCLC not otherwise specified should be minimized. This new classification strategy is based on a multidisciplinary approach to diagnosis of lung adenocarcinoma that incorporates clinical, molecular, radiologic, and surgical issues, but it is primarily based on histology. This classification is intended to support clinical practice, and research investigation and clinical trials. As EGFR mutation is a validated predictive marker for response and progression-free survival with EGFR tyrosine kinase inhibitors in advanced lung adenocarcinoma, we recommend that patients with advanced adenocarcinomas be tested for EGFR mutation. This has implications for strategic management of tissue, particularly for small biopsies and cytology samples, to maximize high-quality tissue available for molecular studies. Potential impact for tumor, node, and metastasis staging include adjustment of the size T factor according to only the invasive component (1) pathologically in invasive tumors with lepidic areas or (2) radiologically by measuring the solid component of part-solid nodules.

Cystic pancreatic tumors (CPT): predictors of malignant behavior.

PubMed

Javle, Milind; Shah, Pankaj; Yu, Jihnhee; Bhagat, Vishal; Litwin, Alan; Iyer, Renuka; Gibbs, John

2007-03-01

Due to widespread use of imaging studies, increasing cystic pancreatic tumor (CPT) cases are being detected. The diagnosis of malignancy in CPT cases requires pancreatectomy. Clinical and laboratory characteristics of CPT may predict underlying malignancy. CPT cases treated between 1994 and 2004 at our institution were included. Pseudocysts were excluded. Serous cystadenoma (SCA), mucinous cystadenoma (MCA), intrapapillary mucinous tumor, cystic endocrine tumor, and lymphoepithelial cysts were classified as benign or pre-malignant. Serous cystadenocarcinoma (SCACA), mucinous cystadenocarcinoma (MCACA), and adenocarcinoma (ACA) were classified as malignant. Thirty-five patients had histological confirmation. Median age was 65 years. Male/female ratio was higher in malignant group (P = 0.0284). Weight loss and abdominal mass were more prevalent in malignant group (P = 0.042 and 0.028, respectively). Malignant lesions were larger, associated with local invasion (superior mesenteric artery (SMA), superior mesenteric vein (SMV), portal vein (PV) complex or celiac encasement) and CA 19-9 elevation. On univariate analyses, local invasion (P = 0.0029), negative surgical intervention (P = 0.0010), presence of ACA (P = 0.0044), or malignant CPT (P = 0.0018) were associated with shorter survival. On a multivariate analysis, local invasion was associated with shorter survival [Hazard ratio (HR) = 4.322, P = 0.0218], while surgical intervention was associated with improved survival (HR = 0.179, P = 0.0124). Male sex, abdominal mass, weight loss, larger tumor size, local invasion, and elevated CA 19-9 were associated with malignant CPT.

Multicenter study for optimal categorization of extramural tumor deposits for colorectal cancer staging.

PubMed

Ueno, Hideki; Mochizuki, Hidetaka; Shirouzu, Kazuo; Kusumi, Takaya; Yamada, Kazutaka; Ikegami, Masahiro; Kawachi, Hiroshi; Kameoka, Shingo; Ohkura, Yasuo; Masaki, Tadahiko; Kushima, Ryoji; Takahashi, Keiichi; Ajioka, Yoichi; Hase, Kazuo; Ochiai, Atsushi; Wada, Ryo; Iwaya, Keiichi; Nakamura, Takahiro; Sugihara, Kenichi

2012-04-01

This study aimed to determine the optimal categorization of extramural tumor deposits lacking residual lymph node (LN) structure (EX) in colorectal cancer staging. The TNM classification system categorizes EX on the basis of their contour characteristics (the contour rule). We conducted a multicenter, retrospective, pathological review of 1716 patients with stage I to III curatively resected colorectal cancer who were treated at 11 institutions (1994-1998). In addition, 2242 patients from 9 institutions (1999-2003) were enrolled as a second cohort for validating results. EX were classified as isolated foci confined to vascular or perineural spaces (ie, lymphatic, venous, or perineural invasion) or as tumor nodules (ND). N- and T-staging systems employing different categories for staging were compared in terms of their prognostic power. In addition, the diagnoses of extramural, discontinuously spreading lesions made by 11 observers from different institutions were assessed for interobserver agreement. EX were observed in 18.2% of patients in the first cohort. The method of categorization of EX in tumor staging has a stronger impact on N than T staging. The N-staging system in which all ND types were classified as N factor (the ND rule) could more effectively stratify the survival outcome than the contour rule (Akaike information criterion, 3040.8 vs 3059.5; the Harrell C-index, 0.7255 vs 0.7103). EX were observed in 16.9% of patients in the second cohort. Statistically, the ND rule was more informative than the contour rule for N staging. The Fleiss kappa coefficient for distinguishing LN metastases from EX (0.74) was lower than expected for complete agreement, and it decreased further to 0.51 when calculated for the judgment of ND with smooth contours. Classifying all ND types as N factors irrespective of contours can simplify the tumor staging system by enhancing diagnostic objectivity, resulting in improved prognostic accuracy.

DOE Office of Scientific and Technical Information (OSTI.GOV)

You, D; Aryal, M; Samuels, S

Purpose: A previous study showed that large sub-volumes of tumor with low blood volume (BV) (poorly perfused) in head-and-neck (HN) cancers are significantly associated with local-regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel-wise contrast-enhanced temporal features of dynamic contrastenhanced (DCE) MRI in HN cancers for predicting LRF. Methods: Our model development consists of training and testing stages. The training stage includes preprocessing of individual-voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, featuremore » selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast-enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel-wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub-volume classification. Voxel-wise classification accuracy was computed to evaluate performance of the model. Results: Average classification accuracies were 87.2% for training (10-fold crossvalidation) and 82.5% for testing. The lowest and highest accuracies (patient-wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub-volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub-volume boundaries. Conclusion: This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrastenhanced features of DCE-MRI in other tumors. We would like to acknowledge NIH for funding support: UO1 CA183848.« less

Sparse feature selection for classification and prediction of metastasis in endometrial cancer.

PubMed

Ahsen, Mehmet Eren; Boren, Todd P; Singh, Nitin K; Misganaw, Burook; Mutch, David G; Moore, Kathleen N; Backes, Floor J; McCourt, Carolyn K; Lea, Jayanthi S; Miller, David S; White, Michael A; Vidyasagar, Mathukumalli

2017-03-27

Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

Diagnosis of Lung Cancer in Small Biopsies and Cytology

PubMed Central

Travis, William D.; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G.; Geisinger, Kim; Yatabe, Yasushi; Ishikawa, Yuichi; Wistuba, Ignacio; Flieder, Douglas B.; Franklin, Wilbur; Gazdar, Adi; Hasleton, Philip S.; Henderson, Douglas W.; Kerr, Keith M.; Petersen, Iver; Roggli, Victor; Thunnissen, Erik; Tsao, Ming

2015-01-01

The new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification provides, for the first time, standardized terminology for lung cancer diagnosis in small biopsies and cytology; this was not primarily addressed by previous World Health Organization classifications. Until recently there have been no therapeutic implications to further classification of NSCLC, so little attention has been given to the distinction of adenocarcinoma and squamous cell carcinoma in small tissue samples. This situation has changed dramatically in recent years with the discovery of several therapeutic options that are available only to patients with adenocarcinoma or NSCLC, not otherwise specified, rather than squamous cell carcinoma. This includes recommendation for use of special stains as an aid to diagnosis, particularly in the setting of poorly differentiated tumors that do not show clear differentiation by routine light microscopy. A limited diagnostic workup is recommended to preserve as much tissue for molecular testing as possible. Most tumors can be classified using a single adenocarcinoma marker (eg, thyroid transcription factor 1 or mucin) and a single squamous marker (eg, p40 or p63). Carcinomas lacking clear differentiation by morphology and special stains are classified as NSCLC, not otherwise specified. Not otherwise specified carcinomas that stain with adenocarcinoma markers are classified as NSCLC, favor adenocarcinoma, and tumors that stain only with squamous markers are classified as NSCLC, favor squamous cell carcinoma. The need for every institution to develop a multidisciplinary tissue management strategy to obtain these small specimens and process them, not only for diagnosis but also for molecular testing and evaluation of markers of resistance to therapy, is emphasized. PMID:22970842

Comparative Expression Profiling of Distinct T Cell Subsets Undergoing Oxidative Stress

PubMed Central

Lichtenfels, Rudolf; Mougiakakos, Dimitrios; Johansson, C. Christian; Dressler, Sven P.; Recktenwald, Christian V.; Kiessling, Rolf; Seliger, Barbara

2012-01-01

The clinical outcome of adoptive T cell transfer-based immunotherapies is often limited due to different escape mechanisms established by tumors in order to evade the hosts' immune system. The establishment of an immunosuppressive micromilieu by tumor cells along with distinct subsets of tumor-infiltrating lymphocytes is often associated with oxidative stress that can affect antigen-specific memory/effector cytotoxic T cells thereby substantially reducing their frequency and functional activation. Therefore, protection of tumor-reactive cytotoxic T lymphocytes from oxidative stress may enhance the anti-tumor-directed immune response. In order to better define the key pathways/proteins involved in the response to oxidative stress a comparative 2-DE-based proteome analysis of naïve CD45RA+ and their memory/effector CD45RO+ T cell counterparts in the presence and absence of low dose hydrogen peroxide (H2O2) was performed in this pilot study. Based on the profiling data of these T cell subpopulations under the various conditions, a series of differentially expressed spots were defined, members thereof identified by mass spectrometry and subsequently classified according to their cellular function and localization. Representative targets responding to oxidative stress including proteins involved in signaling pathways, in regulating the cellular redox status as well as in shaping/maintaining the structural cell integrity were independently verified at the transcript and protein level under the same conditions in both T cell subsets. In conclusion the resulting profiling data describe complex, oxidative stress-induced, but not strictly concordant changes within the respective expression profiles of CD45RA+ and CD45RO+ T cells. Some of the differentially expressed genes/proteins might be further exploited as potential targets toward modulating the redox capacity of the distinct lymphocyte subsets thereby providing the basis for further studies aiming at rendering them more resistant to tumor micromilieu-induced oxidative stress. PMID:22911781

Orbital neoplasia in 23 dogs.

PubMed

Kern, T J

1985-03-01

Medical records of 23 dogs with histologically documented orbital neoplasia and admitted to the New York State College of Veterinary Medicine between 1975 and 1984 were reviewed. Almost all (91%) of the tumors were classified as malignant; 74% of the tumors arose as primary neoplasms within the orbit. Eleven tumor types of connective tissue, bone, epithelial, and hemolymphatic origin were represented. The typically afflicted dog was purebred, female, and middle-aged. Review of this series confirmed the clinical impression that orbital neoplasms in dogs are aggressive malignancies with poor long-term prognosis.

[Primary perivascular epitheloid cell tumour (PEComa) of the liver - is a new entity of the liver tumors?].

PubMed

Panahova, S; Rempp, H; Sipos, B; Malek, N P; Boozari, B

2015-05-01

Perivascular epitheloid cell tumor (PEComa) is a rare tumor, characterized by dual Expression of smooth muscle and melanocytic markers. Due to the development of diagnostic procedures, we now diagnose PEComa more often. We report about a case of PEComa of the liver as an accidental finding. We analyze the clinical and morphological characteristics of this tumor and compare it with the data of the literature. Management of patients with PEComa is not yet standardized; therefore biopsy with immunhistochemical staining is necessary for the diagnosis. In case of liver tumors which cannot be classified by their morphology on imaging modalities, it is important to think about this rare entity. © Georg Thieme Verlag KG Stuttgart · New York.

SU-F-303-05: DCE-MRI Before and During Treatment for Prediction of Concurrent Chemotherapy and Radiation Therapy Response in Head and Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Diwanji, T; Zhang, B

2015-06-15

Purpose: To determine the ability of pharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE- MRI) acquired before and during concurrent chemotherapy and radiation therapy to predict clinical response in patients with head and neck cancer. Methods: Eleven patients underwent a DCE-MRI scan at three time points: 1–2 weeks before treatment, 4–5 weeks after treatment initiation, and 3–4 months after treatment completion. Post-processing of MRI data included correction to reduce motion artifacts. The arterial input function was obtained by measuring the dynamic tracer concentration in the jugular veins. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), rate constant (Kep;more » Kep = Ktrans/ve), and plasma volume fraction (vp) were computed for primary tumors and cervical nodal masses. Patients were categorized into two groups based on response to therapy at 3–4 months: responders (no evidence of disease) and partial responders (regression of disease). Responses of the primary tumor and nodes were evaluated separately. A linear classifier and receiver operating characteristic curve analyses were used to determine the best model for discrimination of responders from partial responders. Results: When the above pharmacokinetic parameters of the primary tumor measured before and during treatment were incorporated into the linear classifier, a discriminative accuracy of 88.9%, with sensitivity =100% and specificity = 66.7%, was observed between responders (n=6) and partial responders (n=3) for the primary tumor with the corresponding accuracy = 44.4%, sensitivity = 66.7%, and specificity of 0% for nodal masses. When only pre-treatment parameters were used, the accuracy decreased to 66.7%, with sensitivity = 66.7% and specificity = 66.7% for the primary tumor and decreased to 33.3%, sensitivity of 50%, and specificity of 0% for nodal masses. Conclusion: Higher accuracy, sensitivity, and specificity were obtained using DCE-MRI-derived pharmacokinetic parameters acquired before and during treatment as compared with those derived from the pre-treatment time-point, exclusively.« less

Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.

PubMed

Huttunen, Mikko J; Hassan, Abdurahman; McCloskey, Curtis W; Fasih, Sijyl; Upham, Jeremy; Vanderhyden, Barbara C; Boyd, Robert W; Murugkar, Sangeeta

2018-06-01

Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

PubMed

Feng, Zhichao; Rong, Pengfei; Cao, Peng; Zhou, Qingyu; Zhu, Wenwei; Yan, Zhimin; Liu, Qianyun; Wang, Wei

2018-04-01

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Discovery and Validation of Novel Expression Signature for Postcystectomy Recurrence in High-Risk Bladder Cancer

PubMed Central

Lam, Lucia L.; Ghadessi, Mercedeh; Erho, Nicholas; Vergara, Ismael A.; Alshalalfa, Mohammed; Buerki, Christine; Haddad, Zaid; Sierocinski, Thomas; Triche, Timothy J.; Skinner, Eila C.; Davicioni, Elai; Daneshmand, Siamak; Black, Peter C.

2014-01-01

Background Nearly half of muscle-invasive bladder cancer patients succumb to their disease following cystectomy. Selecting candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. This study aimed to develop and validate genomic-based signatures that can better identify patients at risk for recurrence than clinical models alone. Methods Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival tumors from 225 patients who underwent radical cystectomy and had muscle-invasive and/or node-positive bladder cancer. Genomic (GC) and clinical (CC) classifiers for predicting recurrence were developed on a discovery set (n = 133). Performances of GC, CC, an independent clinical nomogram (IBCNC), and genomic-clinicopathologic classifiers (G-CC, G-IBCNC) were assessed in the discovery and independent validation (n = 66) sets. GC was further validated on four external datasets (n = 341). Discrimination and prognostic abilities of classifiers were compared using area under receiver-operating characteristic curves (AUCs). All statistical tests were two-sided. Results A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. This was higher than individual clinical variables, IBCNC (AUC = 0.73), and comparable to CC (AUC = 0.78). Performance was improved upon combining GC with clinical nomograms (G-IBCNC, AUC = 0.82; G-CC, AUC = 0.86). G-CC high-risk patients had elevated recurrence probabilities (P < .001), with GC being the best predictor by multivariable analysis (P = .005). Genomic-clinicopathologic classifiers outperformed clinical nomograms by decision curve and reclassification analyses. GC performed the best in validation compared with seven prior signatures. GC markers remained prognostic across four independent datasets. Conclusions The validated genomic-based classifiers outperform clinical models for predicting postcystectomy bladder cancer recurrence. This may be used to better identify patients who need more aggressive management. PMID:25344601

Expression and significance of Ki-67 in lung cancer.

PubMed

Folescu, Roxana; Levai, Codrina Mihaela; Grigoraş, Mirela Loredana; Arghirescu, Teodora Smaranda; Talpoş, Ioana Cristina; Gîndac, Ciprian Mihai; Zamfir, Carmen Lăcrămioara; Poroch, Vladimir; Anghel, Mirella Dorina

2018-01-01

Ki-67 parameter is a proliferation marker in malignant tumors. The increased proliferation activity and the decreased prognosis in lung cancer determined us to investigate different parameters connected to the tumor's aggression, such as cellularity, Ki-67 positivity rate, and proliferating cell nuclear antigen (PCNA). We evaluated the proliferative activity in 62 primary lung tumors by determining the cell's percentage of Ki-67 and immunoreactive PCNA (using MIB-1 and PCNA monoclonal antibodies), classifying Ki-67 and PCNA immunoreactivity into three score groups. The results obtained emphasized a linkage between Ki-67 score with the histological tumor subtype, tumor cellularity and degree of differentiation and with other proliferation immunohistochemistry (IHC) markers, such as p53 cellular tumor antigen. The tumor's cellularity, the Ki-67 positivity rate and PCNA, together with the clinical stage and the histological differentiation bring extra pieces of useful information in order to anticipate the evolution and the prognosis of lung cancer.

An Integrated Prognostic Classifier for Stage I Lung Adenocarcinoma based on mRNA, microRNA and DNA Methylation Biomarkers

PubMed Central

Robles, Ana I.; Arai, Eri; Mathé, Ewy A.; Okayama, Hirokazu; Schetter, Aaron J.; Brown, Derek; Petersen, David; Bowman, Elise D.; Noro, Rintaro; Welsh, Judith A.; Edelman, Daniel C.; Stevenson, Holly S.; Wang, Yonghong; Tsuchiya, Naoto; Kohno, Takashi; Skaug, Vidar; Mollerup, Steen; Haugen, Aage; Meltzer, Paul S.; Yokota, Jun; Kanai, Yae

2015-01-01

Introduction Up to 30% Stage I lung cancer patients suffer recurrence within 5 years of curative surgery. We sought to improve existing protein-coding gene and microRNA expression prognostic classifiers by incorporating epigenetic biomarkers. Methods Genome-wide screening of DNA methylation and pyrosequencing analysis of HOXA9 promoter methylation were performed in two independently collected cohorts of Stage I lung adenocarcinoma. The prognostic value of HOXA9 promoter methylation alone and in combination with mRNA and miRNA biomarkers was assessed by Cox regression and Kaplan-Meier survival analysis in both cohorts. Results Promoters of genes marked by Polycomb in Embryonic Stem Cells were methylated de novo in tumors and identified patients with poor prognosis. The HOXA9 locus was methylated de novo in Stage I tumors (P < 0.0005). High HOXA9 promoter methylation was associated with worse cancer-specific survival (Hazard Ratio [HR], 2.6; P = 0.02) and recurrence-free survival (HR, 3.0; P = 0.01), and identified high-risk patients in stratified analysis of Stage IA and IB. Four protein-coding gene (XPO1, BRCA1, HIF1α, DLC1), miR-21 expression and HOXA9 promoter methylation were each independently associated with outcome (HR, 2.8; P = 0.002; HR, 2.3; P = 0.01; and HR, 2.4; P = 0.005, respectively), and, when combined, identified high-risk, therapy naïve, Stage I patients (HR, 10.2; P = 3x10−5). All associations were confirmed in two independently collected cohorts. Conclusion A prognostic classifier comprising three types of genomic and epigenomic data may help guide the postoperative management of Stage I lung cancer patients at high risk of recurrence. PMID:26134223

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI.

PubMed

Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2008-01-01

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.

Classification of current anticancer immunotherapies

PubMed Central

Vacchelli, Erika; Pedro, José-Manuel Bravo-San; Buqué, Aitziber; Senovilla, Laura; Baracco, Elisa Elena; Bloy, Norma; Castoldi, Francesca; Abastado, Jean-Pierre; Agostinis, Patrizia; Apte, Ron N.; Aranda, Fernando; Ayyoub, Maha; Beckhove, Philipp; Blay, Jean-Yves; Bracci, Laura; Caignard, Anne; Castelli, Chiara; Cavallo, Federica; Celis, Estaban; Cerundolo, Vincenzo; Clayton, Aled; Colombo, Mario P.; Coussens, Lisa; Dhodapkar, Madhav V.; Eggermont, Alexander M.; Fearon, Douglas T.; Fridman, Wolf H.; Fučíková, Jitka; Gabrilovich, Dmitry I.; Galon, Jérôme; Garg, Abhishek; Ghiringhelli, François; Giaccone, Giuseppe; Gilboa, Eli; Gnjatic, Sacha; Hoos, Axel; Hosmalin, Anne; Jäger, Dirk; Kalinski, Pawel; Kärre, Klas; Kepp, Oliver; Kiessling, Rolf; Kirkwood, John M.; Klein, Eva; Knuth, Alexander; Lewis, Claire E.; Liblau, Roland; Lotze, Michael T.; Lugli, Enrico; Mach, Jean-Pierre; Mattei, Fabrizio; Mavilio, Domenico; Melero, Ignacio; Melief, Cornelis J.; Mittendorf, Elizabeth A.; Moretta, Lorenzo; Odunsi, Adekunke; Okada, Hideho; Palucka, Anna Karolina; Peter, Marcus E.; Pienta, Kenneth J.; Porgador, Angel; Prendergast, George C.; Rabinovich, Gabriel A.; Restifo, Nicholas P.; Rizvi, Naiyer; Sautès-Fridman, Catherine; Schreiber, Hans; Seliger, Barbara; Shiku, Hiroshi; Silva-Santos, Bruno; Smyth, Mark J.; Speiser, Daniel E.; Spisek, Radek; Srivastava, Pramod K.; Talmadge, James E.; Tartour, Eric; Van Der Burg, Sjoerd H.; Van Den Eynde, Benoît J.; Vile, Richard; Wagner, Hermann; Weber, Jeffrey S.; Whiteside, Theresa L.; Wolchok, Jedd D.; Zitvogel, Laurence; Zou, Weiping

2014-01-01

During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into “passive” and “active” based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches. PMID:25537519

Pulmonary artery sarcoma with angiosarcoma phenotype mimicking pleomorphic malignant fibrous histiocytoma: a case report

PubMed Central

2012-01-01

Abstract Primary sarcomas of the major blood vessels can be classified based on location in relationship to the wall or by histologic type. Angiosarcomas are malignant neoplasms that arise from the endothelial lining of the blood vessels; those arising in the intimal compartment of pulmonary artery are rare. We report a case of pulmonary artery angiosarcoma in a 36-year old female with pulmonary masses. The patient had no other primary malignant neoplasm, thus excluding a metastatic lesion. Gross examination revealed a thickened right pulmonary artery and a necrotic and hemorrhagic tumor, filling and occluding the vascular lumen. The mass extended distally, within the pulmonary vasculature of the right lung. Microscopically, an intravascular undifferentiated tumor was identified. The tumor cells showed expression for vascular markers VEGFR, VEGFR3, PDGFRa, FGF, Ulex europaeus, FVIII, FLI-1, CD31 and CD34; p53 was overexpressed and Ki67 proliferative rate was increased. Intravascular angiosarcomas are aggressive neoplasms, often associated with poor outcome. Virtual slide The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2315906377648045. PMID:23134683

Distant failure prediction for early stage NSCLC by analyzing PET with sparse representation

NASA Astrophysics Data System (ADS)

Hao, Hongxia; Zhou, Zhiguo; Wang, Jing

2017-03-01

Positron emission tomography (PET) imaging has been widely explored for treatment outcome prediction. Radiomicsdriven methods provide a new insight to quantitatively explore underlying information from PET images. However, it is still a challenging problem to automatically extract clinically meaningful features for prognosis. In this work, we develop a PET-guided distant failure predictive model for early stage non-small cell lung cancer (NSCLC) patients after stereotactic ablative radiotherapy (SABR) by using sparse representation. The proposed method does not need precalculated features and can learn intrinsically distinctive features contributing to classification of patients with distant failure. The proposed framework includes two main parts: 1) intra-tumor heterogeneity description; and 2) dictionary pair learning based sparse representation. Tumor heterogeneity is initially captured through anisotropic kernel and represented as a set of concatenated vectors, which forms the sample gallery. Then, given a test tumor image, its identity (i.e., distant failure or not) is classified by applying the dictionary pair learning based sparse representation. We evaluate the proposed approach on 48 NSCLC patients treated by SABR at our institute. Experimental results show that the proposed approach can achieve an area under the characteristic curve (AUC) of 0.70 with a sensitivity of 69.87% and a specificity of 69.51% using a five-fold cross validation.

Long-Term Outcomes of Eye-Sparing Surgery for Adenoid Cystic Carcinoma of Lacrimal Gland.

PubMed

Han, Jisang; Kim, Yoon-Duck; Woo, Kyung In; Sobti, Deepak

This study's primary purpose is to assess the long-term outcomes of patients who have undergone eye-sparing surgery and adjuvant radiotherapy for adenoid cystic carcinoma of the lacrimal gland. In this retrospective analysis, clinical records were reviewed of all patients diagnosed with adenoid cystic carcinoma of the lacrimal gland, at a single institution, between March 1998 and November 2012. Ten patients were identified as having undergone eye-sparing surgery and adjuvant radiotherapy for adenoid cystic carcinoma of the lacrimal gland. Preoperative radiographic findings, treatment modalities, histological results, and patient outcomes were analyzed. There were 6 male and 4 female patients. The patients' tumors were staged according to the 8th American Joint Committee on Cancer staging system, and were as follows: 1 patient was classified as T1aN0M0; 6 patients were classified as T2aN0M0; 1 patient was classified as T2cN0M0; 2 patients were classified as T3aN0M0. All patients had a histologically confirmed diagnosis of lacrimal gland adenoid cystic carcinoma, which was confined to the orbit, and was without extension into adjacent bone marrow or other organs. All patients underwent eye-sparing tumor excision followed by postoperative radiotherapy, with a median dose of 6000 cGy (range: 5000-6600 cGy). At the last follow up, 8 patients were alive without evidence of disease. One patient was deceased at 58 months post-surgery, due to esophageal carcinoma; this was unrelated to the lacrimal gland tumor. The final patient experienced tumor recurrence in the medial orbit 53 months post-surgery, and exenteration was performed. This patient was alive, without disease recurrence, at 90 months following exenteration. The median follow-up time was 89.5 months (range: 37-217 months). Systemic metastasis did not occur in any patient. Eye-sparing surgery and adjuvant radiotherapy have demonstrated favorable local control and long-term survival outcomes in patients with orbit-confined lacrimal gland adenoid cystic carcinoma. Consequently, eye-sparing surgery with adjuvant radiotherapy can be considered as a viable treatment option for orbit-confined lacrimal gland adenoid cystic carcinoma.

Favorable Prognosis in Patients With High-Grade Glioma With Radiation Necrosis: The University of Colorado Reoperation Series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rusthoven, Kyle E.; Olsen, Christine; Franklin, Wilbur

Purpose: To analyze the pathology, outcomes, and prognostic factors in patients with high-grade glioma undergoing reoperation after radiotherapy (RT). Methods and Materials: Fifty-one patients with World Health Organization Grade 3-4 glioma underwent reoperation after prior RT. The median dose of prior RT was 60 Gy, and 84% received chemotherapy as part of their initial treatment. Estimation of the percentage of necrosis and recurrent tumor in each reoperation specimen was performed. Pathology was classified as RT necrosis if {>=}80% of the specimen was necrotic and as tumor recurrence if {>=}20% was tumor. Predictors of survival were analyzed using log-rank comparisons andmore » Cox proportional hazards regression. Results: The median interval between the completion of RT and reoperation was 6.7 months (range, 1-59 months). Pathologic analysis showed RT necrosis in 27% and recurrence in 73% of cases. Thirteen patients required a reoperation for uncontrolled symptoms. Among them, 1 patient (8%) had pathology showing RT necrosis, and 12 (92%) had tumor recurrence. Median survival after reoperation was longer for patients with RT necrosis (21.8 months vs. 7.0 months, p = 0.047). In 7 patients with Grade 4 tumors treated with temozolomide-based chemoradiation with RT necrosis, median survival from diagnosis and reoperation were 30.2 months and 21.8 months, respectively. Conclusions: Patients with RT necrosis at reoperation have improved survival compared with patients with tumor recurrence. Future efforts to intensify local therapy and increase local tumor control in patients with high-grade glioma seem warranted.« less

Three primary synchronous malignancies of the uterus, cervix, and fallopian tube: A case report.

PubMed

Song, Liang; Li, Qingli; Yang, Kaixuan; Yin, Rutie; Wang, Danqing

2018-06-01

Multiple primary malignancies can occur in the same organ or in multiple organs or systems. Likewise, they can occur simultaneously or successively. Based on the timing of the diagnosis, they are classified as multiple synchronous (i.e., concurrent) or metachronous (i.e., successive) primary malignancies. The vast majority of patients have multiple metachronous malignant tumors; multiple synchronous tumors are rare. A 63-year-old woman presented with the chief complaint of vaginal fluid discharge for 3 months and abdominal pain for 1 month. The patient was diagnosed with multiple synchronous primary malignancies: 1) endometrial poorly differentiated serous adenocarcinoma, stage IV; 2) poorly differentiated squamous cell carcinoma of the cervix, stage IB1; and 3) left-sided fallopian tube carcinoma in situ. After total abdominal hysterectomy, bilateral salpingo-oophorectomy, and comprehensive staging and debulking, the patient was administered eight courses of adjuvant chemotherapy (taxane carboplatin/taxane cisplatin). After chemotherapy completion, the patient has been undergoing regular follow-up examinations; no recurrence has been noted at 18 months. It is important to distinguish between multiple synchronous primary malignancies and metastasis of a primary tumor to select the appropriate treatment regimen and to adequately assess the patient's prognosis. When a cancer patient shows clinical manifestations of another tumor, not only metastasis but also the possibility of multiple synchronous primary malignant tumors should be considered. The duration of follow-up in patients with malignant tumors should be extended as much as possible, as the timely detection and treatment of other primary malignant tumors can prolong survival and improve the quality of life.

Using the Hill viewpoints from 1965 for evaluating strengths of evidence of the risk for brain tumors associated with use of mobile and cordless phones.

PubMed

Hardell, Lennart; Carlberg, Michael

2013-01-01

Wireless phones, i.e., mobile phones and cordless phones, emit radiofrequency electromagnetic fields (RF-EMF) when used. An increased risk of brain tumors is a major concern. The International Agency for Research on Cancer (IARC) at the World Health Organization (WHO) evaluated the carcinogenic effect to humans from RF-EMF in May 2011. It was concluded that RF-EMF is a group 2B, i.e., a "possible", human carcinogen. Bradford Hill gave a presidential address at the British Royal Society of Medicine in 1965 on the association or causation that provides a helpful framework for evaluation of the brain tumor risk from RF-EMF. All nine issues on causation according to Hill were evaluated. Regarding wireless phones, only studies with long-term use were included. In addition, laboratory studies and data on the incidence of brain tumors were considered. The criteria on strength, consistency, specificity, temporality, and biologic gradient for evidence of increased risk for glioma and acoustic neuroma were fulfilled. Additional evidence came from plausibility and analogy based on laboratory studies. Regarding coherence, several studies show increasing incidence of brain tumors, especially in the most exposed area. Support for the experiment came from antioxidants that can alleviate the generation of reactive oxygen species involved in biologic effects, although a direct mechanism for brain tumor carcinogenesis has not been shown. In addition, the finding of no increased risk for brain tumors in subjects using the mobile phone only in a car with an external antenna is supportive evidence. Hill did not consider all the needed nine viewpoints to be essential requirements. Based on the Hill criteria, glioma and acoustic neuroma should be considered to be caused by RF-EMF emissions from wireless phones and regarded as carcinogenic to humans, classifying it as group 1 according to the IARC classification. Current guidelines for exposure need to be urgently revised.

Quantification of tumor morphology via 3D histology: application to oral cavity cancers

NASA Astrophysics Data System (ADS)

Doyle, Scott; Brandwein-Gensler, Margaret; Tomaszewski, John

2016-03-01

Traditional histopathology quantifies disease through the study of glass slides, i.e. two-dimensional samples that are representative of the overall process. We hypothesize that 3D reconstruction can enhance our understanding of histopathologic interpretations. To test this hypothesis, we perform a pilot study of the risk model for oral cavity cancer (OCC), which stratifies patients into low-, intermediate-, and high-risk for locoregional disease-free survival. Classification is based on study of hematoxylin and eosin (H and E) stained tissues sampled from the resection specimens. In this model, the Worst Pattern of Invasion (WPOI) is assessed, representing specific architectural features at the interface between cancer and non-cancer tissue. Currently, assessment of WPOI is based on 2D sections of tissue, representing complex 3D structures of tumor growth. We believe that by reconstructing a 3D model of tumor growth and quantifying the tumor-host interface, we can obtain important diagnostic information that is difficult to assess in 2D. Therefore, we introduce a pilot study framework for visualizing tissue architecture and morphology in 3D from serial sections of histopathology. This framework can be used to enhance predictive models for diseases where severity is determined by 3D biological structure. In this work we utilize serial H and E-stained OCC resections obtained from 7 patients exhibiting WPOI-3 (low risk of recurrence) through WPOI-5 (high risk of recurrence). A supervised classifier automatically generates a map of tumor regions on each slide, which are then co-registered using an elastic deformation algorithm. A smooth 3D model of the tumor region is generated from the registered maps, which is suitable for quantitative tumor interface morphology feature extraction. We report our preliminary models created with this system and suggest further enhancements to traditional histology scoring mechanisms that take spatial architecture into consideration.

Defining the cellular precursors to human breast cancer

PubMed Central

Keller, Patricia J.; Arendt, Lisa M.; Skibinski, Adam; Logvinenko, Tanya; Klebba, Ina; Dong, Shumin; Smith, Avi E.; Prat, Aleix; Perou, Charles M.; Gilmore, Hannah; Schnitt, Stuart; Naber, Stephen P.; Garlick, Jonathan A.; Kuperwasser, Charlotte

2012-01-01

Human breast cancers are broadly classified based on their gene-expression profiles into luminal- and basal-type tumors. These two major tumor subtypes express markers corresponding to the major differentiation states of epithelial cells in the breast: luminal (EpCAM+) and basal/myoepithelial (CD10+). However, there are also rare types of breast cancers, such as metaplastic carcinomas, where tumor cells exhibit features of alternate cell types that no longer resemble breast epithelium. Until now, it has been difficult to identify the cell type(s) in the human breast that gives rise to these various forms of breast cancer. Here we report that transformation of EpCAM+ epithelial cells results in the formation of common forms of human breast cancer, including estrogen receptor-positive and estrogen receptor-negative tumors with luminal and basal-like characteristics, respectively, whereas transformation of CD10+ cells results in the development of rare metaplastic tumors reminiscent of the claudin-low subtype. We also demonstrate the existence of CD10+ breast cells with metaplastic traits that can give rise to skin and epidermal tissues. Furthermore, we show that the development of metaplastic breast cancer is attributable, in part, to the transformation of these metaplastic breast epithelial cells. These findings identify normal cellular precursors to human breast cancers and reveal the existence of a population of cells with epidermal progenitor activity within adult human breast tissues. PMID:21940501

Diets That Promote Colon Inflammation Associate With Risk of Colorectal Carcinomas That Contain Fusobacterium nucleatum.

PubMed

Liu, Li; Tabung, Fred K; Zhang, Xuehong; Nowak, Jonathan A; Qian, Zhi Rong; Hamada, Tsuyoshi; Nevo, Daniel; Bullman, Susan; Mima, Kosuke; Kosumi, Keisuke; da Silva, Annacarolina; Song, Mingyang; Cao, Yin; Twombly, Tyler S; Shi, Yan; Liu, Hongli; Gu, Mancang; Koh, Hideo; Li, Wanwan; Du, Chunxia; Chen, Yang; Li, Chenxi; Li, Wenbin; Mehta, Raaj S; Wu, Kana; Wang, Molin; Kostic, Aleksander D; Giannakis, Marios; Garrett, Wendy S; Hutthenhower, Curtis; Chan, Andrew T; Fuchs, Charles S; Nishihara, Reiko; Ogino, Shuji; Giovannucci, Edward L

2018-04-24

Specific nutritional components are likely to induce intestinal inflammation, which is characterized by increased levels of interleukin 6 (IL6), C-reactive protein (CRP), and tumor necrosis factor-receptor superfamily member 1B (TNFRSF1B) in the circulation and promotes colorectal carcinogenesis. The inflammatory effects of a diet can be estimated based on an empiric dietary inflammatory pattern (EDIP) score, calculated based on intake of 18 foods associated with plasma levels of IL6, CRP, and TNFRSF1B. An inflammatory environment in the colon (based on increased levels of IL6, CRP, and TNFRSF1B in peripheral blood) contributes to impairment of the mucosal barrier and altered immune cell responses, affecting the composition of the intestinal microbiota. Colonization by Fusobacterium nucleatum has been associated with the presence and features of colorectal adenocarcinoma. We investigated the association between diets that promote inflammation (based on EDIP score) and colorectal cancer subtypes classified by level of F nucleatum in the tumor microenvironment. We calculated EDIP scores based on answers to questionnaires collected from participants in the Nurses' Health Study (through June 1, 2012) and the Health Professionals Follow-up Study (through January 31, 2012). Participants in both cohorts reported diagnoses of rectal or colon cancer in biennial questionnaires; deaths from unreported colorectal cancer cases were identified through the National Death Index and next of kin. Colorectal tumor tissues were collected from hospitals where the patients underwent tumor resection and F nucleatum DNA was quantified by a polymerase chain reaction assay. We used multivariable duplication-method Cox proportional hazard regression to assess the associations of EDIP scores with risks of colorectal cancer subclassified by F nucleatum status. During 28 years of follow-up evaluation of 124,433 participants, we documented 951 incident cases of colorectal carcinoma with tissue F nucleatum data. Higher EDIP scores were associated with increased risk of F nucleatum-positive colorectal tumors (P trend  = .03); for subjects in the highest vs lowest EDIP score tertiles, the hazard ratio for F nucleatum-positive colorectal tumors was 1.63 (95% CI, 1.03-2.58). EDIP scores did not associate with F nucleatum-negative tumors (P trend  = .44). High EDIP scores associated with proximal F nucleatum-positive colorectal tumors but not with proximal F nucleatum-negative colorectal tumors (P heterogeneity  = .003). Diets that promote intestinal inflammation, based on EDIP score, are associated with increased risk of F nucleatum-positive colorectal carcinomas, but not carcinomas that do not contain these bacteria. These findings indicate that diet-induced intestinal inflammation alters the gut microbiome to contribute to colorectal carcinogenesis; nutritional interventions might be used in precision medicine and cancer prevention. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

An individualized radiation dose escalation trial in non-small cell lung cancer based on FDG-PET imaging.

PubMed

Wanet, Marie; Delor, Antoine; Hanin, François-Xavier; Ghaye, Benoît; Van Maanen, Aline; Remouchamps, Vincent; Clermont, Christian; Goossens, Samuel; Lee, John Aldo; Janssens, Guillaume; Bol, Anne; Geets, Xavier

2017-10-01

The aim of the study was to assess the feasibility of an individualized 18F fluorodeoxyglucose positron emission tomography (FDG-PET)-guided dose escalation boost in non-small cell lung cancer (NSCLC) patients and to assess its impact on local tumor control and toxicity. A total of 13 patients with stage II-III NSCLC were enrolled to receive a dose of 62.5 Gy in 25 fractions to the CT-based planning target volume (PTV; primary turmor and affected lymph nodes). The fraction dose was increased within the individual PET-based PTV (PTV PET ) using intensity modulated radiotherapy (IMRT) with a simultaneous integrated boost (SIB) until the predefined organ-at-risk (OAR) threshold was reached. Tumor response was assessed during follow-up by means of repeat FDG-PET/computed tomography. Acute and late toxicity were recorded and classified according to the CTCAE criteria (Version 4.0). Local progression-free survival was determined using the Kaplan-Meier method. The average dose to PTV PET reached 89.17 Gy for peripheral and 75 Gy for central tumors. After a median follow-up period of 29 months, seven patients were still alive, while six had died (four due to distant progression, two due to grade 5 toxicity). Local progression was seen in two patients in association with further recurrences. One and 2-year local progression free survival rates were 76.9% and 52.8%, respectively. Three cases of acute grade 3 esophagitis were seen. Two patients with central tumors developed late toxicity and died due to severe hemoptysis. These results suggest that a non-uniform and individualized dose escalation based on FDG-PET in IMRT delivery is feasible. The doses reached were higher in patients with peripheral compared to central tumors. This strategy enables good local control to be achieved at acceptable toxicity rates. However, dose escalation in centrally located tumors with direct invasion of mediastinal organs must be performed with great caution in order to avoid severe late toxicity.

The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

PubMed

Banan, Rouzbeh; Hartmann, Christian

2017-03-01

The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only established in a small number of institutions. In summary, while neuropathologists have now encountered various challenges in the transitional phase from the previous WHO 2007 version to the new WHO 2016 classification of brain tumors, clinical neurooncologists now face many new diagnoses allowing a clearly improved understanding that could offer them more effective therapeutic opportunities in neurooncological treatment. The new WHO 2016 classification presumably presents the highest number of modifications since the initial WHO classification of 1979 and thereby forces all professionals in the field of neurooncology to intensively understand the new concepts. This review article aims to present the basic concepts of the new WHO 2016 brain tumor classification for neurosurgeons with a focus on neurooncology.

Evaluation of chemotherapy response in patients with advanced head and neck cancer using [F-18]fluorodeoxyglucose positron emission tomography.

PubMed

Lowe, V J; Dunphy, F R; Varvares, M; Kim, H; Wittry, M; Dunphy, C H; Dunleavy, T; McDonough, E; Minster, J; Fletcher, J W; Boyd, J H

1997-12-01

[F-18]Fluorodeoxyglucose (FDG)-positron emission tomography (PET) can measure the metabolic activity of tissues; FDG-PET may be able to predict response to chemotherapy by identifying changes in tumor metabolism. Measurement of response to treatment may help improve survival in the management of advanced head and neck cancer. We evaluated this particular use of FDG-PET in patients participating in a neoadjuvant organ-preservation protocol using taxol and carboplatin and compared pathologic response after chemotherapy with changes in tumor metabolism measured by FDG-PET. Serial FDG-PET studies (n = 56) were performed in patients (n = 28) with stage III/IV head and neck cancer participating in a neoadjuvant organ-preservation protocol. The FDG-PET studies were performed before and after chemotherapy. All patients had tissue biopsies before and after chemotherapy. Patients were classified as pathologic complete response (PCR) or residual disease (RD) based on tissue biopsies. Visual analysis of PET scans was performed to identify patients with complete response by PET, and these findings were compared with pathology results. Metabolic changes were also evaluated using standardized uptake ratios (SUR) of FDG. The sensitivity and specificity of PET for residual cancer after therapy was 90% (19/21) and 83% (5/6), respectively. Two patients had initially negative biopsies and positive PET studies for persistent disease. Pathology review and rebiospy led to confirmation of the PET results in these cases, giving a sensitivity of 90% for initial tissue biopsy. In this preliminary analysis, FDG-PET was accurate in classifying response to chemotherapy in most patients. Fluorodeoxyglucose-PET may identify residual viable tumor when it is otherwise undetectable.

A classification prognostic score to predict OS in stage IV well-differentiated neuroendocrine tumors

PubMed Central

Pusceddu, Sara; Barretta, Francesco; Trama, Annalisa; Botta, Laura; Milione, Massimo; Buzzoni, Roberto; De Braud, Filippo; Mazzaferro, Vincenzo; Pastorino, Ugo; Seregni, Ettore; Mariani, Luigi; Gatta, Gemma; Di Bartolomeo, Maria; Femia, Daniela; Prinzi, Natalie; Coppa, Jorgelina; Panzuto, Francesco; Antonuzzo, Lorenzo; Bajetta, Emilio; Brizzi, Maria Pia; Campana, Davide; Catena, Laura; Comber, Harry; Dwane, Fiona; Fazio, Nicola; Faggiano, Antongiulio; Giuffrida, Dario; Henau, Kris; Ibrahim, Toni; Marconcini, Riccardo; Massironi, Sara; Žakelj, Maja Primic; Spada, Francesca; Tafuto, Salvatore; Van Eycken, Elizabeth; Van der Zwan, Jan Maaten; Žagar, Tina; Giacomelli, Luca; Miceli, Rosalba; Aroldi, Francesca; Bongiovanni, Alberto; Berardi, Rossana; Brighi, Nicole; Cingarlini, Sara; Cauchi, Carolina; Cavalcoli, Federica; Carnaghi, Carlo; Corti, Francesca; Duro, Marilina; Davì, Maria Vittoria; De Divitiis, Chiara; Ermacora, Paola; La Salvia, Anna; Luppi, Gabriele; Lo Russo, Giuseppe; Nichetti, Federico; Raimondi, Alessandra; Perfetti, Vittorio; Razzore, Paola; Rinzivillo, Maria; Siesling, Sabine; Torchio, Martina; Van Dijk, Boukje; Visser, Otto; Vernieri, Claudio

2018-01-01

No validated prognostic tool is available for predicting overall survival (OS) of patients with well-differentiated neuroendocrine tumors (WDNETs). This study, conducted in three independent cohorts of patients from five different European countries, aimed to develop and validate a classification prognostic score for OS in patients with stage IV WDNETs. We retrospectively collected data on 1387 patients: (i) patients treated at the Istituto Nazionale Tumori (Milan, Italy; n = 515); (ii) European cohort of rare NET patients included in the European RARECAREnet database (n = 457); (iii) Italian multicentric cohort of pancreatic NET (pNETs) patients treated at 24 Italian institutions (n = 415). The score was developed using data from patients included in cohort (i) (training set); external validation was performed by applying the score to the data of the two independent cohorts (ii) and (iii) evaluating both calibration and discriminative ability (Harrell C statistic). We used data on age, primary tumor site, metastasis (synchronous vs metachronous), Ki-67, functional status and primary surgery to build the score, which was developed for classifying patients into three groups with differential 10-year OS: (I) favorable risk group: 10-year OS ≥70%; (II) intermediate risk group: 30% ≤ 10-year OS < 70%; (III) poor risk group: 10-year OS <30%. The Harrell C statistic was 0.661 in the training set, and 0.626 and 0.601 in the RARECAREnet and Italian multicentric validation sets, respectively. In conclusion, based on the analysis of three ‘field-practice’ cohorts collected in different settings, we defined and validated a prognostic score to classify patients into three groups with different long-term prognoses. PMID:29559553

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

NASA Astrophysics Data System (ADS)

Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

2018-02-01

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

PubMed

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

Genomic characterization of recurrent high-grade astroblastoma.

PubMed

Bale, Tejus A; Abedalthagafi, Malak; Bi, Wenya Linda; Kang, Yun Jee; Merrill, Parker; Dunn, Ian F; Dubuc, Adrian; Charbonneau, Sarah K; Brown, Loreal; Ligon, Azra H; Ramkissoon, Shakti H; Ligon, Keith L

2016-01-01

Astroblastomas are rare primary brain tumors, diagnosed based on histologic features. Not currently assigned a WHO grade, they typically display indolent behavior, with occasional variants taking a more aggressive course. We characterized the immunohistochemical characteristics, copy number (high-resolution array comparative genomic hybridization, OncoCopy) and mutational profile (targeted next-generation exome sequencing, OncoPanel) of a cohort of seven biopsies from four patients to identify recurrent genomic events that may help distinguish astroblastomas from other more common high-grade gliomas. We found that tumor histology was variable across patients and between primary and recurrent tumor samples. No common molecular features were identified among the four tumors. Mutations commonly observed in astrocytic tumors (IDH1/2, TP53, ATRX, and PTEN) or ependymoma were not identified. However one case with rapid clinical progression displayed mutations more commonly associated with GBM (NF1(N1054H/K63)*, PIK3CA(R38H) and ERG(A403T)). Conversely, another case, originally classified as glioblastoma with nine-year survival before recurrence, lacked a GBM mutational profile. Other mutations frequently seen in lower grade gliomas (BCOR, BCORL1, ERBB3, MYB, ATM) were also present in several tumors. Copy number changes were variable across tumors. Our findings indicate that astroblastomas have variable growth patterns and morphologic features, posing significant challenges to accurate classification in the absence of diagnostically specific copy number alterations and molecular features. Their histopathologic overlap with glioblastoma will likely confound the observation of long-term GBM "survivors". Further genomic profiling is needed to determine whether these tumors represent a distinct entity and to guide management strategies. Copyright © 2016 Elsevier Inc. All rights reserved.

An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer

PubMed Central

Cosgrove, Casey M; Tritchler, David L; Cohn, David E; Mutch, David G; Rush, Craig M; Lankes, Heather A; Creasman, William T.; Miller, David S; Ramirez, Nilsa C; Geller, Melissa A; Powell, Matthew A; Backes, Floor J; Landrum, Lisa M; Timmers, Cynthia; Suarez, Adrian A; Zaino, Richard J; Pearl, Michael L; DiSilvestro, Paul A; Lele, Shashikant B; Goodfellow, Paul J

2017-01-01

Objectives The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. Methods Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. Results Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53–3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10–7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04–4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. Conclusions A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system. PMID:29132872

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers.

PubMed

Berger, Ashton C; Korkut, Anil; Kanchi, Rupa S; Hegde, Apurva M; Lenoir, Walter; Liu, Wenbin; Liu, Yuexin; Fan, Huihui; Shen, Hui; Ravikumar, Visweswaran; Rao, Arvind; Schultz, Andre; Li, Xubin; Sumazin, Pavel; Williams, Cecilia; Mestdagh, Pieter; Gunaratne, Preethi H; Yau, Christina; Bowlby, Reanne; Robertson, A Gordon; Tiezzi, Daniel G; Wang, Chen; Cherniack, Andrew D; Godwin, Andrew K; Kuderer, Nicole M; Rader, Janet S; Zuna, Rosemary E; Sood, Anil K; Lazar, Alexander J; Ojesina, Akinyemi I; Adebamowo, Clement; Adebamowo, Sally N; Baggerly, Keith A; Chen, Ting-Wen; Chiu, Hua-Sheng; Lefever, Steve; Liu, Liang; MacKenzie, Karen; Orsulic, Sandra; Roszik, Jason; Shelley, Carl Simon; Song, Qianqian; Vellano, Christopher P; Wentzensen, Nicolas; Weinstein, John N; Mills, Gordon B; Levine, Douglas A; Akbani, Rehan

2018-04-09

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories. Copyright © 2018 Elsevier Inc. All rights reserved.

Apolipoprotein concentrations are elevated in malignant ovarian cyst fluids suggesting that lipoprotein metabolism is dysregulated in epithelial ovarian cancer.

PubMed

Podzielinski, Iwona; Saunders, Brook A; Kimbler, Kimberly D; Branscum, Adam J; Fung, Eric T; DePriest, Paul D; van Nagell, John R; Ueland, Frederick R; Baron, Andre T

2013-05-01

SELDI-TOF MS analysis of ovarian cyst fluids revealed that peaks m/z 8696 and 8825 discriminate malignant, borderline, and benign tumors. These peaks correspond to isoforms of apoA2. ELISA demonstrates that apoA1, A2, B, C2, C3, and E cyst fluid concentrations are uncorrelated and higher in malignant ovarian tumors, but only apoA2, apoE, and age are independent classifiers of malignant ovarian tumors, yielding 55.1% sensitivity, 95% specificity, and 88.1% accuracy to discern malignant from benign and borderline tumors. These data suggest that lipoprotein metabolism is dysregulated in ovarian cancer and that apoA2 and apoE warrant further investigation as ovarian tumor biomarkers.

Computer-aided diagnosis of liver tumors on computed tomography images.

PubMed

Chang, Chin-Chen; Chen, Hong-Hao; Chang, Yeun-Chung; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao-Lang; Chang, Ruey-Feng

2017-07-01

Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation. The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713. Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PubMed

Kunimatsu, Akira; Kunimatsu, Natsuko; Yasaka, Koichiro; Akai, Hiroyuki; Kamiya, Kouhei; Watadani, Takeyuki; Mori, Harushi; Abe, Osamu

2018-05-16

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T 1 -weighted images. This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T 1 -weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Salivary gland tumors in Turkey: demographic features and histopathological distribution of 510 patients.

PubMed

Kızıl, Yusuf; Aydil, Utku; Ekinci, Ozgür; Dilci, Alper; Köybaşıoğlu, Ahmet; Düzlü, Mehmet; Inal, Erdoğan

2013-07-01

The aim of this study was to evaluate the demographic and clinicopathologic data of salivary gland tumors managed at a tertiary referral medical center in Turkey. The data of 510 patients with salivary gland tumors managed during the period of January 1984 to May 2012, were reviewed. Only primary neoplasms derived from salivary glands were included. Out of 510 neoplasms, 352 (69.0 %) were classified as benign and 158 (31.0 %) were classified as malignant. There was a male predominance and male:female ratio was 1.23 (281/229). The most common location was parotid gland (372/510, 72.9 %) followed by minor salivary glands (97/510, 19.0 %) and submandibular gland (40/510, 7.9 %). The malignancy rates were 21.5, 40.0, and 56.7 % in parotid, submandibular, and minor salivary glands locations, respectively. The most common location for minor salivary gland neoplasms was oral cavity (61/97, 62.9 %). Pleomorphic adenoma (PA) was the most common histopathological type (45.3 %) in the whole study group and also among pediatric patients. The most common malignant neoplasms were adenoid cystic carcinoma (39/510, 7.6 %) and mucoepidermoid carcinoma (5.7 %). Salivary gland tumors are more common in men. The malignancy rate is almost three times higher in neoplasms derived from minor glands when compared to parotid gland. PA is the most common histopathological tumor type in all locations and in all age groups.

Prognostic significance of DNA ploidy in adenocarcinoma of the pancreas. A flow cytometric study of paraffin-embedded specimens.

PubMed

Porschen, R; Remy, U; Bevers, G; Schauseil, S; Hengels, K J; Borchard, F

1993-06-15

The prognostic significance of tumor DNA ploidy in patients with cancer of the pancreas has not been defined because conflicting results have been reported. DNA content was measured in 56 ductal adenocarcinomas of the pancreas. DNA ploidy status was evaluated by flow cytometry in nuclei isolated from paraffin-embedded tumor tissues. An abnormal DNA stemline was observed in 27 (48%) patients. The percentage of aneuploid tumors was significantly increased in tumors classified as Stage III/IV (53%) compared with those classified as Stage I (22%). A borderline significant association existed between DNA ploidy and radicality of surgery (P = 0.08). The median survival of patients with diploid carcinomas was 6.9 months (standard error, +/- 0.9) in comparison to 4.5 +/- 1.2 months for patients with aneuploid tumors (P = 0.013 by generalized Wilcoxon test; P = 0.023 by generalized Savage test). Although a selection bias cannot be excluded, survival of patients with a radical resection was longer than that of patients with a nonradical resection (P = 0.0008 and P = 0.0085, respectively). In addition, presence of distant metastasis (P = 0.0006 [Wilcoxon test] and P = 0.033 [Savage test]) could be identified as a prognostic factor. In a Cox regression model, results of surgery and DNA ploidy were independent prognostic variables. Because DNA ploidy has a significant impact on prognosis in pancreatic cancer, it should be used as a variable for stratified randomization of patients in therapeutic trials.

Prognostic factors of phyllodes tumor of the breast.

PubMed

Roa, Juan Carlos; Tapia, Oscar; Carrasco, Paula; Contreras, Enrique; Araya, Juan Carlos; Muñoz, Sergio; Roa, Iván

2006-06-01

The phyllodes tumor is characterized by its tendency to recur locally and occasionally to metastasize. The purpose of the present paper was to assess the prognostic value of clinical-morphological characteristics in patients with phyllodes tumor. Forty-seven cases of phyllodes tumors was studied; the World Health Organization classification was used and follow up was obtained. A total of 51%, 28% and 21% of the tumors were classified as benign, borderline and malignant, respectively. The adherence (P = 0.01), size >10 cm (P = 0.001), high mitotic activity (P = 0.03), infiltrative tumor margin (P = 0.0002) and type of surgery in malignant tumors (P = 0.02) proved to be good predictors of relapse. The presence of pain (P = 0.03), postmenopausal status (P < 0.04), heavy cellular pleomorphism (P = 0.007), high mitotic activity (P = 0.002), tumoral grade (P = 0.006) and metastasis (P < 0.00001) were prognostic factors of poor survival. Tumoral grade and some clinical-morphological characteristics of patients with phyllodes tumors have a significant impact on the prediction of its biological behavior.

Combating malignant astrocytes: Strategies mitigating tumor invasion.

PubMed

Umans, Robyn A; Sontheimer, Harald

2018-01-01

Malignant gliomas are glial-derived, primary brain tumors that carry poor prognosis. Existing therapeutics are largely ineffective and dramatically affect quality of life. The standard of care details a taxing combination of surgical resection, radiation of the resection cavity, and temozolomide (TMZ) chemotherapy, with treatment extending life by only an average of months (Maher et al., 2001; Stupp et al., 2005). Despite scientific and technological advancement, surgery remains the most important treatment modality. Therapeutic obstacles include xenobiotic protection conveyed by the blood-brain barrier (Zhang et al., 2015), invasiveness and therapeutic resistance of tumor cell populations (Bao et al., 2006), and distinctive attributes of secondary glioma occurrence (Ohgaki and Kleihues, 2013). While these brain malignancies can be classified by grade or grouped by molecular subclass, each tumor presents itself as its own complication. Based on all of these obstacles, new therapeutic approaches are urgently needed. These will likely emerge from numerous exciting studies of glioma biology that are ongoing and reviewed here. These show unexpected roles for ion channels, amino-acid transporters, and connexin gap junctions in supporting the invasive growth of gliomas. These studies have identified a number of proteins that may be targeted for therapy in the future. Copyright © 2017 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

A Case of Paraneoplastic Remitting Seronegative Symmetrical Synovitis with Pitting Edema Syndrome Improved by Chemotherapy

PubMed Central

Sakamoto, Takahiko; Ota, Shuji; Haruyama, Terunobu; Ishihara, Masashi; Natsume, Maika; Fukasawa, Yoko; Tanzawa, Shigeru; Usui, Ryo; Honda, Takeshi; Ichikawa, Yasuko; Watanabe, Kiyotaka; Seki, Nobuhiko

2017-01-01

The patient was a 69-year-old male who had started experiencing acute-onset pain in both shoulder joints and edema of both hands and feet. His symptoms progressively worsened within 1 month. Laboratory data indicated elevated CRP and erythrocyte sedimentation rate despite the normal range of antinuclear antibodies and rheumatoid factor and normal organ function. Furthermore, imaging data of the hand indicated synovitis without bone erosions. Meanwhile, chest CT revealed a lung tumor, leading to a diagnosis of primary lung adenocarcinoma with EGFR mutation (cT2aN3M0, stage IIIB). Based on these findings, he was diagnosed as suffering from paraneoplastic remitting seronegative symmetrical synovitis with pitting edema (RS3PE) syndrome. Thereafter, his symptoms disappeared as the tumor size was rapidly decreased by gefitinib therapy for lung adenocarcinoma. Currently, RS3PE syndrome can be classified as a vascular endothelial growth factor (VEGF)-associated disorder. Given that his symptoms improved by chemotherapy, the present case further supported the possible hypothesis that paraneoplastic RS3PE syndrome might be caused by tumor-induced VEGF. Therefore, the present case suggested that the symptoms of acute-onset joint pain accompanied by pitting edema in elderly patients should be considered suspicious for a malignant tumor, thereby warranting a detailed full-body examination. PMID:29430239

A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy

NASA Astrophysics Data System (ADS)

Tang, Xiaoli; Lin, Tong; Jiang, Steve

2009-09-01

We propose a novel approach for potential online treatment verification using cine EPID (electronic portal imaging device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated radiotherapy requires high precision. It is essential to effectively monitor the target to ensure that the tumor is within the beam aperture. We modeled the treatment verification problem as a two-class classification problem and applied an artificial neural network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes—with the tumor inside or outside of the beam aperture. Training samples were generated for the ANN using digitally reconstructed radiographs (DRRs) with artificially added shifts in the tumor location—to simulate cine EPID images with different tumor locations. Principal component analysis (PCA) was used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm was tested on five hypofractionated lung patients in a retrospective fashion. On average, our proposed algorithm achieved a 98.0% classification accuracy, a 97.6% recall rate and a 99.7% precision rate. This work was first presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.

A comparison of machine learning techniques for survival prediction in breast cancer

PubMed Central

2011-01-01

Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. PMID:21569330

A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data.

PubMed

Plancoulaine, Benoit; Laurinaviciene, Aida; Herlin, Paulette; Besusparis, Justinas; Meskauskas, Raimundas; Baltrusaityte, Indra; Iqbal, Yasir; Laurinavicius, Arvydas

2015-10-19

Digital image analysis (DIA) enables higher accuracy, reproducibility, and capacity to enumerate cell populations by immunohistochemistry; however, the most unique benefits may be obtained by evaluating the spatial distribution and intra-tissue variance of markers. The proliferative activity of breast cancer tissue, estimated by the Ki67 labeling index (Ki67 LI), is a prognostic and predictive biomarker requiring robust measurement methodologies. We performed DIA on whole-slide images (WSI) of 302 surgically removed Ki67-stained breast cancer specimens; the tumour classifier algorithm was used to automatically detect tumour tissue but was not trained to distinguish between invasive and non-invasive carcinoma cells. The WSI DIA-generated data were subsampled by hexagonal tiling (HexT). Distribution and texture parameters were compared to conventional WSI DIA and pathology report data. Factor analysis of the data set, including total numbers of tumor cells, the Ki67 LI and Ki67 distribution, and texture indicators, extracted 4 factors, identified as entropy, proliferation, bimodality, and cellularity. The factor scores were further utilized in cluster analysis, outlining subcategories of heterogeneous tumors with predominant entropy, bimodality, or both at different levels of proliferative activity. The methodology also allowed the visualization of Ki67 LI heterogeneity in tumors and the automated detection and quantitative evaluation of Ki67 hotspots, based on the upper quintile of the HexT data, conceptualized as the "Pareto hotspot". We conclude that systematic subsampling of DIA-generated data into HexT enables comprehensive Ki67 LI analysis that reflects aspects of intra-tumor heterogeneity and may serve as a methodology to improve digital immunohistochemistry in general.

Papillary glioneuronal tumor. A case report.

PubMed

Castro Castro, Julián; Lista Martínez, Olalla; Caramés Díaz, Nuria; Conde Lorenzo, Noemi

2018-05-19

Papillary glioneuronal tumor (PGNT) is a recently described central nervous system neoplasm. In 2007, the World Health Organization classified this tumor as a grade I neuronal-glial neoplasm. Patients are usually juvenile and young adults who commonly present with headache or seizures. We report a case of a 13-year-old boy that was related to our hospital after suffering a mild head injury result of an automobile accident. Emergent CT scan showed a right hypointense temporo-occipital lesion. MRI confirmed the presence of a lesion suggestive of a primary brain tumor. The patient underwent total resection of the tumor, followed by an uneventful recovery. Pathological analysis of the lesion revealed characteristic pseudopapillary structure with astrocytes and neurons, compatible with PGNT. We discuss the clinical, Radiological and histological features of this infrequent type of tumors. Copyright © 2018 Sociedad Española de Neurocirugía. Publicado por Elsevier España, S.L.U. All rights reserved.

An Efficient Statistical Computation Technique for Health Care Big Data using R

NASA Astrophysics Data System (ADS)

Sushma Rani, N.; Srinivasa Rao, P., Dr; Parimala, P.

2017-08-01

Due to the changes in living conditions and other factors many critical health related problems are arising. The diagnosis of the problem at earlier stages will increase the chances of survival and fast recovery. This reduces the time of recovery and the cost associated for the treatment. One such medical related issue is cancer and breast cancer has been identified as the second leading cause of cancer death. If detected in the early stage it can be cured. Once a patient is detected with breast cancer tumor, it should be classified whether it is cancerous or non-cancerous. So the paper uses k-nearest neighbors(KNN) algorithm which is one of the simplest machine learning algorithms and is an instance-based learning algorithm to classify the data. Day-to -day new records are added which leds to increase in the data to be classified and this tends to be big data problem. The algorithm is implemented in R whichis the most popular platform applied to machine learning algorithms for statistical computing. Experimentation is conducted by using various classification evaluation metric onvarious values of k. The results show that the KNN algorithm out performes better than existing models.

Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry

PubMed Central

Alber, Georgina; Bette, Stefanie; Kaesmacher, Johannes; Boeckh-Behrens, Tobias; Gempt, Jens; Ringel, Florian; Specht, Hanno M.; Meyer, Bernhard; Zimmer, Claus

2017-01-01

Purpose Unambiguous evaluation of glioblastoma (GB) progression is crucial, both for clinical trials as well as day by day routine management of GB patients. 3D-volumetry in the follow-up of GB provides quantitative data on tumor extent and growth, and therefore has the potential to facilitate objective disease assessment. The present study investigated the utility of absolute changes in volume (delta) or regional, segmentation-based subtractions for detecting disease progression in longitudinal MRI follow-ups. Methods 165 high resolution 3-Tesla MRIs of 30 GB patients (23m, mean age 60.2y) were retrospectively included in this single center study. Contrast enhancement (CV) and tumor-related signal alterations in FLAIR images (FV) were semi-automatically segmented. Delta volume (dCV, dFV) and regional subtractions (sCV, sFV) were calculated. Disease progression was classified for every follow-up according to histopathologic results, decisions of the local multidisciplinary CNS tumor board and a consensus rating of the neuro-radiologic report. Results A generalized logistic mixed model for disease progression (yes / no) with dCV, dFV, sCV and sFV as input variables revealed that only dCV was significantly associated with prediction of disease progression (P = .005). Delta volume had a better accuracy than regional, segmentation-based subtractions (79% versus 72%) and a higher area under the curve by trend in ROC curves (.83 versus .75). Conclusion Absolute volume changes of the contrast enhancing tumor part were the most accurate volumetric determinant to detect progressive disease in assessment of GB and outweighed FLAIR changes as well as regional, segmentation-based image subtractions. This parameter might be useful in upcoming objective response criteria for glioblastoma. PMID:28245291

Individualized Radiation Dose Escalation Based on the Decrease in Tumor FDG Uptake and Normal Tissue Constraints Improve Survival in Patients With Esophageal Carcinoma.

PubMed

Ma, Jinbo; Wang, Zhaoyang; Wang, Chengde; Chen, Ercheng; Dong, Yaozong; Song, Yipeng; Wang, Wei; You, Dong; Jiang, Wei; Zang, Rukun

2017-02-01

To determine whether individualized radiation dose escalation after planned chemoradiation based on the decrease in tumor and normal tissue constraints can improve survival in patients with esophageal carcinoma. From August 2005 to December 2010, 112 patients with squamous esophageal carcinoma were treated with radical concurrent chemoradiation. Patients received positron emission tomography-computer tomography scan twice, before radiation and after radiation dose of 50.4 Gy. All patients were noncomplete metabolic response groups according to the Response Evaluation Criteria in solid tumors. Only 52 patients with noncomplete metabolic response received individualized dose escalation based on tumor and normal tissue constraints. Survival and treatment failure were observed and analyzed using SPSS (13.0). The rate of complete metabolic response for patients with noncomplete metabolic response after dose escalation reached 17.3% (9 of 52). The 2-year overall survival rates for patients with noncomplete metabolic response in the conventional and dose-escalation groups were 20.5% and 42.8%, respectively( P = .001). The 2-year local control rates for patients were 35.7% and 76.2%, respectively ( P = .002). When patients were classified into partial metabolic response and no metabolic response, 2-year overall survival rates for patients with partial metabolic response were significantly different in conventional and dose-escalation groups (33.8% vs 78.4%; P = .000). The 2-year overall survival rates for patients with no metabolic response in two groups (8.6% vs 15.1%) did not significantly differ ( P = .917). Individualized radiation dose escalation has the potential to improve survival in patients with esophageal carcinoma according to increased rate of complete metabolic response. However, further trials are needed to confirm this and to identify patients who may benefit from dose escalation.

WE-AB-204-07: Spatiotemporal Distribution of the FDG PET Tracer in Solid Tumors: Contributions of Diffusion and Convection Mechanisms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soltani, M; Sefidgar, M; Bazmara, H

2015-06-15

Purpose: In this study, a mathematical model is utilized to simulate FDG distribution in tumor tissue. In contrast to conventional compartmental modeling, tracer distributions across space and time are directly linked together (i.e. moving beyond ordinary differential equations (ODEs) to utilizing partial differential equations (PDEs) coupling space and time). The diffusion and convection transport mechanisms are both incorporated to model tracer distribution. We aimed to investigate the contributions of these two mechanisms on FDG distribution for various tumor geometries obtained from PET/CT images. Methods: FDG transport was simulated via a spatiotemporal distribution model (SDM). The model is based on amore » 5K compartmental model. We model the fact that tracer concentration in the second compartment (extracellular space) is modulated via convection and diffusion. Data from n=45 patients with pancreatic tumors as imaged using clinical FDG PET/CT imaging were analyzed, and geometrical information from the tumors including size, shape, and aspect ratios were classified. Tumors with varying shapes and sizes were assessed in order to investigate the effects of convection and diffusion mechanisms on FDG transport. Numerical methods simulating interstitial flow and solute transport in tissue were utilized. Results: We have shown the convection mechanism to depend on the shape and size of tumors whereas diffusion mechanism is seen to exhibit low dependency on shape and size. Results show that concentration distribution of FDG is relatively similar for the considered tumors; and that the diffusion mechanism of FDG transport significantly dominates the convection mechanism. The Peclet number which shows the ratio of convection to diffusion rates was shown to be of the order of 10−{sup 3} for all considered tumors. Conclusion: We have demonstrated that even though convection leads to varying tracer distribution profiles depending on tumor shape and size, the domination of the diffusion phenomenon prevents these factors from modulating FDG distribution.« less

American Joint Committee on Cancer Classification of Uveal Melanoma (Anatomic Stage) Predicts Prognosis in 7,731 Patients: The 2013 Zimmerman Lecture.

PubMed

Shields, Carol L; Kaliki, Swathi; Furuta, Minoru; Fulco, Enzo; Alarcon, Carolina; Shields, Jerry A

2015-06-01

To analyze the clinical features and prognosis of posterior uveal melanoma based on the American Joint Committee on Cancer (AJCC) (7th edition) tumor staging. Retrospective interventional case series. A total of 7731 patients. Uveal melanoma management. Melanoma-related metastasis and death. Of 7731 patients with posterior uveal (ciliary body and choroidal) melanoma, the AJCC tumor staging was stage I in 2767 (36%), stage II in 3735 (48%), stage III in 1220 (16%), and stage IV in 9 (<1%). Based on tumor staging (I, II, III, and IV), features that showed significant increase with tumor staging included age at presentation (57, 58, 60, 60 years) (P < 0.001), tumor base (8, 12, 17, 17 mm) (P < 0.001), tumor thickness (2.9, 6.0, 10.1, 10.2 mm) (P < 0.001), distance to optic disc (3, 5, 5, 5 mm) (P < 0.001), distance to foveola (3, 5, 5, 5 mm) (P < 0.001), mushroom configuration (6%, 24%, 34%, 33%) (P < 0.001), plateau configuration (3%, 4%, 7%, 11%) (P < 0.001), tumor pigmentation (48%, 53%, 69%, 78%) (P < 0.001), and extraocular extension (0%, 1%, 11%, 22%) (P < 0.001). After therapy, Kaplan-Meier estimates of metastasis at 1, 5, 10, and 20 years were <1%, 5%, 12%, and 20% for stage I, 2%; 17%, 29%, and 44% for stage II; 6%, 44%, 61%, and 73% for stage III, and 100% by 1 year for stage IV. Kaplan-Meier estimates of death at 1, 5, 10, and 20 years were <1%, 3%, 6%, and 8% for stage I; <1%, 9%, 15%, and 24% for stage II; 3%, 27%, 39%, and 53% for stage III, and 100% by 1 year for stage IV. Compared with stage I, the hazard ratio for metastasis/death was 3.1/3.1 for stage II and 9.3/10.1 for stage III. Compared with uveal melanoma classified as AJCC stage I, the rate of metastasis/death was 3 times greater for stage II, 9 to 10 times greater for stage III, and further greater for stage IV. Early detection of posterior uveal melanoma, at a point when the tumor is small, can be lifesaving. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Brain tissue analysis using texture features based on optical coherence tomography images

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Dillmann, Christopher; Gerhardt, Nils C.; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2018-02-01

Brain tissue differentiation is highly demanded in neurosurgeries, i.e. tumor resection. Exact navigation during the surgery is essential in order to guarantee best life quality afterwards. So far, no suitable method has been found that perfectly covers this demands. With optical coherence tomography (OCT), fast three dimensional images can be obtained in vivo and contactless with a resolution of 1-15 μm. With these specifications OCT is a promising tool to support neurosurgeries. Here, we investigate ex vivo samples of meningioma, healthy white and healthy gray matter in a preliminary study towards in vivo brain tumor removal assistance. Raw OCT images already display structural variations for different tissue types, especially meningioma. But, in order to achieve neurosurgical guidance directly during resection, an automated differentiation approach is desired. For this reason, we employ different texture feature based algorithms, perform a Principal Component Analysis afterwards and then train a Support Vector Machine classifier. In the future we will try different combinations of texture features and perform in vivo measurements in order to validate our findings.

Ga-68-DOTA-TATE PET/CT for discrimination of tumors of the optic pathway.

PubMed

Klingenstein, Annemarie; Haug, Alexander R; Miller, Christina; Hintschich, Christoph

2015-02-01

Symptomatic tumors of the optic nerve pathway may endanger vision. They are difficult to classify by imaging alone and biopsy may damage visual function. Tumor pathology influences treatment decision and a diagnostic tool with a high sensitivity and specificity would therefore be invaluable. We hypothesized that Ga-68-DOTA-TATE PET/CT may help in discriminating optic nerve tumors as uptake of somatostatin is elevated in meningiomas. Ga-68-DOTA-TATE PET/CT was used to examine 13 patients with ambiguous, symptomatic lesions of the optic pathway for treatment planning. The presence or absence of meningioma was validated by histopathology or supplementary diagnostic work-up. Ga-68-DOTA-TATE PET/CT identified 10 meningiomas (en plaque = 1, optic nerve sheath = 4, sphenoidal = 5) correctly via increased SSTR (somatostatin receptor) expression (mean SUVmax (maximum standardized uptake value) = 14.3 ± 15.4). 3 tumors did not show elevated Ga-68-DOTA-TATE uptake (SUVmax = 2.1 ± 1.0). Subsumizing all clinical-radiological follow-up tools available, these lesions were classified as an intracerebral metastasis of an advanced gastric carcinoma, histologically proven inflammatory collagenous connective tissue and presumed leukemic infiltration of a newly diagnosed chronic lymphocytic leukemia. In this case series, Ga-68-DOTA-TATE PET/CT demonstrated both a sensitivity and specificity of 100%. Yet, the golden standard of histopathology was only available in a subset of patients included. Ga-68-DOTA-TATE PET/CT proved to be a valuable diagnostic tool for the correct classification of equivocal, symptomatic tumors of the anterior optic pathway requiring therapy. PET/CT results influenced therapy decision essentially in all cases.

Extracranial metastasizing solitary fibrous tumors (SFT) of meninges: histopathological features of a case with long-term follow-up.

PubMed

Gessi, Marco; Gielen, Gerrit H; Roeder-Geyer, Eva-Dorette; Sommer, Clemens; Vieth, Michael; Braun, Veit; Kuchelmeister, Klaus; Pietsch, Torsten

2013-02-01

Extrapleural solitary fibrous tumors are uncommon mesenchymal neoplasms frequently observed in middle-aged adults and are classified, according to the WHO classification of soft tissue tumors, as part of the hemangiopericytoma tumor group. However, these two entities remain separated in the WHO classification of tumors of the central nervous system. In fact, meningeal solitary fibrous tumors are believed to be benign lesion and only in a minority of cases local relapses have been described, although detailed survival clinical studies on solitary fibrous tumors of meninges are rare. In contrast to hemangiopericytoma, which frequently shows distant extracranial metastases, such an event is exceptional in patients with meningeal solitary fibrous tumors and has been clinically reported in a handful of cases only and their histopathological features have not been investigated in detail. In this report, we describe the detailed clinico-pathological features of a meningeal solitary fibrous tumor presenting during a 17-year follow-up period, multiple intra-, extracranial relapses and lung metastases. © 2012 Japanese Society of Neuropathology.

[A case of lung abscess during chemotherapy for testicular tumor].

PubMed

Hayashi, Yujiro; Miyago, Naoki; Takeda, Ken; Yamaguchi, Yuichiro; Nakayama, Masashi; Arai, Yasuyuki; Kakimoto, Ken-ichi; Nishimura, Kazuo

2014-05-01

32-year-old man was seen in a clinic because of prolonged cough and slight-fever. Chest X-ray showed multiple pulmonary nodules, and multiple lung and mediastinal lymph node metastases from right testicular tumor was suspected by positron emission tomography/CT (PET/CT) scan. He was diagnosed with right testicular germ cell tumor (embryonal carcinoma + seminoma, pT2N1M1b), and classified into the intermediate risk group according to International Germ Cell Cancer Collaborative Group. He underwent 4 cycles of chemotherapy with bleomycin, etoposide and cisplatin (BEP therapy). During BEP therapy, sputum with foul odor appeared and chest CT scan revealed lung abscess with a necrotic lesion of metastatic tumor. The lung abscess was treated successfully with antibiotics.

Nasal and paranasal adenocarcinomas with neuroendocrine differentiation in dogs.

PubMed

Ninomiya, F; Suzuki, S; Tanaka, H; Hayashi, S; Ozaki, K; Narama, I

2008-03-01

Tumors of the nasal cavity or paranasal sinuses of 18 dogs were examined histopathologically, immunohistochemically, and histochemically. The tumors were classified histologically as 13 adenocarcinomas, 3 transitional carcinomas, 1 squamous cell carcinoma, and 1 adenosquamous carcinoma. Tumor cells were strongly immunoreactive for broad-spectrum cytokeratins in all cases, for cytokeratin 8/18 in 16 cases, and for cytokeratin 19 in 17 cases. None of the 18 carcinomas had cytologic or histologic features indicative of neuroendocrine differentiation, yet tumor cells in 5 of the 13 adenocarcinomas were argyrophilic and immunohistochemically positive for synaptophysin and chromogranin A. Results of this study indicate that neuroendocrine markers may be detected immunohistochemically and histochemically in canine nasal or paranasal adenocarcinomas despite the lack of typical histologic features of neuroendocrine differentiation.

[Application of neuroendoscope in the treatment of skull base chordoma].

PubMed

Zhang, Ya-Zhuo; Wang, Zong-Cheng; Zong, Xu-Yi; Wang, Xin-Sheng; Gui, Song-Bai; Zhao, Peng; Li, Chu-Zhong; He, Yue; Wang, Hong-Yun

2011-07-05

To further explore the application, approach, indication and prognosis of neuroendoscope treatment for skull base chordoma. A total of 101 patients of skull base chordoma were admitted at our hospital from May 2000 to April 2010. There were 59 males and 42 females. Their major clinical manifestations included headache, cranial nerve damage and dyspnea. They were classified according to the patterns of tumor growth: Type I (n = 13): tumor location at a single component of skull base, i. e. clivus or sphenoid sinus with intact cranial dura; Type II (n = 56): tumor involving more than two components of skull e. g clivus, sphenoid and nasal/oral cavity, etc. But there was no intracranial invasion; Type III (n = 32) : tumor extending widely and intradurally forming compression of brain stems and multiple cranial nerves. Based on the types of chordoma, different endoscopic approaches were employed, viz. transnasal, transoral, trans-subtemporal fossa and plus microsurgical craniotomy for staging in some complex cases. Among all patients, total resection was achieved (n = 19), subtotal (n = 58) and partial (n = 24). In partial resection cases, 16 cases were considered to be subtotal due to a second-stage operation. Most cases had conspicuous clinical improvements. Self-care recovery within one week post-operation accounted for 58.4%, two weeks 30.7%, one month 6.9% and more than one month 1.9%. Postoperative complications occurred in 13 cases (12.8%) and included CSF leakage (n = 4) cranial nerve palsy (n = 5), hemorrhagic nasal wounds (n = 3) and delayed intracranial hemorrhage (n = 1). All of these were cured or improved after an appropriate treatment. A follow-up of 6 - 60 months was conducted in 56 cases. Early detection and early treatment are crucial for achieving a better outcome in chordoma. Neuroendoscopic treatment plays an important role in managing those complicated cases. Precise endoscopic techniques plus different surgical approaches and staging procedures are required to improve the post-operative quality of life for patients.

T3 receptors in human pituitary tumors.

PubMed

Machiavelli, Gloria A; Pauni, Micaela; Heredia Sereno, Gastón M; Szijan, Irene; Basso, Armando; Burdman, José A

2009-11-01

The purpose of this work was to investigate the synthesis of T3 receptors in human tumors of the anterior pituitary gland, its relationship with the hormone synthesized and/or secreted by the tumor and the post-surgical evolution of the patient. Patients were evaluated clinically and by magnetic nuclear resonance to classify the adenoma according to their size. Hormonal concentrations in sera were determined by radioimmunoassay. Immunohistochemistry of the pituitary hormones was performed in the tumors. Tumors were obtained at surgery and immediately frozen in ice, transported to the laboratory and stored at -70 degrees C. Reverse transcription was performed with purified RNA from the tumors. Out of 33 pituitary tumors, 29 had RNA for T3 receptors synthesis (88%). They were present in different histological specimens, the tumors were grades 1-4 according to their size, and there was no relationship between the size of the tumor and the presence of T3 receptor RNAs. The post-surgical evolution of the patient was mostly dependent on the size and not on the presence of T3 receptors. The presence of thyroid hormone receptors in pituitary tumors is in line with two important characteristics of these tumors: they are histologically benign and well differentiated.

Which type of breast cancers is undetectable on ring-type dedicated breast PET?

PubMed

Sasada, Shinsuke; Masumoto, Norio; Goda, Noriko; Kajitani, Keiko; Emi, Akiko; Kadoya, Takayuki; Okada, Morihito

2018-05-22

To assess the factors causing tumor undetectability on ring-type dedicated breast positron emission tomography (DbPET). A total of 265 patients (288 tumors) underwent DbPET and contrast-enhanced magnetic resonance imaging (MRI) in a prone position. The distance between the shallowest part of the breast tumor and the front end of the pectoralis major muscle on MRI was considered as the tumor-to-chest wall distance. Twenty-four tumors (8.3%) were not visualized via DbPET. The tumor-to-chest wall distance for undetectable tumors was shorter than that of the detectable tumors (23.0 mm vs 38.5 mm, P < 0.001). Multivariate analysis indicated that proximity to the chest wall and low-grade tumors were independent predicting factors for undetectable cancers. Among the 24 undetectable cancers, 15 tumors were proximal to the chest wall, suggesting that they were outside or at the edge of field of view (FOV), and 7 were low-grade tumors, suggesting insignificant 18 F-fluorodeoxyglucose (FDG) uptake. The factors of undetectable breast cancers on DbPET are classified into two types; outside or at the edge of FOV and insignificant FDG uptake. Copyright © 2018 Elsevier Inc. All rights reserved.

18F-EF5 PET-based Imageable Hypoxia Predicts Local Recurrence in Tumors Treated With Highly Conformal Radiation Therapy.

PubMed

Qian, Yushen; Von Eyben, Rie; Liu, Yufei; Chin, Frederick T; Miao, Zheng; Apte, Sandeep; Carter, Justin N; Binkley, Michael S; Pollom, Erqi L; Harris, Jeremy P; Prionas, Nicolas D; Kissel, Madelyn; Simmons, Amanda; Diehn, Maximilian; Shultz, David B; Brown, J Martin; Maxim, Peter G; Koong, Albert C; Graves, Edward E; Loo, Billy W

2018-04-18

Tumor hypoxia contributes to radiation resistance. A noninvasive assessment of tumor hypoxia would be valuable for prognostication and possibly selection for hypoxia-targeted therapies. 18 F-pentafluorinated etanidazole ( 18 F-EF5) is a nitroimidazole derivative that has demonstrated promise as a positron emission tomography (PET) hypoxia imaging agent in preclinical and clinical studies. However, correlation of imageable hypoxia by 18 F-EF5 PET with clinical outcomes after radiation therapy remains limited. Our study prospectively enrolled 28 patients undergoing radiation therapy for localized lung or other tumors to receive pretreatment 18 F-EF5 PET imaging. Depending on the level of 18 F-EF5 tumor uptake, patients underwent functional manipulation of tumor oxygenation with either carbogen breathing or oral dichloroacetate followed by repeated 18 F-EF5 PET. The hypoxic subvolume of tumor was defined as the proportion of tumor voxels exhibiting higher 18 F-EF5 uptake than the 95th percentile of 18 F-EF5 uptake in the blood pool. Tumors with a hypoxic subvolume ≥ 10% on baseline 18 F-EF5 PET imaging were classified as hypoxic by imaging. A Cox model was used to assess the correlation between imageable hypoxia and clinical outcomes after treatment. At baseline, imageable hypoxia was demonstrated in 43% of all patients (12 of 28), including 6 of 16 patients with early-stage non-small cell lung cancer treated with stereotactic ablative radiation therapy and 6 of 12 patients with other cancers. Carbogen breathing was significantly associated with decreased imageable hypoxia, while dichloroacetate did not result in a significant change under our protocol conditions. Tumors with imageable hypoxia had a higher incidence of local recurrence at 12 months (30%) than those without (0%) (P < .01). Noninvasive hypoxia imaging by 18 F-EF5 PET identified imageable hypoxia in about 40% of tumors in our study population. Local tumor recurrence after highly conformal radiation therapy was higher in tumors with imageable hypoxia. Copyright © 2018 Elsevier Inc. All rights reserved.

Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach

NASA Astrophysics Data System (ADS)

Wang, Chuang; Udupa, Jayaram K.; Tong, Yubing; Chen, Jerry; Venigalla, Sriram; Odhner, Dewey; Guzzo, Thomas J.; Christodouleas, John; Torigian, Drew A.

2018-02-01

Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 - T stage < T2, with primary tumor locally confined to the bladder, and group 2 - T stage < T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.

Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area.

PubMed

Fang, S; Liang, J; Qian, T; Wang, Y; Liu, X; Fan, X; Li, S; Wang, Y; Jiang, T

2017-10-01

The accuracy of preoperative blood oxygen level-dependent fMRI remains controversial. This study assessed the association between the anatomic location of a tumor and the accuracy of fMRI-based motor function mapping in diffuse lower-grade gliomas. Thirty-five patients with lower-grade gliomas involving motor areas underwent preoperative blood oxygen level-dependent fMRI scans with grasping tasks and received intraoperative direct cortical stimulation. Patients were classified into an overlapping group and a nonoverlapping group, depending on the extent to which blood oxygen level-dependent fMRI and direct cortical stimulation results concurred. Tumor location was quantitatively measured, including the shortest distance from the tumor to the hand knob and the deviation distance of the midpoint of the hand knob in the lesion hemisphere relative to the midline compared with the normal contralateral hemisphere. A 4-mm shortest distance from the tumor to the hand knob value was identified as optimal for differentiating the overlapping and nonoverlapping group with the receiver operating characteristic curve (sensitivity, 84.6%; specificity, 77.8%). The shortest distances from the tumor to the hand knob of ≤4 mm were associated with inaccurate fMRI-based localizations of the hand motor cortex. The shortest distances from the tumor to the hand knob were larger ( P = .002), and the deviation distances for the midpoint of the hand knob in the lesion hemisphere were smaller ( P = .003) in the overlapping group than in the nonoverlapping group. This study suggests that the shortest distance from the tumor to the hand knob and the deviation distance for the midpoint of the hand knob on the lesion hemisphere are predictive of the accuracy of blood oxygen level-dependent fMRI results. Smaller shortest distances from the tumor to the hand knob and larger deviation distances for the midpoint of hand knob on the lesion hemisphere are associated with less accuracy of motor cortex localization with blood oxygen level-dependent fMRI. Preoperative fMRI data for surgical planning should be used cautiously when the shortest distance from the tumor to the hand knob is ≤4 mm, especially for lower-grade gliomas anterior to the central sulcus. © 2017 by American Journal of Neuroradiology.

Gene Discovery in Bladder Cancer Progression using cDNA Microarrays

PubMed Central

Sanchez-Carbayo, Marta; Socci, Nicholas D.; Lozano, Juan Jose; Li, Wentian; Charytonowicz, Elizabeth; Belbin, Thomas J.; Prystowsky, Michael B.; Ortiz, Angel R.; Childs, Geoffrey; Cordon-Cardo, Carlos

2003-01-01

To identify gene expression changes along progression of bladder cancer, we compared the expression profiles of early-stage and advanced bladder tumors using cDNA microarrays containing 17,842 known genes and expressed sequence tags. The application of bootstrapping techniques to hierarchical clustering segregated early-stage and invasive transitional carcinomas into two main clusters. Multidimensional analysis confirmed these clusters and more importantly, it separated carcinoma in situ from papillary superficial lesions and subgroups within early-stage and invasive tumors displaying different overall survival. Additionally, it recognized early-stage tumors showing gene profiles similar to invasive disease. Different techniques including standard t-test, single-gene logistic regression, and support vector machine algorithms were applied to identify relevant genes involved in bladder cancer progression. Cytokeratin 20, neuropilin-2, p21, and p33ING1 were selected among the top ranked molecular targets differentially expressed and validated by immunohistochemistry using tissue microarrays (n = 173). Their expression patterns were significantly associated with pathological stage, tumor grade, and altered retinoblastoma (RB) expression. Moreover, p33ING1 expression levels were significantly associated with overall survival. Analysis of the annotation of the most significant genes revealed the relevance of critical genes and pathways during bladder cancer progression, including the overexpression of oncogenic genes such as DEK in superficial tumors or immune response genes such as Cd86 antigen in invasive disease. Gene profiling successfully classified bladder tumors based on their progression and clinical outcome. The present study has identified molecular biomarkers of potential clinical significance and critical molecular targets associated with bladder cancer progression. PMID:12875971

Incidence of thromboembolic events after use of gelatin-thrombin-based hemostatic matrix during intracranial tumor surgery.

PubMed

Gazzeri, Roberto; Galarza, Marcelo; Conti, Carlo; De Bonis, Costanzo

2018-01-01

Association between the use of hemostatic agents made from collagen/gelatin mixed with thrombin and thromboembolic events in patients undergoing tumor resection has been suggested. This study evaluates the relationship between flowable hemostatic matrix and deep vein thrombosis in a large cohort of patients treated for brain tumor removal. The authors conducted a retrospective, multicenter, clinical review of all craniotomies for tumor removal performed between 2013 and 2014. Patients were classified in three groups: group I (flowable gelatin hemostatic matrix with thrombin), group II (gelatin hemostatic without thrombin), and group III (classical hemostatic). A total of 932 patients were selected: tumor pathology included 441 gliomas, 296 meningiomas, and 195 metastases. Thromboembolic events were identified in 4.7% of patients in which gelatin matrix with thrombin was applied, in 8.4% of patients with gelatin matrix without thrombin, and in 3.6% of cases with classical methods of hemostasis. Patients with venous thromboembolism had an increased proportion of high-grade gliomas (7.2%). Patients receiving a greater dose than 10 ml gelatin hemostatic had a higher rate of thromboembolic events. Intracranial hematoma requiring reintervention occurred in 19 cases: 4.5% of cases of group III, while reoperation was performed in 1.3 and 1.6% of patients in which gelatin matrix with or without thrombin was applied. Gelatin matrix hemostat is an efficacious tool for neurosurgeons in cases of difficult intraoperative bleeding during cranial tumor surgery. This study may help to identify those patients at high risk for developing thromboembolism and to treat them accordingly.

Diffuse reflectance spectroscopy as a tool for real-time tissue assessment during colorectal cancer surgery

NASA Astrophysics Data System (ADS)

Baltussen, Elisabeth J. M.; Snaebjornsson, Petur; de Koning, Susan G. Brouwer; Sterenborg, Henricus J. C. M.; Aalbers, Arend G. J.; Kok, Niels; Beets, Geerard L.; Hendriks, Benno H. W.; Kuhlmann, Koert F. D.; Ruers, Theo J. M.

2017-10-01

Colorectal surgery is the standard treatment for patients with colorectal cancer. To overcome two of the main challenges, the circumferential resection margin and postoperative complications, real-time tissue assessment could be of great benefit during surgery. In this ex vivo study, diffuse reflectance spectroscopy (DRS) was used to differentiate tumor tissue from healthy surrounding tissues in patients with colorectal neoplasia. DRS spectra were obtained from tumor tissue, healthy colon, or rectal wall and fat tissue, for every patient. Data were randomly divided into training (80%) and test (20%) sets. After spectral band selection, the spectra were classified using a quadratic classifier and a linear support vector machine. Of the 38 included patients, 36 had colorectal cancer and 2 had an adenoma. When the classifiers were applied to the test set, colorectal cancer could be discriminated from healthy tissue with an overall accuracy of 0.95 (±0.03). This study demonstrates the possibility to separate colorectal cancer from healthy surrounding tissue by applying DRS. High classification accuracies were obtained both in homogeneous and inhomogeneous tissues. This is a fundamental step toward the development of a tool for real-time in vivo tissue assessment during colorectal surgery.

Spatial Statistics for Tumor Cell Counting and Classification

NASA Astrophysics Data System (ADS)

Wirjadi, Oliver; Kim, Yoo-Jin; Breuel, Thomas

To count and classify cells in histological sections is a standard task in histology. One example is the grading of meningiomas, benign tumors of the meninges, which requires to assess the fraction of proliferating cells in an image. As this process is very time consuming when performed manually, automation is required. To address such problems, we propose a novel application of Markov point process methods in computer vision, leading to algorithms for computing the locations of circular objects in images. In contrast to previous algorithms using such spatial statistics methods in image analysis, the present one is fully trainable. This is achieved by combining point process methods with statistical classifiers. Using simulated data, the method proposed in this paper will be shown to be more accurate and more robust to noise than standard image processing methods. On the publicly available SIMCEP benchmark for cell image analysis algorithms, the cell count performance of the present paper is significantly more accurate than results published elsewhere, especially when cells form dense clusters. Furthermore, the proposed system performs as well as a state-of-the-art algorithm for the computer-aided histological grading of meningiomas when combined with a simple k-nearest neighbor classifier for identifying proliferating cells.

mRNA Expression of Platelet-Derived Growth Factor Receptor-{beta} and C-KIT: Correlation With Pathologic Response to Cetuximab-Based Chemoradiotherapy in Patients With Rectal Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erben, Philipp; Horisberger, Karoline; Muessle, Benjamin

2008-12-01

Purpose: Deviant expression of platelet-derived growth factor receptor-{beta} (PDGFR{beta}) and c-kit was shown in patients with colorectal cancer. In the present study, mRNA expression of PDGFR{beta} and c-kit in 33 patients with locally advanced rectal cancer undergoing preoperative chemoradiotherapy with cetuximab/capecitabine/irinotecan in correlation with the tumor regression rate was investigated. Methods and Materials: Pretherapeutic biopsy cores and tumor material from the resected specimens were collected in parallel with normal rectal mucosa. The expression levels of PDGFR{beta} and c-kit were measured by quantitative polymerase chain reaction. Tumors were classified as good responders (tumor regression grade [TRG], 2-3) or poor responders (TRG,more » 0-1). Results: The TRG evaluation of the resected specimen was TRG 0-1 in 11 and TRG 2-3 in 22. The median normalized ratios in the pretreatment mucosa vs. tumor biopsy cores was as follows: PDGFR{beta} ratio of 15.2 vs. 49.5 (p <0.0001) and c-kit ratio of 0.94 vs. 0.67 (p = 0.014). The same tendency was observed for the median PDGFR{beta} ratios after chemoradiotherapy completion: 34.2 vs. 170.0 (p <0.0001). The PDGFR{beta} and c-kit mRNA expression values in the pretreatment tumor biopsy cores were lower than the values in the resected specimens: PDGFR{beta} ratio 49.5 vs. 170.0 (p = 0.0002) and c-kit ratio 0.67 vs. 1.1 (p = 0.0003). Nevertheless, no correlation was seen between the pretherapeutic PDGFR{beta} and c-kit mRNA expression and the pathologic regression rate. Conclusion: Cetuximab-based chemoradiotherapy increased PDGFR{beta} levels even further compared with the pretreatment samples and deserves further investigation.« less

Pertuzumab/Trastuzumab/CT Versus Trastuzumab/CT Therapy for HER2+ Breast Cancer: Results from the Prospective Neoadjuvant Breast Registry Symphony Trial (NBRST).

PubMed

Beitsch, Peter; Whitworth, Pat; Baron, Paul; Rotkis, Michael C; Mislowsky, Angela M; Richards, Paul D; Murray, Mary K; Pellicane, James V; Dul, Carrie L; Nash, Charles H; Stork-Sloots, Lisette; de Snoo, Femke; Untch, Sarah; Lee, Laura A

2017-09-01

Pertuzumab became a standard part of neoadjuvant therapy for human epidermal growth factor receptor 2-positive (HER2+) breast cancers approximately halfway through Neoadjuvant Breast Registry Symphony Trial (NBRST) enrollment, providing a unique opportunity to determine biologically which clinical HER2+ patients benefit most from dual targeting. As a neoadjuvant phase 4 study, NBRST classifies patients by both conventional and molecular subtyping. Of 308 clinical HER2+ patients enrolled in NBRST between 2011 and 2014 from 62 U.S. institutions, 297 received neoadjuvant chemotherapy (NCT) with HER2-targeted therapy and underwent surgery. This study compared the pathologic complete response (pCR) rate of BluePrint versus clinical subtypes with treatment, specifically differences between trastuzumab (T) treatment and trastuzumab and pertuzumab (T/P) treatment. In this study, 60% of the patients received NCT-T, and 40% received NCT-T/P. The overall pCR rate (ypT0/isN0) was 47%. BluePrint classified 161 tumors (54%) as HER2 type, with a pCR rate of 65%. This was significantly higher than the pCR rate for the 91 HER2+ tumors (31%) classified as luminal (18%) (p = 0.00001) and the 45 tumors (15%) classified as basal (44%) (p = 0.0166). The patients treated with T/P had higher pCR rates than those treated with trastuzumab alone. The difference was most pronounced in the BluePrint luminal patients (8 vs. 31%). The highest pCR was reached by the BluePrint HER2-type patients treated with T/P (76%). The addition of pertuzumab leads to increased pCR rates for all HER2+ patient groups except for the BluePrint basal-type patients. This better response was most pronounced for the BluePrint luminal-type patients.

TMPRSS2-ERG gene fusion status in minute (minimal) prostatic adenocarcinoma.

PubMed

Albadine, Roula; Latour, Mathieu; Toubaji, Antoun; Haffner, Michael; Isaacs, William B; A Platz, Elizabeth; Meeker, Alan K; Demarzo, Angelo M; Epstein, Jonathan I; Netto, George J

2009-11-01

Minute prostatic adenocarcinomas are considered to be of insufficient virulence. Given recent suggestions of TMPRSS2-ERG gene fusion association with aggressive prostatic adenocarcinoma, we evaluated the incidence of TMPRSS2-ERG fusion in minute prostatic adenocarcinomas. A total of 45 consecutive prostatectomies with minute adenocarcinoma were used for tissue microarray construction. A total of 63 consecutive non-minimal, Gleason Score 6 tumors, from a separate PSA Era prostatectomy tissue microarray, were used for comparison. FISH was carried out using ERG break-apart probes. Tumors were assessed for fusion by deletion (Edel) or split (Esplit), duplicated fusions and low-level copy number gain in normal ERG gene locus. Minute adenocarcinomas: Fusion was evaluable in 32/45 tumors (71%). Fifteen out of 32 (47%) tumors were positive for fusion. Six (19%) were of the Edel class and 7 (22%) were classified as combined Edel+Esplit. Non-minute adenocarcinomas (pT2): Fusion was identified in 20/30 tumors (67%). Four (13%) were of Edel class and 5 (17%) were combined Edel+Esplit. Duplicated fusions were encountered in 5 (16%) tumors. Non-minute adenocarcinomas (pT3): Fusion was identified in 19/33 (58%). Fusion was due to a deletion in 6 (18%) tumors. Seven tumors (21%) were classified as combined Edel+Esplit. One tumor showed Esplit alone. Duplicated fusions were encountered in 3 (9%) cases. The incidence of duplicated fusions was higher in non-minute adenocarcinomas (13 vs 0%; P=0.03). A trend for higher incidence of low-level copy number gain in normal ERG gene locus without fusion was noted in non-minute adenocarcinomas (10 vs 0%; P=0.07). We found a TMPRSS2-ERG fusion rate of 47% in minute adenocarcinomas. The latter is not significantly different from that of grade matched non-minute adenocarcinomas. The incidence of duplicated fusion was higher in non-minute adenocarcinomas. Our finding of comparable rate of TMPRSS2-ERG fusion in minute adenocarcinomas may argue against its value as a marker of aggressive prostate carcinoma phenotype.

Cyclooxygenase-2 expression in non-metastatic triple-negative breast cancer patients.

PubMed

Mosalpuria, Kailash; Hall, Carolyn; Krishnamurthy, Savitri; Lodhi, Ashutosh; Hallman, D Michael; Baraniuk, Mary S; Bhattacharyya, Anirban; Lucci, Anthony

2014-09-01

Triple-negative breast cancer (TNBC) is characterised by lack of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER)2/neu gene amplification. TNBC patients typically present at a younger age, with a larger average tumor size, higher grade and higher rates of lymph node positivity compared to patients with ER/PR-positive tumors. Cyclooxygenase (COX)-2 regulates the production of prostaglandins and is overexpressed in a variety of solid tumors. In breast cancer, the overexpression of COX-2 is associated with indicators of poor prognosis, such as lymph node metastasis, poor differentiation and large tumor size. Since both TNBC status and COX-2 overexpression are known poor prognostic markers in primary breast cancer, we hypothesized that the COX-2 protein is overexpressed in the primary tumors of TNBC patients. The purpose of this study was to determine whether there exists an association between TNBC status and COX-2 protein overexpression in primary breast cancer. We prospectively evaluated COX-2 expression levels in primary tumor samples obtained from 125 patients with stage I-III breast cancer treated between February, 2005 and October, 2007. Information on clinicopathological factors was obtained from a prospective database. Baseline tumor characteristics and patient demographics were compared between TNBC and non-TNBC patients using the Chi-square and Fisher's exact tests. In total, 60.8% of the patients were classified as having ER-positive tumors, 51.2% were PR-positive, 14.4% had HER-2/neu amplification and 28.0% were classified as TNBC. COX-2 overexpression was found in 33.0% of the patients. TNBC was associated with COX-2 overexpression (P=0.009), PR expression (P=0.048) and high tumor grade (P=0.001). After adjusting for age, menopausal status, body mass index (BMI), lymph node status and neoadjuvant chemotherapy (NACT), TNBC was an independent predictor of COX-2 overexpression (P=0.01). In conclusion, the association between TNBC and COX-2 overexpression in operable breast cancer supports further investigation into COX-2-targeted therapy for patients with TNBC.

Bayesian Inference on Malignant Breast Cancer in Nigeria: A Diagnosis of MCMC Convergence

PubMed Central

Ogunsakin, Ropo Ebenezer; Siaka, Lougue

2017-01-01

Background: There has been no previous study to classify malignant breast tumor in details based on Markov Chain Monte Carlo (MCMC) convergence in Western, Nigeria. This study therefore aims to profile patients living with benign and malignant breast tumor in two different hospitals among women of Western Nigeria, with a focus on prognostic factors and MCMC convergence. Materials and Methods: A hospital-based record was used to identify prognostic factors for malignant breast cancer among women of Western Nigeria. This paper describes Bayesian inference and demonstrates its usage to estimation of parameters of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The result of the Bayesian approach is compared with the classical statistics. Results: The mean age of the respondents was 42.2 ±16.6 years with 52% of the women aged between 35-49 years. The results of both techniques suggest that age and women with at least high school education have a significantly higher risk of being diagnosed with malignant breast tumors than benign breast tumors. The results also indicate a reduction of standard errors is associated with the coefficients obtained from the Bayesian approach. In addition, simulation result reveal that women with at least high school are 1.3 times more at risk of having malignant breast lesion in western Nigeria compared to benign breast lesion. Conclusion: We concluded that more efforts are required towards creating awareness and advocacy campaigns on how the prevalence of malignant breast lesions can be reduced, especially among women. The application of Bayesian produces precise estimates for modeling malignant breast cancer. PMID:29072396

Comprehensive Molecular Characterization of Papillary Renal Cell Carcinoma

PubMed Central

Linehan, W. Marston; Spellman, Paul T.; Ricketts, Christopher J.; Creighton, Chad J.; Fei, Suzanne S.; Davis, Caleb; Wheeler, David A.; Murray, Bradley A.; Schmidt, Laura; Vocke, Cathy D.; Peto, Myron; Al Mamun, Abu Amar M.; Shinbrot, Eve; Sethi, Anurag; Brooks, Samira; Rathmell, W. Kimryn; Brooks, Angela N.; Hoadley, Katherine A.; Robertson, A. Gordon; Brooks, Denise; Bowlby, Reanne; Sadeghi, Sara; Shen, Hui; Weisenberger, Daniel J.; Bootwalla, Moiz; Baylin, Stephen B.; Laird, Peter W.; Cherniack, Andrew D.; Saksena, Gordon; Haake, Scott; Li, Jun; Liang, Han; Lu, Yiling; Mills, Gordon B.; Akbani, Rehan; Leiserson, Mark D.M.; Raphael, Benjamin J.; Anur, Pavana; Bottaro, Donald; Albiges, Laurence; Barnabas, Nandita; Choueiri, Toni K.; Czerniak, Bogdan; Godwin, Andrew K.; Hakimi, A. Ari; Ho, Thai; Hsieh, James; Ittmann, Michael; Kim, William Y.; Krishnan, Bhavani; Merino, Maria J.; Mills Shaw, Kenna R.; Reuter, Victor E.; Reznik, Ed; Shelley, Carl Simon; Shuch, Brian; Signoretti, Sabina; Srinivasan, Ramaprasad; Tamboli, Pheroze; Thomas, George; Tickoo, Satish; Burnett, Kenneth; Crain, Daniel; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph D.; Penny, Robert J.; Shelton, Candace; Shelton, W. Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Avedon, Melissa T.; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Santos, Tracie; Wise, Lisa; Zmuda, Erik; Demchok, John A.; Felau, Ina; Hutter, Carolyn M.; Sheth, Margi; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Ayala, Brenda; Baboud, Julien; Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Ally, Adrian; Balasundaram, Miruna; Balu, Saianand; Beroukhim, Rameen; Bodenheimer, Tom; Buhay, Christian; Butterfield, Yaron S.N.; Carlsen, Rebecca; Carter, Scott L.; Chao, Hsu; Chuah, Eric; Clarke, Amanda; Covington, Kyle R.; Dahdouli, Mahmoud; Dewal, Ninad; Dhalla, Noreen; Doddapaneni, HarshaVardhan; Drummond, Jennifer; Gabriel, Stacey B.; Gibbs, Richard A.; Guin, Ranabir; Hale, Walker; Hawes, Alicia; Hayes, D. Neil; Holt, Robert A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Steven J.M.; Jones, Corbin D.; Kalra, Divya; Kovar, Christie; Lewis, Lora; Li, Jie; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Meng, Shaowu; Meyerson, Matthew; Mieczkowski, Piotr A.; Moore, Richard A.; Morton, Donna; Mose, Lisle E.; Mungall, Andrew J.; Muzny, Donna; Parker, Joel S.; Perou, Charles M.; Roach, Jeffrey; Schein, Jacqueline E.; Schumacher, Steven E.; Shi, Yan; Simons, Janae V.; Sipahimalani, Payal; Skelly, Tara; Soloway, Matthew G.; Sougnez, Carrie; Tam, Angela; Tan, Donghui; Thiessen, Nina; Veluvolu, Umadevi; Wang, Min; Wilkerson, Matthew D.; Wong, Tina; Wu, Junyuan; Xi, Liu; Zhou, Jane; Bedford, Jason; Chen, Fengju; Fu, Yao; Gerstein, Mark; Haussler, David; Kasaian, Katayoon; Lai, Phillip; Ling, Shiyun; Radenbaugh, Amie; Van Den Berg, David; Weinstein, John N.; Zhu, Jingchun; Albert, Monique; Alexopoulou, Iakovina; Andersen, Jeremiah J; Auman, J. Todd; Bartlett, John; Bastacky, Sheldon; Bergsten, Julie; Blute, Michael L.; Boice, Lori; Bollag, Roni J.; Boyd, Jeff; Castle, Erik; Chen, Ying-Bei; Cheville, John C.; Curley, Erin; Davies, Benjamin; DeVolk, April; Dhir, Rajiv; Dike, Laura; Eckman, John; Engel, Jay; Harr, Jodi; Hrebinko, Ronald; Huang, Mei; Huelsenbeck-Dill, Lori; Iacocca, Mary; Jacobs, Bruce; Lobis, Michael; Maranchie, Jodi K.; McMeekin, Scott; Myers, Jerome; Nelson, Joel; Parfitt, Jeremy; Parwani, Anil; Petrelli, Nicholas; Rabeno, Brenda; Roy, Somak; Salner, Andrew L.; Slaton, Joel; Stanton, Melissa; Thompson, R. Houston; Thorne, Leigh; Tucker, Kelinda; Weinberger, Paul M.; Winemiller, Cythnia; Zach, Leigh Anne; Zuna, Rosemary

2016-01-01

Background Papillary renal cell carcinoma, accounting for 15% of renal cell carcinoma, is a heterogeneous disease consisting of different types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal cell carcinoma; no effective forms of therapy for advanced disease exist. Methods We performed comprehensive molecular characterization utilizing whole-exome sequencing, copy number, mRNA, microRNA, methylation and proteomic analyses of 161 primary papillary renal cell carcinomas. Results Type 1 and Type 2 papillary renal cell carcinomas were found to be different types of renal cancer characterized by specific genetic alterations, with Type 2 further classified into three individual subgroups based on molecular differences that influenced patient survival. MET alterations were associated with Type 1 tumors, whereas Type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-ARE pathway. A CpG island methylator phenotype (CIMP) was found in a distinct subset of Type 2 papillary renal cell carcinoma characterized by poor survival and mutation of the fumarate hydratase (FH) gene. Conclusions Type 1 and Type 2 papillary renal cell carcinomas are clinically and biologically distinct. Alterations in the MET pathway are associated with Type 1 and activation of the NRF2-ARE pathway with Type 2; CDKN2A loss and CIMP in Type 2 convey a poor prognosis. Furthermore, Type 2 papillary renal cell carcinoma consists of at least 3 subtypes based upon molecular and phenotypic features. PMID:26536169

Simultaneous liver mucinous cystic and intraductal papillary mucinous neoplasms of the bile duct: a case report.

PubMed

Budzynska, Agnieszka; Hartleb, Marek; Nowakowska-Dulawa, Ewa; Krol, Robert; Remiszewski, Piotr; Mazurkiewicz, Michal

2014-04-14

Cystic hepatic neoplasms are rare tumors, and are classified into two separate entities: mucinous cystic neoplasms (MCNs) and intraductal papillary mucinous neoplasms of the bile duct (IPMN-B). We report the case of a 56-year-old woman who presented with abdominal pain and jaundice due to the presence of a large hepatic multilocular cystic tumor associated with an intraductal tumor. Partial hepatectomy with resection of extrahepatic bile ducts demonstrated an intrahepatic MCN and an intraductal IPMN-B. This is the first report of the simultaneous occurrence of these two histologically distinct entities in the liver.

Are the uterine serous carcinomas underdiagnosed? Histomorphologic and immunohistochemical correlates and clinical follow up in high-grade endometrial carcinomas initially diagnosed as high-grade endometrioid carcinoma.

PubMed

Hu, Shaomin; Hinson, Jeff L; Matnani, Rahul; Cibull, Michael L; Karabakhtsian, Rouzan G

2018-02-01

Histologic subclassification of high-grade endometrial carcinomas can sometimes be a diagnostic challenge when based on histomorphology alone. Here we utilized immunohistochemical markers to determine the immunophenotype in histologically ambiguous high-grade endometrial carcinomas that were initially diagnosed as pure or mixed high-grade endometrioid carcinoma, aiming to determine the utility of selected immunohistochemical panel in accurate classification of these distinct tumor types, while correlating these findings with the clinical outcome. A total of 43 high-grade endometrial carcinoma cases initially classified as pure high-grade endometrioid carcinoma (n=32), mixed high-grade endometrioid carcinoma/serous carcinoma (n=9) and mixed high-grade endometrioid carcinoma/clear cell carcinoma (n=2) were retrospectively stained with a panel of immunostains, including antibodies for p53, p16, estrogen receptor, and mammaglobin. Clinical follow-up data were obtained, and stage-to-stage disease outcomes were compared for different tumor types. Based on aberrant staining for p53 and p16, 17/43 (40%) of the high-grade endometrial carcinoma cases initially diagnosed as high-grade endometrioid carcinoma were re-classified as serous carcinoma. All 17 cases showed negative staining for mammaglobin, while estrogen receptor was positive in only 6 (35%) cases. The remaining 26 cases of high-grade endometrioid carcinoma showed wild-type staining for p53 in 25 (96%) cases, patchy staining for p16 in 20 (77%) cases, and were positive for mammaglobin and estrogen receptor in 8 (31%) and 19 (73%) cases, respectively, thus the initial diagnosis of high-grade endometrioid carcinoma was confirmed in these cases. In addition, the patients with re-classified serous carcinoma had advanced clinical stages at diagnosis and poorer overall survival on clinical follow-up compared to that of the remaining 26 high-grade endometrioid carcinoma cases. These results indicate that selected immunohistochemical panel, including p53, p16, and mammaglobin can be helpful in reaching accurate diagnosis in cases of histomorphologically ambiguous endometrial carcinomas, and can assist in providing guidance for appropriate therapeutic options for the patients.

Transcutaneous in vivo Raman spectroscopic studies in a mouse model: evaluation of changes in the breast associated with pregnancy and lactation

NASA Astrophysics Data System (ADS)

Bhattacharjee, Tanmoy; Maru, Girish; Ingle, Arvind; Krishna, C. Murali

2013-04-01

Raman spectroscopy (RS) has been extensively explored as an alternative diagnostic tool for breast cancer. This can be attributed to its sensitivity to malignancy-associated biochemical changes. However, biochemical changes due to nonmalignant conditions like benign lesions, inflammatory diseases, aging, menstrual cycle, pregnancy, and lactation may act as confounding factors in diagnosis of breast cancer. Therefore, in this study, the efficacy of RS to classify pregnancy and lactation-associated changes as well as its effect on breast tumor diagnosis was evaluated. Since such studies are difficult in human subjects, a mouse model was used. Spectra were recorded transcutaneously from the breast region of six Swiss bare mice postmating, during pregnancy, and during lactation. Data were analyzed using multivariate statistical tool Principal Component-Linear Discriminant Analysis. Results suggest that RS can differentiate breasts of pregnant/lactating mice from those of normal mice, the classification efficiencies being 100%, 60%, and 88% for normal, pregnant, and lactating mice, respectively. Frank breast tumors could be classified with 97.5% efficiency, suggesting that these physiological changes do not affect the ability of RS to detect breast tumors.

Automated Image Analysis of HER2 Fluorescence In Situ Hybridization to Refine Definitions of Genetic Heterogeneity in Breast Cancer Tissue

PubMed Central

Radziuviene, Gedmante; Rasmusson, Allan; Augulis, Renaldas; Lesciute-Krilaviciene, Daiva; Laurinaviciene, Aida; Clim, Eduard

2017-01-01

Human epidermal growth factor receptor 2 gene- (HER2-) targeted therapy for breast cancer relies primarily on HER2 overexpression established by immunohistochemistry (IHC) with borderline cases being further tested for amplification by fluorescence in situ hybridization (FISH). Manual interpretation of HER2 FISH is based on a limited number of cells and rather complex definitions of equivocal, polysomic, and genetically heterogeneous (GH) cases. Image analysis (IA) can extract high-capacity data and potentially improve HER2 testing in borderline cases. We investigated statistically derived indicators of HER2 heterogeneity in HER2 FISH data obtained by automated IA of 50 IHC borderline (2+) cases of invasive ductal breast carcinoma. Overall, IA significantly underestimated the conventional HER2, CEP17 counts, and HER2/CEP17 ratio; however, it collected more amplified cells in some cases below the lower limit of GH definition by manual procedure. Indicators for amplification, polysomy, and bimodality were extracted by factor analysis and allowed clustering of the tumors into amplified, nonamplified, and equivocal/polysomy categories. The bimodality indicator provided independent cell diversity characteristics for all clusters. Tumors classified as bimodal only partially coincided with the conventional GH heterogeneity category. We conclude that automated high-capacity nonselective tumor cell assay can generate evidence-based HER2 intratumor heterogeneity indicators to refine GH definitions. PMID:28752092

Automated Image Analysis of HER2 Fluorescence In Situ Hybridization to Refine Definitions of Genetic Heterogeneity in Breast Cancer Tissue.

PubMed

Radziuviene, Gedmante; Rasmusson, Allan; Augulis, Renaldas; Lesciute-Krilaviciene, Daiva; Laurinaviciene, Aida; Clim, Eduard; Laurinavicius, Arvydas

2017-01-01

Human epidermal growth factor receptor 2 gene- (HER2-) targeted therapy for breast cancer relies primarily on HER2 overexpression established by immunohistochemistry (IHC) with borderline cases being further tested for amplification by fluorescence in situ hybridization (FISH). Manual interpretation of HER2 FISH is based on a limited number of cells and rather complex definitions of equivocal, polysomic, and genetically heterogeneous (GH) cases. Image analysis (IA) can extract high-capacity data and potentially improve HER2 testing in borderline cases. We investigated statistically derived indicators of HER2 heterogeneity in HER2 FISH data obtained by automated IA of 50 IHC borderline (2+) cases of invasive ductal breast carcinoma. Overall, IA significantly underestimated the conventional HER2, CEP17 counts, and HER2/CEP17 ratio; however, it collected more amplified cells in some cases below the lower limit of GH definition by manual procedure. Indicators for amplification, polysomy, and bimodality were extracted by factor analysis and allowed clustering of the tumors into amplified, nonamplified, and equivocal/polysomy categories. The bimodality indicator provided independent cell diversity characteristics for all clusters. Tumors classified as bimodal only partially coincided with the conventional GH heterogeneity category. We conclude that automated high-capacity nonselective tumor cell assay can generate evidence-based HER2 intratumor heterogeneity indicators to refine GH definitions.

Pancreatic neuroendocrine tumors containing areas of iso- or hypoattenuation in dynamic contrast-enhanced computed tomography: Spectrum of imaging findings and pathological grading.

PubMed

Hyodo, Ryota; Suzuki, Kojiro; Ogawa, Hiroshi; Komada, Tomohiro; Naganawa, Shinji

2015-11-01

To evaluate dynamic contrast-enhanced computed tomography (CT) features of pancreatic neuroendocrine tumors (PNETs) containing areas of iso- or hypoattenuation and the relationship with pathological grading. Between June 2006 and March 2014, 61 PNETs in 58 consecutive patients (29 male, 29 female; median-age 55 years), which were surgically diagnosed, underwent preoperative dynamic contrast-enhanced CT. PNETs were classified based on contrast enhancement patterns in the pancreatic phase: iso/hypo-PNETs were defined as tumors containing areas of iso- or hypoattenuation except for cystic components, and hyper-PNETs were tumors showing hyperattenuation over the whole area. CT findings and contrast-enhancement patterns of the tumors were evaluated retrospectively by two radiologists and compared with the pathological grading. Iso/hypo-PNETs comprised 26 tumors, and hyper-PNETs comprised 35 tumors. Not only hyper-PNETs but also most iso/hypo-PNETs showed peak enhancement in the pancreatic phase and a washout from the portal venous phase to the delayed phase. Iso/hypo-PNETs showed larger tumor size than the hyper-PNETs (mean, 3.7 cm vs. 1.6 cm; P<0.001), and were significantly correlated with unclear tumor margins (n=4 vs. n=0; P=0.029), the existence of cystic components (n=10 vs. n=3; P=0.006), intratumoral blood vessels in the early arterial phase (n=13 vs. n=3; P<0.001), and a smooth rim enhancement in the delayed phase (n=12 vs. n=6; P=0.019). Iso/hypo-PNETs also showed significantly higher pathological grading (WHO 2010 classification; iso/hypo, G1=14, G2=11, G3=1; hyper, G1=34, G2=1; P<0.001). PNETs containing iso/hypo-areas showed a rapid enhancement pattern as well as hyper-PNETs, various radiological features and higher malignant potential. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Zonal NePhRO scoring system: a superior renal tumor complexity classification model.

PubMed

Hakky, Tariq S; Baumgarten, Adam S; Allen, Bryan; Lin, Hui-Yi; Ercole, Cesar E; Sexton, Wade J; Spiess, Philippe E

2014-02-01

Since the advent of the first standardized renal tumor complexity system, many subsequent scoring systems have been introduced, many of which are complicated and can make it difficult to accurately measure data end points. In light of these limitations, we introduce the new zonal NePhRO scoring system. The zonal NePhRO score is based on 4 anatomical components that are assigned a score of 1, 2, or 3, and their sum is used to classify renal tumors. The zonal NePhRO scoring system is made up of the (Ne)arness to collecting system, (Ph)ysical location of the tumor in the kidney, (R)adius of the tumor, and (O)rganization of the tumor. In this retrospective study, we evaluated patients exhibiting clinical stage T1a or T1b who underwent open partial nephrectomy performed by 2 genitourinary surgeons. Each renal unit was assigned both a zonal NePhRO score and a RENAL (radius, exophytic/endophytic properties, nearness of tumor to the collecting system or sinus in millimeters, anterior/posterior, location relative to polar lines) score, and a blinded reviewer used the same preoperative imaging study to obtain both scores. Additional data points gathered included age, clamp time, complication rate, urine leak rate, intraoperative blood loss, and pathologic tumor size. One hundred sixty-six patients underwent open partial nephrectomy. There were 37 perioperative complications quantitated using the validated Clavien-Dindo system; their occurrence was predicted by the NePhRO score on both univariate and multivariate analyses (P = .0008). Clinical stage, intraoperative blood loss, and tumor diameter were all correlated with the zonal NePhRO score on univariate analysis only. The zonal NePhRO scoring system is a simpler tool that accurately predicts the surgical complexity of a renal lesion. Copyright © 2014 Elsevier Inc. All rights reserved.

Two-stage microfluidic chip for selective isolation of circulating tumor cells (CTCs).

PubMed

Hyun, Kyung-A; Lee, Tae Yoon; Lee, Su Hyun; Jung, Hyo-Il

2015-05-15

Over the past few decades, circulating tumor cells (CTCs) have been studied as a means of overcoming cancer. However, the rarity and heterogeneity of CTCs have been the most significant hurdles in CTC research. Many techniques for CTC isolation have been developed and can be classified into positive enrichment (i.e., specifically isolating target cells using cell size, surface protein expression, and so on) and negative enrichment (i.e., specifically eluting non-target cells). Positive enrichment methods lead to high purity, but could be biased by their selection criteria, while the negative enrichment methods have relatively low purity, but can isolate heterogeneous CTCs. To compensate for the known disadvantages of the positive and negative enrichments, in this study we introduced a two-stage microfluidic chip. The first stage involves a microfluidic magnetic activated cell sorting (μ-MACS) chip to elute white blood cells (WBCs). The second stage involves a geometrically activated surface interaction (GASI) chip for the selective isolation of CTCs. We observed up to 763-fold enrichment in cancer cells spiked into 5 mL of blood sample using the μ-MACS chip at 400 μL/min flow rate. Cancer cells were successfully separated with separation efficiencies ranging from 10.19% to 22.91% based on their EpCAM or HER2 surface protein expression using the GASI chip at a 100 μL/min flow rate. Our two-stage microfluidic chips not only isolated CTCs from blood cells, but also classified heterogeneous CTCs based on their characteristics. Therefore, our chips can contribute to research on CTC heterogeneity of CTCs, and, by extension, personalized cancer treatment. Copyright © 2014 Elsevier B.V. All rights reserved.

Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma

PubMed Central

Olar, Adriana; Wani, Khalida M; Wilson, Charmaine D; Zadeh, Gelareh; DeMonte, Franco; Jones, David TW; Pfister, Stefan M; Sulman, Erik P; Aldape, Kenneth D

2017-01-01

Meningioma is the most common primary brain tumor and carries a substantial risk of local recurrence. Methylation profiles of meningioma and their clinical implications are not well understood. We hypothesized that aggressive meningiomas have unique DNA methylation patterns that could be used to better stratify patient management. Samples (n=140) were profiled using the Illumina HumanMethylation450 BeadChip. Unsupervised modeling on a training set (n=89) identified 2 molecular methylation subgroups of meningioma (MM) with significantly different recurrence free survival (RFS) times between the groups: a prognostically unfavorable subgroup (MM-UNFAV) and a prognostically favorable subgroup (MM-FAV). This finding was validated in the remaining 51 samples and led to a baseline meningioma methylation classifier (bMMC) defined by 283 CpG loci (283-bMMC). To further optimize a recurrence predictor, probes subsumed within the baseline classifier were subject to additional modeling using a similar training/validation approach, leading to a 64-CpG loci meningioma methylation predictor (64-MMP). After adjustment for relevant clinical variables [WHO grade, mitotic index, Simpson grade, sex, location, and copy number aberrations (CNA)] multivariable analyses for RFS showed that the baseline methylation classifier was not significant (p=0.0793). The methylation predictor however was significantly associated with tumor recurrence (p<0.0001). CNA were extracted from the 450k intensity profiles. Tumor samples in the MM-UNFAV subgroup showed an overall higher proportion of CNAs compared to the MM-FAV subgroup tumors and the CNAs were complex in nature. CNAs in the MM-UNFAV subgroup included recurrent losses of 1p, 6q, 14q and 18q, and gain of 1q, all of which were previously identified as indicators of poor outcome. In conclusion, our analyses demonstrate robust DNA methylation signatures in meningioma that correlate with CNAs and stratify patients by recurrence risk. PMID:28130639

Gastrointestinal Stromal Tumors - Diagnosis and Surgical Treatment.

PubMed

Alecu, L; Tulin, A; Enciu, O; Bărbulescu, M; Ursuţ, B; Obrocea, F

2015-01-01

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the gastrointestinal tract, previously classified as leiomyomas, leiomyosarcomas, leiomyoblastomas or schwannomas. They are now recognized as a distinct entity with origin in the mesodermal interstitial cell of Cajal, cells that express the c-KIT protein (tirozine kinase receptor). The definitive diagnosis is established by immunohistochemistry, more than 95% of GISTs being positive for CD117. Despite the major progress of chemotherapy, the treatment of choice is surgery, and it implies the complete resection of the tumor. The evolution of these tumors is unpredictable and the prognosis depends on localization, tumor size and mitotic index. Benign tumors have an excellent prognosis after surgery, with a 5 year survival of 90%, while malignant tumors resistant to radiotherapy and chemotherapy have a dismal prognosis even after surgical resection, with a median survival of 1 year. We studied a group of 15 patients diagnosed with TSGI in the Surgery Clinic of the œProf. Dr. Agrippa Ionescu Clinical Emergency Hospital, between 2003 and 2013, following the particularities of presentation, diagnosis and treatment, with focus on the prognostic factors according to available literature data. Celsius.

Body mass index and risk of colorectal carcinoma subtypes classified by tumor differentiation status.

PubMed

Hanyuda, Akiko; Cao, Yin; Hamada, Tsuyoshi; Nowak, Jonathan A; Qian, Zhi Rong; Masugi, Yohei; da Silva, Annacarolina; Liu, Li; Kosumi, Keisuke; Soong, Thing Rinda; Jhun, Iny; Wu, Kana; Zhang, Xuehong; Song, Mingyang; Meyerhardt, Jeffrey A; Chan, Andrew T; Fuchs, Charles S; Giovannucci, Edward L; Ogino, Shuji; Nishihara, Reiko

2017-05-01

Previous studies suggest that abnormal energy balance status may dysregulate intestinal epithelial homeostasis and promote colorectal carcinogenesis, yet little is known about how host energy balance and obesity influence enterocyte differentiation during carcinogenesis. We hypothesized that the association between high body mass index (BMI) and colorectal carcinoma incidence might differ according to tumor histopathologic differentiation status. Using databases of the Nurses' Health Study and Health Professionals Follow-up Study, and duplication-method Cox proportional hazards models, we prospectively examined an association between BMI and the incidence of colorectal carcinoma subtypes classified by differentiation features. 120,813 participants were followed for 26 or 32 years and 1528 rectal and colon cancer cases with available tumor pathological data were documented. The association between BMI and colorectal cancer risk significantly differed depending on the presence or absence of poorly-differentiated foci (P heterogeneity  = 0.006). Higher BMI was associated with a higher risk of colorectal carcinoma without poorly-differentiated foci (≥30.0 vs. 18.5-22.4 kg/m 2 : multivariable-adjusted hazard ratio, 1.87; 95% confidence interval, 1.49-2.34; P trend  < 0.001), but not with risk of carcinoma with poorly-differentiated foci (P trend  = 0.56). This differential association appeared to be consistent in strata of tumor microsatellite instability or FASN expression status, although the statistical power was limited. The association between BMI and colorectal carcinoma risk did not significantly differ by overall tumor differentiation, mucinous differentiation, or signet ring cell component (P heterogeneity  > 0.03, with the adjusted α of 0.01). High BMI was associated with risk of colorectal cancer subtype containing no poorly-differentiated focus. Our findings suggest that carcinogenic influence of excess energy balance might be stronger for tumors that retain better intestinal differentiation throughout the tumor areas.

MRI brain tumor segmentation based on improved fuzzy c-means method

NASA Astrophysics Data System (ADS)

Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

2009-10-01

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

Cellular Angiofibroma Presenting as an Inguinal Subcutaneous Mass: a Case Report and Review of the Literature.

PubMed

Schiebel, Frank; Cassim, R

2016-01-01

Cellular angiofibroma is a rare benign mesenchymal tumor that occurs in the inguinal and vulvovaginal region. We report a case of the tumor occurring in the right inguinal region of a 64 old male and a review of the current literature. A 64 year old male veteran was referred to our general surgery service with an incidentally discovered right inguinal mass on a computerized tomography scan. The scan was performed to follow a history of prostate cancer that had been treated with brachytherapy. Magnetic resonance imaging of the lesion helped confirm that the mass did not represent a hernia or an undescended testicle. Surgical resection revealed encapsulated, yellowish, pink tissue measuring 6.5 x 5 x 3.5 cm. Microscopically, the sections showed densely fibrous to loose and focally fibromyxoid background of oval to spindle-shaped cells with a few scattered plasma cells and mast cells. Based upon the clinical, histologic, and immunohistochemical findings, the lesion was classified as a cellular angiofibroma. Cellular angiofibroma of the inguinal region is a rare benign encapsulated tumor.It should be considered in the differential diagnosis of a male with an inguinal mass proven not to be a hernia or undescended testicle.

Inflammatory myofibroblastic tumor in the head of the pancreas with anorexia and vomiting in a 69-year-old man: A case report.

PubMed

Ding, Ding; Bu, Xianmin; Tian, Feng

2016-08-01

Inflammatory myofibroblastic tumor (IMT) is a rare condition of unclear etiology that is commonly observed in the lung but rarely in the pancreas. WHO classified IMT as a potentially malignant or aggressive tumor. In the present report, the case of a 69-year-old male patient with an IMT in the head of the pancreas, who experienced anorexia, nausea and vomiting, is presented. The patient's clinical symptoms were nonspecific, and the imaging findings revealed a hypovascularized pancreatic mass with stenosis of the descending duodenum. The electronic endoscopy findings revealed protruding lesions in the duodenal bulb and the descending duodenum. Biopsies of the mass were conducted with an electronic endoscope, but were not diagnostic. Subsequent duodenopancreatectomy aided in determining a pathological diagnosis of IMT, based on the histology and immunohistochemistry results. The patient experienced a recovery without further incident, as observed during a regular follow-up 3 years later. IMT in the head of the pancreas is rare, particularly in adults. In the present study, an extremely rare case of IMT involving the head of the pancreas in an adult patient is presented, and the therapeutic options for this condition are discussed.

[Head and neck paragangliomas. Embryological origin and anatomical characteristics: topographic distribution and vascularization pattern].

PubMed

Carretero González, José; Blanco Pérez, Pedro; Vázquez Osorio, María Teresa; Benito González, Fernando; Sañudo Tejedo, José Ramón

2009-02-01

Paragangliomas are tumors that arise in the extraadrenal paraganglia and result from migration of neural crest cells during embryonic development. Based on their anatomical distribution, innervation and microscopic structure, these tumors can be classified into interrelated families: branchiomeric paraganglia (related to the branchial clefts and arches), intravagal, aortic-sympathetic and visceral-autonomic. Head and neck paragangliomas belong mainly to the first two of these families. The present article is divided into two parts. The first part reviews the embryological origin of these tumors. Special emphasis is placed on the process of neurulation or neural tube formation, neurosegmentation (with a summary of the mechanisms involved in the initial segmentation of the neural tube and of the hindbrain and spinal medulla), and the development of the sensory placodes and secondary inductions in the cranial region. Subsequently, the neural crest is analyzed, with special attention paid to the cranial neural crest. The embryonogenesis of paragangliomas is also described. The second part describes the topographical distribution of head and neck paragangliomas according to their localization: jugulotympanic, orbit, intercarotid, subclavian and laryngeal. The embryonogenesis and most important anatomical characteristics are described for each type.

PAM50 Breast Cancer Subtyping by RT-qPCR and Concordance with Standard Clinical Molecular Markers

PubMed Central

2012-01-01

Background Many methodologies have been used in research to identify the “intrinsic” subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions. Methods We used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and “intrinsic” subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments. Results ESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis. Conclusions The standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic. PMID:23035882

[Recent incidences and trends of childhood malignant solid tumors in Shanghai, 2002-2010].

PubMed

Bao, Ping-Ping; Li, Kai; Wu, Chun-Xiao; Huang, Zhe-Zhou; Wang, Chun-Fang; Xiang, Yong-Mei; Peng, Peng; Gong, Yang-Ming; Xiao, Xian-Min; Zheng, Ying

2013-04-01

To examine the recent incidences and trends of childhood malignant solid tumors in Shanghai. Data from the population-based Shanghai Cancer Registry and related retrospective survey were used to analyze the patterns of incidence and trends of malignant solid tumors diagnosed between 2002 and 2010 in children aged 0-14 years. The distributions of incidences were described according to gender, age and cancer types which were classified according to International Classification of Childhood Cancer (ICCC). Annual age-standardized rates (ASRs) were adjusted by the world standard population. Approximate confidence intervals for standardized rate ratios (SRR) based Poisson distribution test-based methods were used to assess changes in incidence over the period 2002 - 2006 and 2007 - 2010. (1)A total of 868 cases of childhood malignant solid tumors were diagnosed in Shanghai during 2002 - 2010, accounting for 65.8% of all childhood cancers. The ASR of 2002 - 2010 was 80.2 per million for all solid tumors. (2) The ASR was higher in boys (86.3 per million) than in girls (73.8 per million) with SRR 1.2 (95%CI 1.0 - 1.3). Incidence rate was the highest in the first five years of life with 93.4 per million. The age-specific incidence rates in 5 - 9 and 10 - 14 age groups were 65.2 and 79.3 per million, respectively. (3) CNS tumors, lymphomas, germ cell tumors, neuroblastoma, and soft tissue sarcomas were the top 5 most common solid tumors in children, with the incidence rate of 23.8, 11.0, 7.8, 7.7 and 6.8 per million, respectively. The patterns of subgroups varied in different age groups. Blastomas, such as neuroblastoma, retinoblastoma, were more common in the children aged 0 - 4 years, whereas epithelial carcinomas and bone tumors developed more frequently in elder children aged 10 - 14 years. (4) Compared with the ASR in 2002 - 2006, the ASR for both genders in 2007 - 2010 had no substantial changes (78.7 per million in 2002 - 2006 and 82.9 per million in 2007 - 2010). However, among boys, the incidence rate in 2007 - 2010 was significantly higher than that in 2002 - 2006 with SRR 1.2 (95%CI: 1.0 - 1.4). For specific subgroups of cancer, there were no substantial changes. Some cautions should be taken when interpreting results involving a small number of cases per year and those with wide 95% confidence intervals. The incidence rate of pediatric malignant solid tumors among males was higher than females during 2002 - 2010, and it differed among different age groups with the highest in the first five years of life. CNS tumor was the most common type of solid tumors in children. This was a unique characteristics comparing with adult reflected in disease spectrum and age of onset. The patterns of incidence and its trends for childhood malignant solid tumors in Shanghai could provide a basis for etiologic research and preventive interventions. The findings also suggest an urgent need for longer population-based surveillance to verify the pattern and changing trends.

Neurosurgical treatment of oligodendroglial tumors in children and adolescents: a single-institution series of 35 consecutive patients.

PubMed

Lundar, Tryggve; Due-Tønnessen, Bernt Johan; Egge, Arild; Scheie, David; Stensvold, Einar; Brandal, Petter

2013-09-01

The object of this study was to delineate long-term results of the surgical treatment of pediatric CNS tumors classified as oligodendroglioma (OD) or oligoastrocytoma (OA) WHO Grade II or III. A cohort of 45 consecutive patients 19 years or younger who had undergone primary resection of CNS tumors originally described as oligodendroglial during the years 1970-2009 at a single institution were reviewed in this retrospective study of surgical morbidity, mortality, and academic achievement and/or work participation. Gross motor function and activities of daily living were scored using the Barthel Index (BI). Patient records for 35 consecutive children and adolescents who had undergone resection for an OA (17 patients) or OD (18 patients) were included in this study. Of the 35 patients, 12 were in the 1st decade of life at the first surgery, whereas 23 were in the 2nd decade. The male/female ratio was 1.19 (19/16). No patient was lost to follow-up. The tumor was localized to the supratentorial compartment in 33 patients, the posterior fossa in 1 patient, and the cervical medulla in 1 patient. Twenty-four tumors were considered to be WHO Grade II, and 11 were classified as WHO Grade III. Among these latter lesions were 2 tumors initially classified as WHO Grade II and later reclassified as WHO Grade III following repeat surgery. Fifty-four tumor resections were performed. Two patients underwent repeat tumor resection within 5 days of the initial procedure, after MRI confirmed residual tumor. Another 10 patients underwent a second resection because of clinical deterioration and progressive disease at time points ranging from 1 month to 10 years after the initial operation. Six patients underwent a third resection, and 1 patient underwent a fourth excision following tumor dissemination to the spinal canal. Sixteen (46%) of the 35 children received adjuvant therapy: 7, fractionated radiotherapy; 4, chemotherapy; and 5, both fractionated radiotherapy and chemotherapy. One patient with primary supratentorial disease experienced clinically malignant development with widespread intraspinal dissemination 9 years after initial treatment. Only 2 patients needed treatment for persistent hydrocephalus. In this series there was no surgical mortality, which was defined as death within 30 days of resection. However, 12 patients in the study, with follow-up times from 1 month to 33 years, died. Twenty-three patients, with follow-up times from 4 to 31 years, remained alive. Among these survivors, the BI was 100 (normal) in 22 patients and 80 in 1 patient. Nineteen patients had full- or part-time work or were in normal school programs. Pediatric oligodendroglial tumors are mainly localized to the supratentorial compartment and more often occur in the 2nd decade of life rather than the 1st. Two-thirds of the patients remained alive after follow-ups from 4 to 31 years. Twelve children succumbed to their disease, 9 of them within 3 years of resection despite combined treatment with radio- and chemotherapy. Three of them remained alive from 9 to 33 years after primary resection. Among the 23 survivors, a stable, very long-term result was attainable in at least 20. Five-, 10-, 20-, and 30-year overall survival in patients with Grade II tumors was 92%, 92%, 92%, and 88%, respectively.

Transcription factor GATA-4 is a marker of anaplasia in adrenocortical neoplasms of the domestic ferret (Mustela putorius furo).

PubMed

Peterson, R A; Kiupel, M; Bielinska, M; Kiiveri, S; Heikinheimo, M; Capen, C C; Wilson, D B

2004-07-01

Adrenocortical neoplasms are a common cause of morbidity in neutered ferrets. Recently we showed that gonadectomized DBA/2J mice develop adrenocortical tumors that express transcription factor GATA-4. Therefore, we screened archival specimens of adrenocortical neoplasms from neutered ferrets to determine whether GATA-4 could be used as a tumor marker in this species. Nuclear immunoreactivity for GATA-4 was evident in 19/22 (86%) of ferret adrenocortical carcinomas and was prominent in areas exhibiting myxoid differentiation. Normal adrenocortical cells lacked GATA-4 expression. Two other markers of adrenocortical tumors in gonadectomized mice, inhibin-alpha and luteinizing hormone receptor, were coexpressed with GATA-4 in some of the ferret tumors. No GATA-4 expression was observed in three cases of nodular hyperplasia, but patches of anaplastic cells expressing GATA-4 were evident in 7/14 (50%) of tumors classified as adenomas. We conclude that GATA-4 can function as a marker of anaplasia in ferret adrenocortical tumors.

Re-evaluation of cases with gastroenteropancreatic neuroendocrine tumors between 2004 and 2012 according to the 2010 criteria.

PubMed

Ozkara, Selvinaz; Aker, Fugen; Yesil, Atakan; Senates, Ebubekir; Canbey, Ceren; Yitik, Ali; Gonen, Can

2013-10-01

We re-evaluated the clinical, histopathological and immunohistochemical features of neuroendocrine tumors (NETs) diagnosed in our pathology laboratory between 2004 and 2012 and re-classified them according to the WHO-2000 and WHO-2010 criteria. The study included NET samples of 106 patients having gastroenteropancreatic and hepatobiliary tumors. The histopathological findings were re-assessed. The cases were re-appraised based on the WHO-2000 and WHO-2010 criteria. The association between survival and Ki-67 index was analysed. The most frequent localization was the stomach. The average tumor size was 3.0±4.1 cm. Differentiation was poor in 17 cases (16.0%). Lymphovascular invasion was detected in 16.1% (n = 17) and necrosis was identified in 15.1% (n = 16). The average number of Ki-67 was 9.1±19.9. Ki-67 measurements were significantly higher in patients who died compared to those who survived (p <0.01). In ROC analysis, the cut-off point for Ki-67 was 5. Our study is a single-center study comprising patients from Turkey for a period of 8 years. We found that the most frequent localization is the stomach. This ratio is associated with common use of endoscopy in our center. The specimens were re-evaluated according to the WHO-2000 and WHO-2010 classification systems the data and terminology have been updated.

Clinical multiplexed exome sequencing distinguishes adult oligodendroglial neoplasms from astrocytic and mixed lineage gliomas.

PubMed

Cryan, Jane B; Haidar, Sam; Ramkissoon, Lori A; Bi, Wenya Linda; Knoff, David S; Schultz, Nikolaus; Abedalthagafi, Malak; Brown, Loreal; Wen, Patrick Y; Reardon, David A; Dunn, Ian F; Folkerth, Rebecca D; Santagata, Sandro; Lindeman, Neal I; Ligon, Azra H; Beroukhim, Rameen; Hornick, Jason L; Alexander, Brian M; Ligon, Keith L; Ramkissoon, Shakti H

2014-09-30

Classifying adult gliomas remains largely a histologic diagnosis based on morphology; however astrocytic, oligodendroglial and mixed lineage tumors can display overlapping histologic features. We used multiplexed exome sequencing (OncoPanel) on 108 primary or recurrent adult gliomas, comprising 65 oligodendrogliomas, 28 astrocytomas and 15 mixed oligoastrocytomas to identify lesions that could enhance lineage classification. Mutations in TP53 (20/28, 71%) and ATRX (15/28, 54%) were enriched in astrocytic tumors compared to oligodendroglial tumors of which 4/65 (6%) had mutations in TP53 and 2/65 (3%) had ATRX mutations. We found that oligoastrocytomas harbored mutations in TP53 (80%, 12/15) and ATRX (60%, 9/15) at frequencies similar to pure astrocytic tumors, suggesting that oligoastrocytomas and astrocytomas may represent a single genetic or biological entity. p53 protein expression correlated with mutation status and showed significant increases in astrocytomas and oligoastrocytomas compared to oligodendrogliomas, a finding that also may facilitate accurate classification. Furthermore our OncoPanel analysis revealed that 15% of IDH1/2 mutant gliomas would not be detected by traditional IDH1 (p.R132H) antibody testing, supporting the use of genomic technologies in providing clinically relevant data. In all, our results demonstrate that multiplexed exome sequencing can support evaluation and classification of adult low-grade gliomas with a single clinical test.

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N. C.; Tomaszewski, John; González, Fabio A.; Madabhushi, Anant

2017-04-01

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.

PubMed

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N C; Tomaszewski, John; González, Fabio A; Madabhushi, Anant

2017-04-18

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

In vivo Raman spectroscopy of human uterine cervix: exploring the utility of vagina as an internal control

NASA Astrophysics Data System (ADS)

Shaikh, Rubina; Dora, Tapas Kumar; Chopra, Supriya; Maheshwari, Amita; Kedar K., Deodhar; Bharat, Rekhi; Krishna, C. Murali

2014-08-01

In vivo Raman spectroscopy is being projected as a new, noninvasive method for cervical cancer diagnosis. In most of the reported studies, normal areas in the cancerous cervix were used as control. However, in the Indian subcontinent, the majority of cervical cancers are detected at advanced stages, leaving no normal sites for acquiring control spectra. Moreover, vagina and ectocervix are reported to have similar biochemical composition. Thus, in the present study, we have evaluated the feasibility of classifying normal and cancerous conditions in the Indian population and we have also explored the utility of the vagina as an internal control. A total of 228 normal and 181 tumor in vivo Raman spectra were acquired from 93 subjects under clinical supervision. The spectral features in normal conditions suggest the presence of collagen, while DNA and noncollagenous proteins were abundant in tumors. Principal-component linear discriminant analysis (PC-LDA) yielded 97% classification efficiency between normal and tumor groups. An analysis of a normal cervix and vaginal controls of cancerous and noncancerous subjects suggests similar spectral features between these groups. PC-LDA of tumor, normal cervix, and vaginal controls further support the utility of the vagina as an internal control. Overall, findings of the study corroborate with earlier studies and facilitate objective, noninvasive, and rapid Raman spectroscopic-based screening/diagnosis of cervical cancers.

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

PubMed Central

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N.C.; Tomaszewski, John; González, Fabio A.; Madabhushi, Anant

2017-01-01

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma. PMID:28418027

In vivo Raman spectroscopy of human uterine cervix: exploring the utility of vagina as an internal control.

PubMed

Shaikh, Rubina; Dora, Tapas Kumar; Chopra, Supriya; Maheshwari, Amita; Kedar K, Deodhar; Bharat, Rekhi; Krishna, C Murali

2014-08-01

In vivo Raman spectroscopy is being projected as a new, noninvasive method for cervical cancer diagnosis. In most of the reported studies, normal areas in the cancerous cervix were used as control. However, in the Indian subcontinent, the majority of cervical cancers are detected at advanced stages, leaving no normal sites for acquiring control spectra. Moreover, vagina and ectocervix are reported to have similar biochemical composition. Thus, in the present study, we have evaluated the feasibility of classifying normal and cancerous conditions in the Indian population and we have also explored the utility of the vagina as an internal control. A total of 228 normal and 181 tumor in vivo Raman spectra were acquired from 93 subjects under clinical supervision. The spectral features in normal conditions suggest the presence of collagen, while DNA and noncollagenous proteins were abundant in tumors. Principal-component linear discriminant analysis (PC-LDA) yielded 97% classification efficiency between normal and tumor groups. An analysis of a normal cervix and vaginal controls of cancerous and noncancerous subjects suggests similar spectral features between these groups. PC-LDA of tumor, normal cervix, and vaginal controls further support the utility of the vagina as an internal control. Overall, findings of the study corroborate with earlier studies and facilitate objective, noninvasive, and rapid Raman spectroscopic-based screening/diagnosis of cervical cancers.

Advances in Synthetic Peptides Reagent Discovery

DTIC Science & Technology

2013-07-01

to promote specific and high affinity binding. Longer incubations may result in nonspecific attachment, such as early biofilm formation. Because...peptide libraries yields ligand arrays that classify breast tumor subtypes,” Molecular Cancer Therapeutics, 8(5), 1312-1318 (2009). [26] J. M. Kogot

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution

PubMed Central

Kim, Hoon; Zheng, Siyuan; Amini, Seyed S.; Virk, Selene M.; Mikkelsen, Tom; Brat, Daniel J.; Grimsby, Jonna; Sougnez, Carrie; Muller, Florian; Hu, Jian; Sloan, Andrew E.; Cohen, Mark L.; Van Meir, Erwin G.; Scarpace, Lisa; Laird, Peter W.; Weinstein, John N.; Lander, Eric S.; Gabriel, Stacey; Getz, Gad; Meyerson, Matthew; Chin, Lynda; Barnholtz-Sloan, Jill S.

2015-01-01

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ∼7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity. PMID:25650244

Proteolysis during Tumor Cell Extravasation In Vitro: Metalloproteinase Involvement across Tumor Cell Types

PubMed Central

Voura, Evelyn B.; English, Jane L.; Yu, Hoi-Ying E.; Ho, Andrew T.; Subarsky, Patrick; Hill, Richard P.; Hojilla, Carlo V.; Khokha, Rama

2013-01-01

To test if proteolysis is involved in tumor cell extravasation, we developed an in vitro model where tumor cells cross an endothelial monolayer cultured on a basement membrane. Using this model we classified the ability of the cells to transmigrate through the endothelial cell barrier onto the underlying matrix, and scored this invasion according to the stage of passage through the endothelium. Metalloproteinase inhibitors reduced tumor cell extravasation by at least 35%. Visualization of protease and cell adhesion molecules by confocal microscopy demonstrated the cell surface localization of MMP-2, MMP-9, MT1-MMP, furin, CD44 and αvβ3, during the process of transendothelial migration. By the addition of inhibitors and bio-modulators we assessed the functional requirement of the aforementioned molecules for efficient migration. Proteolytic digestion occurred at the cell-matrix interface and was most evident during the migratory stage. All of the inhibitors and biomodulators affected the transition of the tumor cells into the migratory stage, highlighting the most prevalent use of proteolysis at this particular step of tumor cell extravasation. These data suggest that a proteolytic interface operates at the tumor cell surface within the tumor-endothelial cell microenvironment. PMID:24194929

Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes.

PubMed

Tomlins, Scott A; Alshalalfa, Mohammed; Davicioni, Elai; Erho, Nicholas; Yousefi, Kasra; Zhao, Shuang; Haddad, Zaid; Den, Robert B; Dicker, Adam P; Trock, Bruce J; DeMarzo, Angelo M; Ross, Ashley E; Schaeffer, Edward M; Klein, Eric A; Magi-Galluzzi, Cristina; Karnes, R Jeffrey; Jenkins, Robert B; Feng, Felix Y

2015-10-01

Prostate cancer (PCa) molecular subtypes have been defined by essentially mutually exclusive events, including ETS gene fusions (most commonly involving ERG) and SPINK1 overexpression. Clinical assessment may aid in disease stratification, complementing available prognostic tests. To determine the analytical validity and clinicopatholgic associations of microarray-based molecular subtyping. We analyzed Affymetrix GeneChip expression profiles for 1577 patients from eight radical prostatectomy cohorts, including 1351 cases assessed using the Decipher prognostic assay (GenomeDx Biosciences, San Diego, CA, USA) performed in a laboratory with Clinical Laboratory Improvements Amendment certification. A microarray-based (m-) random forest ERG classification model was trained and validated. Outlier expression analysis was used to predict other mutually exclusive non-ERG ETS gene rearrangements (ETS(+)) or SPINK1 overexpression (SPINK1(+)). Associations with clinical features and outcomes by multivariate logistic regression analysis and receiver operating curves. The m-ERG classifier showed 95% accuracy in an independent validation subset (155 samples). Across cohorts, 45% of PCas were classified as m-ERG(+), 9% as m-ETS(+), 8% as m-SPINK1(+), and 38% as triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Gene expression profiling supports three underlying molecularly defined groups: m-ERG(+), m-ETS(+), and m-SPINK1(+)/triple negative. On multivariate analysis, m-ERG(+) tumors were associated with lower preoperative serum prostate-specific antigen and Gleason scores, but greater extraprostatic extension (p<0.001). m-ETS(+) tumors were associated with seminal vesicle invasion (p=0.01), while m-SPINK1(+)/triple negative tumors had higher Gleason scores and were more frequent in Black/African American patients (p<0.001). Clinical outcomes were not significantly different among subtypes. A clinically available prognostic test (Decipher) can also assess PCa molecular subtypes, obviating the need for additional testing. Clinicopathologic differences were found among subtypes based on global expression patterns. Molecular subtyping of prostate cancer can be achieved using extra data generated from a clinical-grade, genome-wide expression-profiling prognostic assay (Decipher). Transcriptomic and clinical analysis support three distinct molecular subtypes: (1) m-ERG(+), (2) m-ETS(+), and (3) m-SPINK1(+)/triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Incorporation of subtyping into a clinically available assay may facilitate additional applications beyond routine prognosis. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Oncogenous osteomalacia and myopericytoma of the thoracic spine: a case report.

PubMed

Brunschweiler, Benoit; Guedj, Nathalie; Lenoir, Thibault; Faillot, Thierry; Rillardon, Ludovic; Guigui, Pierre

2009-11-01

A case report. To illustrate a rare case of oncogenous osteomalacia caused by a spinal thoracic myopericytoma. Osteomalacia related to a tumor is well known. The cause of the disorder is usually a highly vascularized, benign tumor of mesenchymal origin. Location of the tumor in the spine is very rare. Removal of the tumor is followed by resolution of osteomalacia. Diagnosis of oseomalacia was established on the presence of cardinal clinical, biologic, and radiologic features of osteomalacia. Localization of the tumor at T5 and T6 levels was obtained by magnetic resonance imaging. Surgical treatment consisted in a circumferential correction-fusion with hemivertebrectomy of T5 and T6 and tumor removal. Tumor removal was rapidly followed by disappearance of the clinical symptoms of osteomalacia, and by correction of hypophosphatemia. At 2-years follow-up, no recurrence of the tumor was detectable on imaging studies-the correction fusion remained stable. Histologically, the tumor was classified as a myopericytoma. There was no relapse of the clinical features of osteomalacia. However, secondary recurrence of the biologic markers due to an incomplete tumor removal was disclosed. Removal of the tumor was followed by healing of the clinical features of osteomalacia, demonstrating the causal connection between the myopericytoma and the osteopathy.

Distinct tumor protein p53 mutants in breast cancer subgroups.

PubMed

Dumay, Anne; Feugeas, Jean-Paul; Wittmer, Evelyne; Lehmann-Che, Jacqueline; Bertheau, Philippe; Espié, Marc; Plassa, Louis-François; Cottu, Paul; Marty, Michel; André, Fabrice; Sotiriou, Christos; Pusztai, Lajos; de Thé, Hugues

2013-03-01

Tumor protein p53 (TP53) is mutated in approximately 30% of breast cancers, but this frequency fluctuates widely between subclasses. We investigated the p53 mutation status in 572 breast tumors, classified into luminal, basal and molecular apocrine subgroups. As expected, the lowest mutation frequency was observed in luminal (26%), and the highest in basal (88%) tumors. Luminal tumors showed significantly higher frequency of substitutions (82 vs. 65%), notably A/T to G/C transitions (31 vs. 15%), whereas molecular apocrine and basal tumors presented much higher frequencies of complex mutations (deletions/insertions) (36 and 33%, respectively, vs. 18%). Accordingly, missense mutations were significantly more frequent in luminal tumors (75 vs. 54%), whereas basal tumors displayed significantly increased rates of TP53 truncations (43 vs. 25%), resulting in loss of function and/or expression. Interestingly, as basal tumors, molecular apocrine tumors presented with a high rate of complex mutations, but paradoxically, these were not associated with increased frequency of p53 truncation. As in luminal tumors, this could reflect a selective pressure for p53 gain of function, possibly through P63/P73 inactivation. Collectively, these observations point not only to different mechanisms of TP53 alterations, but also to different functional consequences in the different breast cancer subtypes. Copyright © 2012 UICC.

Prediction of tumor differentiation using sequential PET/CT and MRI in patients with breast cancer.

PubMed

Choi, Joon Ho; Lim, Ilhan; Noh, Woo Chul; Kim, Hyun-Ah; Seong, Min-Ki; Jang, Seonah; Seol, Hyesil; Moon, Hansol; Byun, Byung Hyun; Kim, Byung Il; Choi, Chang Woon; Lim, Sang Moo

2018-05-23

The aim of this study is to assess tumor differentiation using parameters from sequential positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in patients with breast cancer. This retrospective study included 78 patients with breast cancer. All patients underwent sequential PET/CT and MRI. For fluorodeoxyglucose (FDG)-PET image analysis, the maximum standardized uptake value (SUV max ) of FDG was assessed at both 1 and 2 h and metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The kinetic analysis of dynamic contrast-enhanced MRI parameters was performed using dynamic enhancement curves. We assessed diffusion-weighted imaging (DWI)-MRI parameters regarding apparent diffusion coefficient (ADC) values. Histologic grades 1 and 2 were classified as low-grade, and grade 3 as high-grade tumor. Forty-five lesions of 78 patients were classified as histologic grade 3, while 26 and 7 lesions were grade 2 and grade 1, respectively. Patients with high-grade tumors showed significantly lower ADC-mean values than patients with low-grade tumors (0.99 ± 0.19 vs.1.12 ± 0.32, p = 0.007). With respect to SUV max 1, MTV2.5, and TLG2.5, patients with high-grade tumors showed higher values than patients with low-grade tumors: SUV max 1 (7.92 ± 4.5 vs.6.19 ± 3.05, p = 0.099), MTV2.5 (7.90 ± 9.32 vs.4.38 ± 5.10, p = 0.095), and TLG2.5 (40.83 ± 59.17 vs.19.66 ± 26.08, p = 0.082). However, other parameters did not reveal significant differences between low-grade and high-grade malignancies. In receiver-operating characteristic (ROC) curve analysis, ADC-mean values showed the highest area under the curve of 0.681 (95%CI 0.566-0.782) for assessing high-grade malignancy. Lower ADC-mean values may predict the poor differentiation of breast cancer among diverse PET-MRI functional parameters.

Trimodal spectra for high discrimination of benign and malignant prostate tissue

NASA Astrophysics Data System (ADS)

Al Salhi, Mohamad; Masilamani, Vadivel; Trinka, Vijmasi; Rabah, Danny; Al Turki, Mohammed R.

2011-02-01

High false positives and over diagnosis is a major problem with management of prostate cancer. A non-invasive or a minimally invasive technique to accurately distinguish malignant prostate cancers from benign tumors will be extremely helpful to overcome this problem. In this paper, we had used three different fluorescence spectroscopy techniques viz., Fluorescence Emission Spectrum (FES), Stokes' Shift Spectrum (SSS) and Reflectance Spectrum (RS) to discriminate benign prostate tumor tissues (N=12) and malignant prostate cancer tissues (N=8). These fluorescence techniques were used to determine the relative concentration of naturally occurring biomolecules such as tryptophan, elastin, NADH and flavin which are found to be out of proportion in cancer tissues. Our studies show that combining all three techniques, benign and malignant prostate tissues could be classified with accuracy greater than 90%. This preliminary report is based on in vitro spectroscopy analysis. However, by employing fluorescence endoscopy techniques, this can be extended to in vivo analysis as well. This technique has the potential to identify malignant prostate tissues without surgery.

The Yin and Yang of Innate Lymphoid Cells in Cancer.

PubMed

Carrega, Paolo; Campana, Stefania; Bonaccorsi, Irene; Ferlazzo, Guido

2016-11-01

The recent appreciation of novel subsets of innate lymphoid cells (ILCs) as important regulators of tissue homeostasis, inflammation and repair, raise questions regarding the presence and role of these cells in cancer tissues. In addition to natural killer and fetal lymphoid tissue inducer (LTi) cells, the ILC family comprises non-cytolytic, cytokine-producing cells that are classified into ILC1, ILC2 and ILC3 based on phenotypic and functional characteristics. Differently from natural killer cells, which are the prototypical members of ILC1 and whose role in tumors is better established, the involvement of other ILC subsets in cancer progression or resistance is still fuzzy and in several instances controversial, since current studies indicate both context-dependent beneficial or pathogenic effects. Here, we review the current knowledge regarding the involvement of these novel ILC subsets in the context of tumor immunology, highlighting how ILC subsets might behave either as friends or foes. Copyright © 2016 European Federation of Immunological Societies. Published by Elsevier B.V. All rights reserved.

Proteomic data analysis of glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques

NASA Astrophysics Data System (ADS)

Lespinats, Sylvain; Pinker-Domenig, Katja; Wengert, Georg; Houben, Ivo; Lobbes, Marc; Stadlbauer, Andreas; Meyer-Bäse, Anke

2016-05-01

Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic data sets requires the development of new data mining and visualization approaches. Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies in the application of novel approaches for the visualization, clustering and projection representation to unveil hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved clustering and visualization results provide a more detailed insight into the protein-level fold changes and putative upstream regulators for the GSCs. However the extracted molecular information is insufficient in classifying GSCs and paving the pathway to an improved therapeutics of the heterogeneous glioma.

Death decoy receptor overexpression and increased malignancy risk in colorectal cancer.

PubMed

Zong, Liang; Chen, Ping; Wang, Da-Xin

2014-04-21

To evaluate human epidermal growth factor receptor 2 (HER2) and death decoy receptor (DcR3) as colorectal cancer prognostic indicators. Colorectal carcinoma specimens from 300 patients were analyzed by immunohistochemistry to detect the staining patterns of HER2 and DcR3. Classification of HER2 staining was carried out using the United States Food and Drug Administration semi-quantitative scoring system, with scores of 0 or 1+ indicating a tumor-negative (normal expression) status and scores of 2+ and 3+ indicating a tumor-positive (overexpression) status. Classification of DcR3 was carried out by quantitating the percentage of positive cells within the stained section, with < 10% indicating a tumor-negative status and ≥ 10% indicating a tumor-positive status. Correlation of the HER2 and DcR3 staining status with clinicopathological parameters [age, sex, tumor size, differentiation, and the tumor, node, metastasis (pTNM) classification] and survival was statistically assessed. Tumor-positive status for HER2 and DcR3 was found in 18.33% and 58.33% of the 300 colorectal carcinoma specimens, respectively. HER2 tumor-positive status showed a significant correlation with tumor size (P = 0.003) but not with other clinicopathological parameters. DcR3 tumor-positive status showed a significant correlation with tumor differentiation (P < 0.001), pTNM stage (P < 0.001), and lymph node metastasis (P < 0.001). However, correlation coefficient analysis did not indicate that a statistically significant correlation exists between tumor-positive status for the HER2 and DcR3 overexpression (P = 0.236). Patients with specimens classified as DcR3-overexpressing had a significantly worse overall survival (OS) rate than those without DcR3 overexpression (median OS: 42.11 vs 61.21 mo; HR = 50.27, 95%CI: 44.90-55.64, P < 0.001). HER2 overexpression had no significant impact on median OS (35.10 mo vs 45.25 mo; HR = 44.40, 95%CI: 39.32-49.48, P = 0.344). However, patients with specimens classified as both HER2- and DcR3-overexpressing had a significantly poorer median OS than those with only HER2 overexpression (31.80 mo vs 52.20 mo; HR = 35.10, 95%CI: 22.04-48.16, P = 0.006). HER2 overexpression is not an independent prognostic marker of colorectal cancer, but DcR3 overexpression is highly correlated with lymph node metastasis and poor OS.

Are 10-, 10-12-, or > 12-mm prostate biopsy core quality control cutoffs reasonable?

PubMed

Sanches, Brunno C F; Lalli, Ana Luiza; Azal Neto, Wilmar; Billis, Athanase; Reis, Leonardo Oliveira

2018-07-01

To explore the role of prostate biopsy core length on prediction of index tumor clinical significance and localization on radical prostatectomy (RP) and time to recurrence, hypothesizing 10-, 10-12-, or > 12-mm minimum core as potential biopsy quality control. Assessed 2424 prostate biopsy cores and corresponding RP of 202 patients submitted to the first set of 12 cores prostate biopsy between 2010 and 2015. Analyzed biopsy core length, age, prostate volume (PV), free and total PSA ratio, PSA density, RP index tumor clinical significance, extension, localization, surgical margins, and cancer control. Prostate biopsy confronted to surgical specimens defined Gleason grade-grouping system (1-5) agreement. Median age was 63.7 years, PSA 10.1 ng/dl, PSA density 28%, and mean follow-up 5 years. Recurrence was identified in 64 (31.7%) patients and predicted by PSA > 10 at time of diagnosis (p = 0.008), seminal vesicle invasion (p = 0.0019), core tumor percentage (p = 0.033), and tumor localization predominantly in the prostate base (p = 0017). The mean core length was longer in index tumor positive cores (p = 0.043) and in tumors classified as clinically insignificant (p = 0.011), without impact on tumor localization (basal vs apical p = 0.592; left vs. right p = 0.320). Biopsy core length categories (≤ 10, 10-12 and > 12 mm) did not significantly impact Gleason grade-grouping agreement or time to recurrence (p > 0.05). Core length was not significantly different in all Gleason grade-groupings 1-5 (p = 0.312). Prostate biopsy core length impacts tumor characterization; however, 10 mm minimum core length and even 10-12- and > 12-mm categories failed as a biopsy quality control in our data.

Group 3 medulloblastoma in a patient with a GYS2 germline mutation and glycogen storage disease 0a.

PubMed

Holsten, Till; Tsiakas, Konstantinos; Kordes, Uwe; Bison, Brigitte; Pietsch, Torsten; Rutkowski, Stefan; Santer, René; Schüller, Ulrich

2018-03-01

Glycogen storage disease (GSD) 0a is a rare congenital metabolic disease with symptoms in infancy and childhood caused by biallelic GYS2 germline variants. A predisposition to cancer has not been described yet. We report here a boy with GSD 0a, who developed a malignant brain tumor at the age of 4.5 years. The tumor was classified as a group 3 medulloblastoma, and the patient died from cancer 27 months after initial tumor diagnosis. This case appears interesting as group 3 medulloblastoma is so far not known to arise in hereditary syndromes and the biology of sporadic group 3 medulloblastoma is largely unknown.

SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer.

PubMed

Petricoin, Emanuel F; Liotta, Lance A

2004-02-01

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.

Computational assessment of deep-seated tumor treatment capability of the 9Be(d,n)10B reaction for accelerator-based boron neutron capture therapy (AB-BNCT).

PubMed

Capoulat, M E; Minsky, D M; Kreiner, A J

2014-03-01

The 9Be(d,n)10B reaction was studied as an epithermal neutron source for brain tumor treatment through Boron Neutron Capture Therapy (BNCT). In BNCT, neutrons are classified according to their energies as thermal (<0.5 eV), epithermal (from 0.5 eV to 10 keV) or fast (>10 keV). For deep-seated tumors epithermal neutrons are needed. Since a fraction of the neutrons produced by this reaction are quite fast (up to 5-6 MeV, even for low-bombarding energies), an efficient beam shaping design is required. This task was carried out (1) by selecting the combinations of bombarding energy and target thickness that minimize the highest-energy neutron production; and (2) by the appropriate choice of the Beam Shaping Assembly (BSA) geometry, for each of the combinations found in (1). The BSA geometry was determined as the configuration that maximized the dose deliverable to the tumor in a 1 h treatment, within the constraints imposed by the healthy tissue dose adopted tolerance. Doses were calculated through the MCNP code. The highest dose deliverable to the tumor was found for an 8 μm target and a deuteron beam of 1.45 MeV. Tumor weighted doses ≥40 Gy can be delivered up to about 5 cm in depth, with a maximum value of 51 Gy at a depth of about 2 cm. This dose performance can be improved by relaxing the treatment time constraint and splitting the treatment into two 1-h sessions. These good treatment capabilities strengthen the prospects for a potential use of this reaction in BNCT. Copyright © 2013 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Tumor Content Chart-Assisted HER2/CEP17 Digital PCR Analysis of Gastric Cancer Biopsy Specimens.

PubMed

Matsusaka, Keisuke; Ishikawa, Shumpei; Nakayama, Atsuhito; Ushiku, Tetsuo; Nishimoto, Aiko; Urabe, Masayuki; Kaneko, Nobuyuki; Kunita, Akiko; Kaneda, Atsushi; Aburatani, Hiroyuki; Fujishiro, Mitsuhiro; Seto, Yasuyuki; Fukayama, Masashi

2016-01-01

Evaluating HER2 gene amplification is an essential component of therapeutic decision-making for advanced or metastatic gastric cancer. A simple method that is applicable to small, formalin-fixed, paraffin-embedded biopsy specimens is desirable as an adjunct to or as a substitute for currently used HER2 immunohistochemistry and in situ hybridization protocols. In this study, we developed a microfluidics-based digital PCR method for determining HER2 and chromosome 17 centromere (CEP17) copy numbers and estimating tumor content ratio (TCR). The HER2/CEP17 ratio is determined by three variables-TCR and absolute copy numbers of HER2 and CEP17-by examining tumor cells; only the ratio of the latter two can be obtained by digital PCR using the whole specimen without purifying tumor cells. TCR was determined by semi-automatic image analysis. We developed a Tumor Content chart, which is a plane of rectangular coordinates consisting of HER2/CEP17 digital PCR data and TCR that delineates amplified, non-amplified, and equivocal areas. By applying this method, 44 clinical gastric cancer biopsy samples were classified as amplified (n = 13), non-amplified (n = 25), or equivocal (n = 6). By comparison, 11 samples were positive, 11 were negative, and 22 were equivocally immunohistochemistry. Thus, our novel method reduced the number of equivocal samples from 22 to 6, thereby obviating the need for confirmation by fluorescence or dual-probe in situ hybridization to < 30% of cases. Tumor content chart-assisted digital PCR analysis is also applicable to multiple sites in surgically resected tissues. These results indicate that this analysis is a useful alternative to HER2 immunohistochemistry in gastric cancers that can serve as a basis for the automated evaluation of HER2 status.

Poor Prognosis of Lower Inner Quadrant in Lymph Node-negative Breast Cancer Patients Who Received No Chemotherapy: A Study Based on Nationwide Korean Breast Cancer Registry Database.

PubMed

Hwang, Ki-Tae; Kim, Jongjin; Kim, Eun-Kyu; Jung, Sung Hoo; Sohn, Guiyun; Kim, Seung Il; Jeong, Joon; Lee, Hyouk Jin; Park, Jin Hyun; Oh, Sohee

2017-07-01

We aimed to investigate the prognostic influence of primary tumor site on the survival of patients with breast cancer. Data of 63,388 patients with primary breast cancer from the Korean Breast Cancer Registry were analyzed. Primary tumor sites were classified into 5 groups: upper outer quadrant, lower outer quadrant, upper inner quadrant, lower inner quadrant (LIQ), and central portion. We analyzed overall survival (OS) and breast cancer-specific survival (BCSS) according to primary tumor site. Central portion and LIQ showed lower survival rates regarding both OS and BCSS compared with the other 3 quadrants (all P < .05) and hazard ratios were 1.267 (95% CI, 1.180-1.360, P < .001) and 1.215 (95% CI, 1.097-1.345, P < .001), respectively. Although central portion showed more unfavorable clinicopathologic features, LIQ showed more favorable features than the other 3 quadrants. Primary tumor site was a significant factor in univariate and multivariate analyses for OS and BCSS (all P < .001). For lymph node-negative patients, LIQ showed a worse OS than the other primary tumor sites in the subgroup with no chemotherapy (P < .001), but that effect disappeared in the subgroup with chemotherapy (P = .058). LIQ showed a worse prognosis despite having more favorable clinicopathologic features than other tumor locations and it was more prominent for lymph node-negative patients who received no chemotherapy. The hypothesis of possible hidden internal mammary node metastasis could be suggested to play a key role in LIQ lesions. Copyright © 2017 Elsevier Inc. All rights reserved.

Tumor Content Chart-Assisted HER2/CEP17 Digital PCR Analysis of Gastric Cancer Biopsy Specimens

PubMed Central

Matsusaka, Keisuke; Ishikawa, Shumpei; Nakayama, Atsuhito; Ushiku, Tetsuo; Nishimoto, Aiko; Urabe, Masayuki; Kaneko, Nobuyuki; Kunita, Akiko; Kaneda, Atsushi; Aburatani, Hiroyuki; Fujishiro, Mitsuhiro; Seto, Yasuyuki; Fukayama, Masashi

2016-01-01

Evaluating HER2 gene amplification is an essential component of therapeutic decision-making for advanced or metastatic gastric cancer. A simple method that is applicable to small, formalin-fixed, paraffin-embedded biopsy specimens is desirable as an adjunct to or as a substitute for currently used HER2 immunohistochemistry and in situ hybridization protocols. In this study, we developed a microfluidics-based digital PCR method for determining HER2 and chromosome 17 centromere (CEP17) copy numbers and estimating tumor content ratio (TCR). The HER2/CEP17 ratio is determined by three variables—TCR and absolute copy numbers of HER2 and CEP17—by examining tumor cells; only the ratio of the latter two can be obtained by digital PCR using the whole specimen without purifying tumor cells. TCR was determined by semi-automatic image analysis. We developed a Tumor Content chart, which is a plane of rectangular coordinates consisting of HER2/CEP17 digital PCR data and TCR that delineates amplified, non-amplified, and equivocal areas. By applying this method, 44 clinical gastric cancer biopsy samples were classified as amplified (n = 13), non-amplified (n = 25), or equivocal (n = 6). By comparison, 11 samples were positive, 11 were negative, and 22 were equivocally immunohistochemistry. Thus, our novel method reduced the number of equivocal samples from 22 to 6, thereby obviating the need for confirmation by fluorescence or dual-probe in situ hybridization to < 30% of cases. Tumor content chart-assisted digital PCR analysis is also applicable to multiple sites in surgically resected tissues. These results indicate that this analysis is a useful alternative to HER2 immunohistochemistry in gastric cancers that can serve as a basis for the automated evaluation of HER2 status. PMID:27119558

Treatment Results and Prognostic Indicators in Thymic Epithelial Tumors: A Clinicopathological Analysis of 45 Patients

PubMed Central

Ansari, Mansour; Dehsara, Farzin; Mohammadianpanah, Mohammad; Mosalaei, Ahmad; Omidvari, Shapour; Ahmadloo, Niloofar

2014-01-01

Background: Thymomas are rare epithelial tumors arising from thymus gland. This study aims at investigating the clinical presentation, prognostic factors and treatment outcome of forty five patients with thymoma and thymic carcinoma. Methods: Forty-five patients being histologically diagnosed with thymoma or thymic carcinoma that were treated and followed-up at a tertiary academic hospital during January 1987 and December 2008 were selected for the present study. Twelve patients were solely treated with surgery, 14 with surgery followed by adjuvant radiotherapy, 12 with sequential combined treatment of surgery, radiotherapy and/or chemotherapy and 7 with non-surgical approach including radiotherapy and/or chemotherapy.  Tumors were classified based on the new World Health Organization (WHO) histological classification. Results: There were 18 women and 27 men with a median age of 43 years. Twelve patients (26.7%) had stage I, 7 (17.8%) had stage II, 23 (51%) had stage III and 2 (4.5%) had stage IV disease. Tumors types were categorized as type A (n=4), type AB (n=10), type B1 (n=9), type B2 (n=10), type B3 (n=5) and type C (n=7). In univariate analysis for overall survival, disease stage (P=0.001), tumor size (P=0.017) and the extent of surgical resection (P<0.001) were prognostic factors. Regarding the multivariate analysis, only the extent of the surgical resection (P<0.001) was the independent prognostic factor and non-surgical treatment had a negative influence on the survival. The 5-year and 10-year overall survival rates were 70.8% and 62.9%, respectively. Conclusion: Complete surgical resection is the most important prognostic factor in patients with thymic epithelial tumors. PMID:25031486

Treatment results and prognostic indicators in thymic epithelial tumors: a clinicopathological analysis of 45 patients.

PubMed

Ansari, Mansour; Dehsara, Farzin; Mohammadianpanah, Mohammad; Mosalaei, Ahmad; Omidvari, Shapour; Ahmadloo, Niloofar

2014-07-01

Thymomas are rare epithelial tumors arising from thymus gland. This study aims at investigating the clinical presentation, prognostic factors and treatment outcome of forty five patients with thymoma and thymic carcinoma. Forty-five patients being histologically diagnosed with thymoma or thymic carcinoma that were treated and followed-up at a tertiary academic hospital during January 1987 and December 2008 were selected for the present study. Twelve patients were solely treated with surgery, 14 with surgery followed by adjuvant radiotherapy, 12 with sequential combined treatment of surgery, radiotherapy and/or chemotherapy and 7 with non-surgical approach including radiotherapy and/or chemotherapy.  Tumors were classified based on the new World Health Organization (WHO) histological classification. There were 18 women and 27 men with a median age of 43 years. Twelve patients (26.7%) had stage I, 7 (17.8%) had stage II, 23 (51%) had stage III and 2 (4.5%) had stage IV disease. Tumors types were categorized as type A (n=4), type AB (n=10), type B1 (n=9), type B2 (n=10), type B3 (n=5) and type C (n=7). In univariate analysis for overall survival, disease stage (P=0.001), tumor size (P=0.017) and the extent of surgical resection (P<0.001) were prognostic factors. Regarding the multivariate analysis, only the extent of the surgical resection (P<0.001) was the independent prognostic factor and non-surgical treatment had a negative influence on the survival. The 5-year and 10-year overall survival rates were 70.8% and 62.9%, respectively. Complete surgical resection is the most important prognostic factor in patients with thymic epithelial tumors.

Multiplexed Immunofluorescence Reveals Potential PD-1/PD-L1 Pathway Vulnerabilities in Craniopharyngioma.

PubMed

Coy, Shannon; Rashid, Rumana; Lin, Jia-Ren; Du, Ziming; Donson, Andrew M; Hankinson, Todd C; Foreman, Nicholas K; Manley, Peter E; Kieran, Mark W; Reardon, David A; Sorger, Peter K; Santagata, Sandro

2018-03-02

Craniopharyngiomas are neoplasms of the sellar/parasellar region that are classified into adamantinomatous (ACP) and papillary (PCP) subtypes. Surgical resection of craniopharyngiomas is challenging, and recurrence is common, frequently leading to profound morbidity. BRAF V600E mutations render PCP susceptible to BRAF/MEK inhibitors, but effective targeted therapies are needed for ACP. We explored the feasibility of targeting the PD-1/PD-L1 immune checkpoint pathway in ACP and PCP. We mapped and quantified PD-L1 and PD-1 expression in ACP and PCP resections using immunohistochemistry, immunofluorescence, and RNA in situ hybridization. We used tissue-based cyclic immunofluorescence (t-CyCIF) to map the spatial distribution of immune cells and characterize cell cycle and signaling pathways in ACP tumor cells which intrinsically express PD-1. All ACP (15±14% of cells, n=23, average±S.D.) and PCP (35±22% of cells, n=18) resections expressed PD-L1. In ACP, PD-L1 was predominantly expressed by tumor cells comprising the cyst-lining. In PCP, PD-L1 was highly-expressed by tumor cells surrounding the stromal fibrovascular cores. ACP also exhibited tumor cell-intrinsic PD-1 expression in whorled epithelial cells with nuclear-localized beta-catenin. These cells exhibited evidence of elevated mTOR and MAPK signaling. Profiling of immune populations in ACP and PCP showed a modest density of CD8+ T-cells. ACP exhibit PD-L1 expression in the tumor cyst-lining and intrinsic PD-1 expression in cells proposed to comprise an oncogenic stem-like population. In PCP, proliferative tumor cells express PD-L1 in a continuous band at the stromal-epithelial interface. Targeting PD-L1 and/or PD-1 in both subtypes of craniopharyngioma might therefore be an effective therapeutic strategy.

CpG Island Methylator Phenotype Positive Tumors in the Absence of MLH1 Methylation Constitute a Distinct Subset of Duodenal Adenocarcinomas and Are Associated with Poor Prognosis

PubMed Central

Fu, Tao; Pappou, Emmanouil P.; Guzzetta, Angela A.; Jeschke, Jana; Kwak, Ruby; Dave, Pujan; Hooker, Craig M.; Morgan, Richard; Baylin, Stephen B.; Iacobuzio-Donahue, Christine A.; Wolfgang, Christopher L.; Ahuja, Nita

2012-01-01

Purpose Little information is available on genetic and epigenetic changes in duodenal adenocarcinomas. The purpose was to identify possible subsets of duodenal adenocarcinomas based on microsatellite instability (MSI), DNA methylation, mutations in the KRAS and BRAF genes, clinicopathologic features, and prognosis. Experimental Design Demographics, tumor characteristics and survival were available for 99 duodenal adenocarcinoma patients. Testing for KRAS and BRAF mutations, MSI, MLH1 methylation and CpG island methylator phenotype (CIMP) status was performed. A Cox proportional hazard model was built to predict survival. Results CIMP+ was detected in 27 of 99 (27.3%) duodenal adenocarcinomas, and was associated with MSI (P = 0.011) and MLH1 methylation (P < 0.001), but not with KRAS mutations (P = 0.114), as compared to CIMP− tumors. No BRAF V600E mutation was detected. Among the CIMP+ tumors, 15 (55.6%) were CIMP+/MLH1-unmethylated (MLH1-U). Kaplan-Meier analysis showed tumors classified by CIMP, CIMP/MLH1 methylation status or CIMP/MSI status could predict overall survival (OS; P = 0.047, 0.002, and 0.002, respectively), while CIMP/MLH1 methylation status could also predict time-to-recurrence (TTR; P = 0.016). In multivariate analysis, CIMP/MLH1 methylation status showed a significant prognostic value regarding both OS (P < 0.001) and TTR (P = 0.023). Patients with CIMP+/MLH1-U tumors had the worst OS and TTR. Conclusions Our results demonstrate existence of CIMP in duodenal adenocarcinomas. The combination of CIMP+/MLH1-U appears to be independently associated with poor prognosis in patients with duodenal adenocarcinomas. This study also suggests that BRAF mutations are not involved in duodenal tumorigenesis, MSI or CIMP development. PMID:22825585

Alternate Metabolic Programs Define Regional Variation of Relevant Biological Features in Renal Cell Carcinoma Progression.

PubMed

Brooks, Samira A; Khandani, Amir H; Fielding, Julia R; Lin, Weili; Sills, Tiffany; Lee, Yueh; Arreola, Alexandra; Milowsky, Mathew I; Wallen, Eric M; Woods, Michael E; Smith, Angie B; Nielsen, Mathew E; Parker, Joel S; Lalush, David S; Rathmell, W Kimryn

2016-06-15

Clear cell renal cell carcinoma (ccRCC) has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We have recently employed the novel PET/magnetic resonance (MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans. ccRCC patients underwent PET/MR imaging, and these images subsequently used to identify areas of varied intensity for sampling. Samples from 8 patients were subjected to histologic, immunohistochemical, and microarray analysis. Tumor subsamples displayed a range of heterogeneity for common features of hypoxia-inducible factor expression and microvessel density, as well as for features closely linked to metabolic processes, such as GLUT1 and FBP1. In addition, gene signatures linked with disease risk (ccA and ccB) also demonstrated variable heterogeneity, with most tumors displaying a dominant panel of features across the sampled regions. Intriguingly, the ccA- and ccB-classified samples corresponded with metabolic features and functional imaging levels. These correlations further linked a variety of metabolic pathways (i.e., the pentose phosphate and mTOR pathways) with the more aggressive, and glucose avid ccB subtype. Higher tumor dependency on exogenous glucose accompanies the development of features associated with the poor risk ccB subgroup. Linking these panels of features may provide the opportunity to create functional maps to enable enhanced visualization of the heterogeneous biologic processes of an individual's disease. Clin Cancer Res; 22(12); 2950-9. ©2016 AACR. ©2016 American Association for Cancer Research.

The prognostic efficacy and improvements of the 7th edition Union for International Cancer Control tumor-node-metastasis classifications for Chinese patients with gastric cancer: Results based on a retrospective three-decade population study.

PubMed

Gu, Huizi; Li, Dongmei; Zhu, Haitao; Zhang, Hao; Yu, Ying; Qin, Dongxue; Yi, Mei; Li, Xiang; Lu, Ping

2017-03-01

This study aimed to evaluate survival trends for patients with gastric cancer in northeast China in the most recent three decades and analyze the applicability of the UICC tumor-node-metastasis (TNM) classification 7th edition for Chinese patients with gastric cancer. A review of all inpatient and outpatient records of patients with gastric cancer was conducted in the first hospital of China Medical University and the Liaoning Cancer Hospital and Institute. All patients who met the inclusion criteria and were seen from January 1980 through December 2009 were included in the study. The primary outcome was 5-year survival, which was analyzed according to decade of diagnosis and TNM classifications. From 1980 through 2009, the 5-year survival rates for patients with gastric cancer (n=2414) increased from 39.1% to 57.3%. Decade of diagnosis was significantly associated with patient survival (p = 0.013), and the 5-year survival rate in the 2000s was remarkably higher than that in the 1980s and 1990s (p = 0.004 and 0.049, respectively). When classified according to the UICC TNM classification of gastric cancer 7th edition, the prognoses of stage IIIA and stage IIIB patients were not significantly different (p = 0.077). However, if stage T4b and stage N0 patients were classified as stage IIIA, the prognoses of stage IIIA and stage IIIB patients were significantly different (p < 0.001). Hence, there was a significant difference in survival during the three time periods in Northeast China. Classifying stage T4b and stage N0 patients as stage IIIA according to the 7th edition of UICC gastric cancer TNM classifications better stratified Chinese patients and predicted prognoses.

Classification algorithm of ovarian tissue based on co-registered ultrasound and photoacoustic tomography

NASA Astrophysics Data System (ADS)

Li, Hai; Kumavor, Patrick D.; Alqasemi, Umar; Zhu, Quing

2014-03-01

Human ovarian tissue features extracted from photoacoustic spectra data, beam envelopes and co-registered ultrasound and photoacoustic images are used to characterize cancerous vs. normal processes using a support vector machine (SVM) classifier. The centers of suspicious tumor areas are estimated from the Gaussian fitting of the mean Radon transforms of the photoacoustic image along 0 and 90 degrees. Normalized power spectra are calculated using the Fourier transform of the photoacoustic beamformed data across these suspicious areas, where the spectral slope and 0-MHz intercepts are extracted. Image statistics, envelope histogram fitting and maximum output of 6 composite filters of cancerous or normal patterns along with other previously used features are calculated to compose a total of 17 features. These features are extracted from 169 datasets of 19 ex vivo ovaries. Half of the cancerous and normal datasets are randomly chosen to train a SVM classifier with polynomial kernel and the remainder is used for testing. With 50 times data resampling, the SVM classifier, for the training group, gives 100% sensitivity and 100% specificity. For the testing group, it gives 89.68+/- 6.37% sensitivity and 93.16+/- 3.70% specificity. These results are superior to those obtained earlier by our group using features extracted from photoacoustic raw data or image statistics only.

Automated Classification of Pathology Reports.

PubMed

Oleynik, Michel; Finger, Marcelo; Patrão, Diogo F C

2015-01-01

This work develops an automated classifier of pathology reports which infers the topography and the morphology classes of a tumor using codes from the International Classification of Diseases for Oncology (ICD-O). Data from 94,980 patients of the A.C. Camargo Cancer Center was used for training and validation of Naive Bayes classifiers, evaluated by the F1-score. Measures greater than 74% in the topographic group and 61% in the morphologic group are reported. Our work provides a successful baseline for future research for the classification of medical documents written in Portuguese and in other domains.

Gene Expression (mRNA) Markers for Differentiating between Malignant and Benign Follicular Thyroid Tumours

PubMed Central

Wojtas, Bartosz; Pfeifer, Aleksandra; Oczko-Wojciechowska, Malgorzata; Krajewska, Jolanta; Czarniecka, Agnieszka; Kukulska, Aleksandra; Eszlinger, Markus; Musholt, Thomas; Stokowy, Tomasz; Swierniak, Michal; Stobiecka, Ewa; Chmielik, Ewa; Rusinek, Dagmara; Tyszkiewicz, Tomasz; Halczok, Monika; Hauptmann, Steffen; Lange, Dariusz; Jarzab, Michal; Paschke, Ralf; Jarzab, Barbara

2017-01-01

Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes (CPQ, PLVAP, TFF3, ACVRL1, ZFYVE21, FAM189A2, and CLEC3B). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes (CPQ, PLVAP, TFF3, ACVRL1). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material. PMID:28574441

Gene Expression (mRNA) Markers for Differentiating between Malignant and Benign Follicular Thyroid Tumours.

PubMed

Wojtas, Bartosz; Pfeifer, Aleksandra; Oczko-Wojciechowska, Malgorzata; Krajewska, Jolanta; Czarniecka, Agnieszka; Kukulska, Aleksandra; Eszlinger, Markus; Musholt, Thomas; Stokowy, Tomasz; Swierniak, Michal; Stobiecka, Ewa; Chmielik, Ewa; Rusinek, Dagmara; Tyszkiewicz, Tomasz; Halczok, Monika; Hauptmann, Steffen; Lange, Dariusz; Jarzab, Michal; Paschke, Ralf; Jarzab, Barbara

2017-06-02

Distinguishing between follicular thyroid cancer (FTC) and follicular thyroid adenoma (FTA) constitutes a long-standing diagnostic problem resulting in equivocal histopathological diagnoses. There is therefore a need for additional molecular markers. To identify molecular differences between FTC and FTA, we analyzed the gene expression microarray data of 52 follicular neoplasms. We also performed a meta-analysis involving 14 studies employing high throughput methods (365 follicular neoplasms analyzed). Based on these two analyses, we selected 18 genes differentially expressed between FTA and FTC. We validated them by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent set of 71 follicular neoplasms from formaldehyde-fixed paraffin embedded (FFPE) tissue material. We confirmed differential expression for 7 genes ( CPQ , PLVAP , TFF3 , ACVRL1 , ZFYVE21 , FAM189A2 , and CLEC3B ). Finally, we created a classifier that distinguished between FTC and FTA with an accuracy of 78%, sensitivity of 76%, and specificity of 80%, based on the expression of 4 genes ( CPQ , PLVAP , TFF3 , ACVRL1 ). In our study, we have demonstrated that meta-analysis is a valuable method for selecting possible molecular markers. Based on our results, we conclude that there might exist a plausible limit of gene classifier accuracy of approximately 80%, when follicular tumors are discriminated based on formalin-fixed postoperative material.

"Radio-oncomics" : The potential of radiomics in radiation oncology.

PubMed

Peeken, Jan Caspar; Nüsslin, Fridtjof; Combs, Stephanie E

2017-10-01

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Clinical usefulness of magnifying endoscopy for non-ampullary duodenal tumors.

PubMed

Mizumoto, Takeshi; Sanomura, Yoji; Tanaka, Shinji; Kuroki, Kazutoshi; Kurihara, Mio; Yoshifuku, Yoshikazu; Oka, Shiro; Arihiro, Koji; Shimamoto, Fumio; Chayama, Kazuaki

2017-04-01

Study aims  This study aimed to investigate the clinical usefulness of magnifying endoscopy (ME) for non-ampullary duodenal tumors. Patients and methods  We enrolled 103 consecutive patients with non-ampullary duodenal tumors that were observed by ME with narrow-band imaging (ME-NBI) and had pit pattern analysis before endoscopic resection at Hiroshima University Hospital before December 2014. ME-NBI images were classified as Type B or Type C according to the Hiroshima classification, and pit patterns were classified as regular or irregular. We studied the clinicopathological features and diagnoses with ME-NBI and pit pattern analyses according to the Vienna classification (category 3: 73 patients; category 4: 30 patients). Results  Category 4 lesions were significantly larger than category 3 lesions. According to ME-NBI images, category 4 Type C lesions (83 %) were significantly more common than category 4 Type B lesions (17 %). According to pit pattern analyses, category 4 irregular lesions 4 (77 %) were significantly more common than category 4 regular lesions (23 %). The accuracies of using Type C ME-NBI images and irregular pit patterns to diagnose category 4 lesions were 87 % and 84 %, the sensitivities were 83 % and 77 %, and the specificities were 89 % and 88 %, respectively. There was no significant difference between ME-NBI and pit pattern analyses for diagnosing the histologic grade of non-ampullary duodenal tumors. Conclusion  Our study showed that ME-NBI and pit pattern analysis had equivalent abilities to determine the histologic grade of non-ampullary duodenal tumors. ME-NBI may be more useful because it is a simple, less time-consuming procedure.

Continuous measurement of breast tumor hormone receptor expression: a comparison of two computational pathology platforms

PubMed Central

Ahern, Thomas P.; Beck, Andrew H.; Rosner, Bernard A.; Glass, Ben; Frieling, Gretchen; Collins, Laura C.; Tamimi, Rulla M.

2017-01-01

Background Computational pathology platforms incorporate digital microscopy with sophisticated image analysis to permit rapid, continuous measurement of protein expression. We compared two computational pathology platforms on their measurement of breast tumor estrogen receptor (ER) and progesterone receptor (PR) expression. Methods Breast tumor microarrays from the Nurses’ Health Study were stained for ER (n=592) and PR (n=187). One expert pathologist scored cases as positive if ≥1% of tumor nuclei exhibited stain. ER and PR were then measured with the Definiens Tissue Studio (automated) and Aperio Digital Pathology (user-supervised) platforms. Platform-specific measurements were compared using boxplots, scatter plots, and correlation statistics. Classification of ER and PR positivity by platform-specific measurements was evaluated with areas under receiver operating characteristic curves (AUC) from univariable logistic regression models, using expert pathologist classification as the standard. Results Both platforms showed considerable overlap in continuous measurements of ER and PR between positive and negative groups classified by expert pathologist. Platform-specific measurements were strongly and positively correlated with one another (rho≥0.77). The user-supervised Aperio workflow performed slightly better than the automated Definiens workflow at classifying ER positivity (AUCAperio=0.97; AUCDefiniens=0.90; difference=0.07, 95% CI: 0.05, 0.09) and PR positivity (AUCAperio=0.94; AUCDefiniens=0.87; difference=0.07, 95% CI: 0.03, 0.12). Conclusion Paired hormone receptor expression measurements from two different computational pathology platforms agreed well with one another. The user-supervised workflow yielded better classification accuracy than the automated workflow. Appropriately validated computational pathology algorithms enrich molecular epidemiology studies with continuous protein expression data and may accelerate tumor biomarker discovery. PMID:27729430

Molecular subgroups of medulloblastoma identification using noninvasive magnetic resonance spectroscopy.

PubMed

Blüml, Stefan; Margol, Ashley S; Sposto, Richard; Kennedy, Rebekah J; Robison, Nathan J; Vali, Marzieh; Hung, Long T; Muthugounder, Sakunthala; Finlay, Jonathan L; Erdreich-Epstein, Anat; Gilles, Floyd H; Judkins, Alexander R; Krieger, Mark D; Dhall, Girish; Nelson, Marvin D; Asgharzadeh, Shahab

2016-01-01

Medulloblastomas in children can be categorized into 4 molecular subgroups with differing clinical characteristics, such that subgroup determination aids in prognostication and risk-adaptive treatment strategies. Magnetic resonance spectroscopy (MRS) is a widely available, noninvasive tool that is used to determine the metabolic characteristics of tumors and provide diagnostic information without the need for tumor tissue. In this study, we investigated the hypothesis that metabolite concentrations measured by MRS would differ between molecular subgroups of medulloblastoma and allow accurate subgroup determination. MRS was used to measure metabolites in medulloblastomas across molecular subgroups (SHH = 12, Groups 3/4 = 17, WNT = 1). Levels of 14 metabolites were analyzed to determine those that were the most discriminant for medulloblastoma subgroups in order to construct a multivariable classifier for distinguishing between combined Group 3/4 and SHH tumors. Medulloblastomas across molecular subgroups revealed distinct spectral features. Group 3 and Group 4 tumors demonstrated metabolic profiles with readily detectable taurine, lower levels of lipids, and high levels of creatine. SHH tumors showed prominent choline and lipid with low levels of creatine and little or no evidence of taurine. A 5-metabolite subgroup classifier inclusive of creatine, myo-inositol, taurine, aspartate, and lipid 13a was developed that could discriminate between Group 3/4 and SHH medulloblastomas with excellent accuracy (cross-validated area under the curve [AUC] = 0.88). The data show that medulloblastomas of Group 3/4 differ metabolically as measured using MRS when compared with SHH molecular subgroups. MRS is a useful and accurate tool to determine medulloblastoma molecular subgroups. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Endometrial vs. cervical cancer: development and pilot testing of a magnetic resonance imaging (MRI) scoring system for predicting tumor origin of uterine carcinomas of indeterminate histology.

PubMed

Bourgioti, Charis; Chatoupis, Konstantinos; Panourgias, Evangelia; Tzavara, Chara; Sarris, Kyrillos; Rodolakis, Alexandros; Moulopoulos, Lia Angela

2015-10-01

To report discriminant MRI features between cervical and endometrial carcinomas and to design an MRI- scoring system, with the potential to predict the origin of uterine cancer (cervix or endometrium) in histologically indeterminate cases. Dedicated pelvic MRIs of 77 patients with uterine tumors involving both cervix and corpus were retrospectively analyzed by two experts in female imaging. Seven MRI tumor characteristics were statistically tested for their discriminant ability for tumor origin compared to final histology: tumor location, perfusion pattern, rim enhancement, depth of myometrial invasion, cervical stromal integrity, intracavitary mass, and retained endometrial secretions. Kappa values were estimated to assess the levels of inter-rater reliability. On the basis of positive likelihood ratio values, an MRI-score was assigned. K value was excellent for most of the imaging criteria. Using ROC curve analysis, the estimated optimal cut-off for the MRI-scoring system was 4 with 96.6% sensitivity and 100% specificity. Using a ≥4 cut-off for cervical cancers and <4 for endometrial cancers, 97.4% of the patients were correctly classified. 2/58 patients with cervical cancer had MRI score <4 and none of the patients with endometrial cancer had MRI score >4. The area under curve of the MRI-scoring system was 0.99 (95% CI 0.98-1.00). When the MRI-score was applied to 20/77 patients with indeterminate initial biopsy and to 5/26 surgically treated patients with erroneous pre-op histology, all cases were correctly classified. The produced MRI-scoring system may be a reliable problem-solving tool for the differential diagnosis of cervical vs. endometrial cancer in cases of equivocal histology.

Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hall, David C.; Trofimov, Alexei V.; Winey, Brian A.

Purpose: To predict the organ at risk (OAR) dose levels achievable with proton beam therapy (PBT), solely based on the geometric arrangement of the target volume in relation to the OARs. A comparison with an alternative therapy yields a prediction of the patient-specific benefits offered by PBT. This could enable physicians at hospitals without proton capabilities to make a better-informed referral decision or aid patient selection in model-based clinical trials. Methods and Materials: Skull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting the correlations between the dose and distance-to-targetmore » in existing PBT plans, the models were independently trained for 6 types of OARs: brainstem, cochlea, optic chiasm, optic nerve, parotid gland, and spinal cord. Once trained, the models could estimate the feasible dose–volume histogram and generalized equivalent uniform dose (gEUD) for OAR structures of new patients. The models were trained using 20 patients and validated using an additional 21 patients. Validation was achieved by comparing the predicted gEUD to that of the actual PBT plan. Results: The predicted and planned gEUD were in good agreement. Considering all OARs, the prediction error was +1.4 ± 5.1 Gy (mean ± standard deviation), and Pearson's correlation coefficient was 93%. By comparing with an intensity modulated photon treatment plan, the model could classify whether an OAR structure would experience a gain, with a sensitivity of 93% (95% confidence interval: 87%-97%) and specificity of 63% (95% confidence interval: 38%-84%). Conclusions: We trained and validated models that could quickly and accurately predict the patient-specific benefits of PBT for skull-base tumors. Similar models could be developed for other tumor sites. Such models will be useful when an estimation of the feasible benefits of PBT is desired but the experience and/or resources required for treatment planning are unavailable.« less

Do craniopharyngioma molecular signatures correlate with clinical characteristics?

PubMed

Omay, Sacit Bulent; Chen, Yu-Ning; Almeida, Joao Paulo; Ruiz-Treviño, Armando Saul; Boockvar, John A; Stieg, Philip E; Greenfield, Jeffrey P; Souweidane, Mark M; Kacker, Ashutosh; Pisapia, David J; Anand, Vijay K; Schwartz, Theodore H

2018-05-01

OBJECTIVE Exome sequencing studies have recently demonstrated that papillary craniopharyngiomas (PCPs) and adamantinomatous craniopharyngiomas (ACPs) have distinct genetic origins, each primarily driven by mutually exclusive alterations: either BRAF ( V600E), observed in 95% of PCPs, or CTNNB1, observed in 75%-96% of ACPs. How the presence of these molecular signatures, or their absence, correlates with clinical, radiographic, and outcome variables is unknown. METHODS The pathology records for patients who underwent surgery for craniopharyngiomas between May 2000 and March 2015 at Weill Cornell Medical College were reviewed. Craniopharyngiomas were identified and classified as PCP or ACP. Patients were placed into 1 of 3 groups based on their genomic mutations: BRAF mutation only, CTNNB1 mutation only, and tumors with neither of these mutations detected (not detected [ND]). Demographic, radiological, and clinical variables were collected, and their correlation with each genomic group was tested. RESULTS Histology correlated strongly with mutation group. All BRAF tumors with mutations were PCPs, and all CTNNB1 with mutations and ND tumors were ACPs. Preoperative and postoperative clinical symptoms and radiographic features did not correlate with any mutation group. There was a statistically significant relationship (p = 0.0323) between the age group (pediatric vs adult) and the mutation groups. The ND group tumors were more likely to involve the sella (p = 0.0065). CONCLUSIONS The mutation signature in craniopharyngioma is highly predictive of histology. The subgroup of tumors in which these 2 mutations are not detected is more likely to occur in children, be located in the sella, and be of ACP histology.

ALK ambiguous-positive non-small cell lung cancers are tumors challenged by diagnostic and therapeutic issues.

PubMed

Uguen, Arnaud; Andrieu-Key, Sophie; Vergne, Florence; Descourt, Renaud; Quéré, Gilles; Quintin-Roué, Isabelle; Key, Stéphane; Guéguen, Paul; Talagas, Matthieu; De Braekeleer, Marc; Marcorelles, Pascale

2016-09-01

Searching for ALK rearrangements using the approved fluorescent in situ hybridization (FISH) test and complementary immunohistochemistry (IHC) has become the rule to treat patients with advanced non‑small cell lung cancer (NSCLC) with anti‑ALK targeted therapy. The concordance between the two techniques is reported to be strong but imperfect. We report our experience with cases of ALK‑rearranged lung adenocarcinomas pointing out particularly ambiguous cases. FISH and IHC data on ALK but also c‑MET IHC as well as EGFR and KRAS mutation screening are considered, together with response to crizotinib treatment. We classified the 55 FISH ALK‑rearranged tumors into two groups according to the FISH and IHC results: a concordant FISH+IHC+ group (31 tumors) and an ambiguous group (24 tumors). These tumors were considered as 'ambiguous' ALK‑positive due to negative (21 tumors) or non‑contributive (3 tumors) IHC. In addition, the percentage of FISH-positive nuclei was between 15 and 20% in 17 tumors belonging to one or the other group (now called borderline tumors). We discuss the accuracy of the different tests with intent to determine whether ambiguous and borderline tumors are real positive ALK‑rearranged tumors. To conclude, ambiguous ALK‑positive lung cancers are challenging tumors with diagnosis and therapeutic issues that can justify parallel FISH, IHC and molecular screening strategy.

The value of intraoperative ultrasonography during the resection of relapsed irradiated malignant gliomas in the brain.

PubMed

Mursch, Kay; Scholz, Martin; Brück, Wolfgang; Behnke-Mursch, Julianne

2017-01-01

The aim of this study was to investigate whether intraoperative ultrasonography (IOUS) helped the surgeon navigate towards the tumor as seen in preoperative magnetic resonance imaging and whether IOUS was able to distinguish between tumor margins and the surrounding tissue. Twenty-five patients suffering from high-grade gliomas who were previously treated by surgery and radiotherapy were included. Intraoperatively, two histopathologic samples were obtained a sample of unequivocal tumor tissue (according to anatomical landmarks and the surgeon's visual and tactile impressions) and a small tissue sample obtained using a navigated needle when the surgeon decided to stop the resection. This specimen was considered to be a boundary specimen, where no tumor tissue was apparent. The decision to take the second sample was not influenced by IOUS. The effect of IOUS was analyzed semi-quantitatively. All 25 samples of unequivocal tumor tissue were histopathologically classified as tumor tissue and were hyperechoic on IOUS. Of the boundary specimens, eight were hypoechoic. Only one harbored tumor tissue (P=0.150). Seventeen boundaries were moderately hyperechoic, and these samples contained all possible histological results (i.e., tumor, infiltration, or no tumor). During surgery performed on relapsed, irradiated, high-grade gliomas, IOUS provided a reliable method of navigating towards the core of the tumor. At borders, it did not reliably distinguish between remnants or tumor-free tissue, but hypoechoic areas seldom contained tumor tissue.

Prognostic and functional role of subtype-specific tumor-stroma interaction in breast cancer.

PubMed

Merlino, Giuseppe; Miodini, Patrizia; Callari, Maurizio; D'Aiuto, Francesca; Cappelletti, Vera; Daidone, Maria Grazia

2017-10-01

None of the clinically relevant gene expression signatures available for breast cancer were specifically developed to capture the influence of the microenvironment on tumor cells. Here, we attempted to build subtype-specific signatures derived from an in vitro model reproducing tumor cell modifications after interaction with activated or normal stromal cells. Gene expression signatures derived from HER2+, luminal, and basal breast cancer cell lines (treated by normal fibroblasts or cancer-associated fibroblasts conditioned media) were evaluated in clinical tumors by in silico analysis on published gene expression profiles (GEPs). Patients were classified as microenvironment-positive (μENV+ve), that is, with tumors showing molecular profiles suggesting activation by the stroma, or microenvironment-negative (μENV-ve) based on correlation of their tumors' GEP with the respective subtype-specific signature. Patients with estrogen receptor alpha (ER)+/HER2-/μENV+ve tumors were characterized by 2.5-fold higher risk of developing distant metastases (HR = 2.546; 95% CI: 1.751-3.701, P = 9.84E-07), while μENV status did not affect, or only suggested the risk of distant metastases, in women with HER2+ (HR = 1.541; 95% CI: 0.788-3.012, P = 0.206) or ER-/HER2- tumors (HR = 1.894; 95% CI: 0.938-3.824; P = 0.0747), respectively. In ER+/HER2- tumors, the μENV status remained significantly associated with metastatic progression (HR = 2.098; CI: 1.214-3.624; P = 0.00791) in multivariable analysis including size, age, and Genomic Grade Index. Validity of our in vitro model was also supported by in vitro biological endpoints such as cell growth (MTT assay) and migration/invasion (Transwell assay). In vitro-derived gene signatures tracing the bidirectional interaction with cancer activated fibroblasts are subtype-specific and add independent prognostic information to classical prognostic variables in women with ER+/HER2- tumors. © 2017 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

PubMed Central

Roy, Janine; Aust, Daniela; Knösel, Thomas; Rümmele, Petra; Jahnke, Beatrix; Hentrich, Vera; Rückert, Felix; Niedergethmann, Marco; Weichert, Wilko; Bahra, Marcus; Schlitt, Hans J.; Settmacher, Utz; Friess, Helmut; Büchler, Markus; Saeger, Hans-Detlev; Schroeder, Michael; Pilarsky, Christian; Grützmann, Robert

2012-01-01

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice. PMID:22615549

Simple Rules, Not So Simple: The Use of International Ovarian Tumor Analysis (IOTA) Terminology and Simple Rules in Inexperienced Hands in a Prospective Multicenter Cohort Study.

PubMed

Meys, Evelyne; Rutten, Iris; Kruitwagen, Roy; Slangen, Brigitte; Lambrechts, Sandrina; Mertens, Helen; Nolting, Ernst; Boskamp, Dieuwke; Van Gorp, Toon

2017-12-01

 To analyze how well untrained examiners - without experience in the use of International Ovarian Tumor Analysis (IOTA) terminology or simple ultrasound-based rules (simple rules) - are able to apply IOTA terminology and simple rules and to assess the level of agreement between non-experts and an expert.  This prospective multicenter cohort study enrolled women with ovarian masses. Ultrasound was performed by non-expert examiners and an expert. Ultrasound features were recorded using IOTA nomenclature, and used for classifying the mass by simple rules. Interobserver agreement was evaluated with Fleiss' kappa and percentage agreement between observers.  50 consecutive women were included. We observed 46 discrepancies in the description of ovarian masses when non-experts utilized IOTA terminology. Tumor type was misclassified often (n = 22), resulting in poor interobserver agreement between the non-experts and the expert (kappa = 0.39, 95 %-CI 0.244 - 0.529, percentage of agreement = 52.0 %). Misinterpretation of simple rules by non-experts was observed 57 times, resulting in an erroneous diagnosis in 15 patients (30 %). The agreement for classifying the mass as benign, malignant or inconclusive by simple rules was only moderate between the non-experts and the expert (kappa = 0.50, 95 %-CI 0.300 - 0.704, percentage of agreement = 70.0 %). The level of agreement for all 10 simple rules features varied greatly (kappa index range: -0.08 - 0.74, percentage of agreement 66 - 94 %).  Although simple rules are useful to distinguish benign from malignant adnexal masses, they are not that simple for untrained examiners. Training with both IOTA terminology and simple rules is necessary before simple rules can be introduced into guidelines and daily clinical practice. © Georg Thieme Verlag KG Stuttgart · New York.

Detection of FAM172A expressed in circulating tumor cells is a feasible method to predict high-risk subgroups of colorectal cancer.

PubMed

Cui, Chun-Hui; Chen, Ri-Hong; Zhai, Duan-Yang; Xie, Lang; Qi, Jia; Yu, Jin-Long

2017-06-01

Previous studies used to enumerate circulating tumor cells to predict prognosis and therapeutic effect of colorectal cancer. However, increasing studies have shown that only circulating tumor cells enumeration was not enough to reflect the heterogeneous condition of tumor. In this study, we classified different metastatic-potential circulating tumor cells from colorectal cancer patients and measured FAM172A expression in circulating tumor cells to improve accuracy of clinical diagnosis and treatment of colorectal cancer. Blood samples were collected from 45 primary colorectal cancer patients. Circulating tumor cells were enriched by blood filtration using isolation by size of epithelial tumor cells, and in situ hybridization with RNA method was used to identify and discriminate subgroups of circulating tumor cells. Afterwards, FAM172A expression in individual circulating tumor cells was measured. Three circulating tumor cell subgroups (epithelial/biophenotypic/mesenchymal circulating tumor cells) were identified using epithelial-mesenchymal transition markers. In our research, mesenchymal circulating tumor cells significantly increased along with tumor progression, development of distant metastasis, and vascular invasion. Furthermore, FAM172A expression rate in mesenchymal circulating tumor cells was significantly higher than that in epithelial circulating tumor cells, which suggested that FAM172A may correlate with malignant degree of tumor. This hypothesis was further verified by FAM172A expression in mesenchymal circulating tumor cells, which was strictly related to tumor aggressiveness factors. Mesenchymal circulating tumor cells and FAM172A detection may predict highrisk stage II colorectal cancer. Our research proved that circulating tumor cells were feasible surrogate samples to detect gene expression and could serve as a predictive biomarker for tumor evaluation.

Molecular approaches for classifying endometrial carcinoma.

PubMed

Piulats, Josep M; Guerra, Esther; Gil-Martín, Marta; Roman-Canal, Berta; Gatius, Sonia; Sanz-Pamplona, Rebeca; Velasco, Ana; Vidal, August; Matias-Guiu, Xavier

2017-04-01

Endometrial carcinoma is the most common cancer of the female genital tract. This review article discusses the usefulness of molecular techniques to classify endometrial carcinoma. Any proposal for molecular classification of neoplasms should integrate morphological features of the tumors. For that reason, we start with the current histological classification of endometrial carcinoma, by discussing the correlation between genotype and phenotype, and the most significant recent improvements. Then, we comment on some of the possible flaws of this classification, by discussing also the value of molecular pathology in improving them, including interobserver variation in pathologic interpretation of high grade tumors. Third, we discuss the importance of applying TCGA molecular approach to clinical practice. We also comment on the impact of intratumor heterogeneity in classification, and finally, we will discuss briefly, the usefulness of TCGA classification in tailoring immunotherapy in endometrial cancer patients. We suggest combining pathologic classification and the surrogate TCGA molecular classification for high-grade endometrial carcinomas, as an option to improve assessment of prognosis. Copyright © 2016 Elsevier Inc. All rights reserved.

Impact of microRNAs on regulatory networks and pathways in human colorectal carcinogenesis and development of metastasis

PubMed Central

2013-01-01

Background Qualitative alterations or abnormal expression of microRNAs (miRNAs) in colon cancer have mainly been demonstrated in primary tumors. Poorly overlapping sets of oncomiRs, tumor suppressor miRNAs and metastamiRs have been linked with distinct stages in the progression of colorectal cancer. To identify changes in both miRNA and gene expression levels among normal colon mucosa, primary tumor and liver metastasis samples, and to classify miRNAs into functional networks, in this work miRNA and gene expression profiles in 158 samples from 46 patients were analysed. Results Most changes in miRNA and gene expression levels had already manifested in the primary tumors while these levels were almost stably maintained in the subsequent primary tumor-to-metastasis transition. In addition, comparing normal tissue, tumor and metastasis, we did not observe general impairment or any rise in miRNA biogenesis. While only few mRNAs were found to be differentially expressed between primary colorectal carcinoma and liver metastases, miRNA expression profiles can classify primary tumors and metastases well, including differential expression of miR-10b, miR-210 and miR-708. Of 82 miRNAs that were modulated during tumor progression, 22 were involved in EMT. qRT-PCR confirmed the down-regulation of miR-150 and miR-10b in both primary tumor and metastasis compared to normal mucosa and of miR-146a in metastases compared to primary tumor. The upregulation of miR-201 in metastasis compared both with normal and primary tumour was also confirmed. A preliminary survival analysis considering differentially expressed miRNAs suggested a possible link between miR-10b expression in metastasis and patient survival. By integrating miRNA and target gene expression data, we identified a combination of interconnected miRNAs, which are organized into sub-networks, including several regulatory relationships with differentially expressed genes. Key regulatory interactions were validated experimentally. Specific mixed circuits involving miRNAs and transcription factors were identified and deserve further investigation. The suppressor activity of miR-182 on ENTPD5 gene was identified for the first time and confirmed in an independent set of samples. Conclusions Using a large dataset of CRC miRNA and gene expression profiles, we describe the interplay of miRNA groups in regulating gene expression, which in turn affects modulated pathways that are important for tumor development. PMID:23987127

Bladder Preservation for Localized Muscle-Invasive Bladder Cancer: The Survival Impact of Local Utilization Rates of Definitive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kozak, Kevin R.; Hamidi, Maryam; Manning, Matthew

2012-06-01

Purpose: This study examines the management and outcomes of muscle-invasive bladder cancer in the United States. Methods and Materials: Patients with muscle-invasive bladder cancer diagnosed between 1988 and 2006 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. Patients were classified according to three mutually exclusive treatment categories based on the primary initial treatment: no local management, radiotherapy, or surgery. Overall survival was assessed with Kaplan-Meier analysis and Cox models based on multiple factors including treatment utilization patterns. Results: The study population consisted of 26,851 patients. Age, sex, race, tumor grade, histology, and geographic location were associated withmore » differences in treatment (all p < 0.01). Patients receiving definitive radiotherapy tended to be older and have less differentiated tumors than patients undergoing surgery (RT, median age 78 years old and 90.6% grade 3/4 tumors; surgery, median age 71 years old and 77.1% grade 3/4 tumors). No large shifts in treatment were seen over time, with most patients managed with surgical resection (86.3% for overall study population). Significant survival differences were observed according to initial treatment: median survival, 14 months with no definitive local treatment; 17 months with radiotherapy; and 43 months for surgery. On multivariate analysis, differences in local utilization rates of definitive radiotherapy did not demonstrate a significant effect on overall survival (hazard ratio, 1.002; 95% confidence interval, 0.999-1.005). Conclusions: Multiple factors influence the initial treatment strategy for muscle-invasive bladder cancer, but definitive radiotherapy continues to be used infrequently. Although patients who undergo surgery fare better, a multivariable model that accounted for patient and tumor characteristics found no survival detriment to the utilization of definitive radiotherapy. These results support continued research into bladder preservation strategies and suggest that definitive radiotherapy represents a viable initial treatment strategy for those who wish to attempt to preserve their native bladder.« less

Gamma knife radiosurgery for skull-base meningiomas.

PubMed

Takanashi, Masami; Fukuoka, Seiji; Hojyo, Atsufumi; Sasaki, Takehiko; Nakagawara, Jyoji; Nakamura, Hirohiko

2009-01-01

The primary purpose of this study was to evaluate the efficacy of gamma knife radiosurgery (GKRS) when used as a treatment modality for cavernous sinus or posterior fossa skull-base meningiomas (SBMs), with particular attention given to whether or not intentional partial resection followed by GKRS constitutes an appropriate combination treatment method for larger SBMs. Of the 101 SBM patients in this series, 38 were classified as having cavernous sinus meningiomas (CSMs), and 63 presented with posterior fossa meningiomas (PFMs). The patients with no history of prior surgery (19 CSMs, 57 PFMs) were treated according to a set protocol. Small to medium-sized SBMs were treated by GKRS only. To minimize the risk of functional deficits, larger tumors were treated with the combination of intentional partial resection followed by GKRS. Residual or recurrent tumors in patients who had undergone extirpations prior to GKRS (19 CSMs, 6 PFMs) are not eligible for this treatment method (due to the surgeries not being performed as part of a combination strategy designed to preserve neurological function as the first priority). The mean follow-up period was 51.9 months (range, 6-144 months). The overall tumor control rates were 95.5% in CSMs and 98.4% in PFMs. Nearly all tumors treated with GKRS alone were well controlled and the patients had no deficits. Furthermore, none of the patients who had undergone prior surgeries experienced new neurological deficits after GKRS. While new neurological deficits appeared far less often in those receiving the combination of partial resection with subsequent GKRS, extirpations tended to be associated with not only a higher incidence of new deficits but also a significant increase in the worsening of already-existing deficits. Our results indicate that GKRS is a safe and effective primary treatment for SBMs with small to moderate tumor volumes. We also found that larger SBMs compressing the optic pathway or brain stem can be effectively treated, minimizing any possible functional damage, by a combination of partial resection with subsequent GKRS.

Mammalian models of chemically induced primary malignancies exploitable for imaging-based preclinical theragnostic research

PubMed Central

Liu, Yewei; Yin, Ting; Feng, Yuanbo; Cona, Marlein Miranda; Huang, Gang; Liu, Jianjun; Song, Shaoli; Jiang, Yansheng; Xia, Qian; Swinnen, Johannes V.; Bormans, Guy; Himmelreich, Uwe; Oyen, Raymond

2015-01-01

Compared with transplanted tumor models or genetically engineered cancer models, chemically induced primary malignancies in experimental animals can mimic the clinical cancer progress from the early stage on. Cancer caused by chemical carcinogens generally develops through three phases namely initiation, promotion and progression. Based on different mechanisms, chemical carcinogens can be divided into genotoxic and non-genotoxic ones, or complete and incomplete ones, usually with an organ-specific property. Chemical carcinogens can be classified upon their origins such as environmental pollutants, cooked meat derived carcinogens, N-nitroso compounds, food additives, antineoplastic agents, naturally occurring substances and synthetic carcinogens, etc. Carcinogen-induced models of primary cancers can be used to evaluate the diagnostic/therapeutic effects of candidate drugs, investigate the biological influential factors, explore preventive measures for carcinogenicity, and better understand molecular mechanisms involved in tumor initiation, promotion and progression. Among commonly adopted cancer models, chemically induced primary malignancies in mammals have several advantages including the easy procedures, fruitful tumor generation and high analogy to clinical human primary cancers. However, in addition to the time-consuming process, the major drawback of chemical carcinogenesis for translational research is the difficulty in noninvasive tumor burden assessment in small animals. Like human cancers, tumors occur unpredictably also among animals in terms of timing, location and the number of lesions. Thanks to the availability of magnetic resonance imaging (MRI) with various advantages such as ionizing-free scanning, superb soft tissue contrast, multi-parametric information, and utility of diverse contrast agents, now a workable solution to this bottleneck problem is to apply MRI for noninvasive detection, diagnosis and therapeutic monitoring on those otherwise uncontrollable animal models with primary cancers. Moreover, it is foreseeable that the combined use of chemically induced primary cancer models and molecular imaging techniques may help to develop new anticancer diagnostics and therapeutics. PMID:26682141

Identifying the potential long-term survivors among breast cancer patients with distant metastasis.

PubMed

Lee, E S; Jung, S Y; Kim, J Y; Kim, J J; Yoo, T K; Kim, Y G; Lee, K S; Lee, E S; Kim, E K; Min, J W; Han, W; Noh, D Y; Moon, H G

2016-05-01

We aimed to develop a prediction model to identify long-term survivors after developing distant metastasis from breast cancer. From the institution's database, we collected data of 547 patients who developed distant metastasis during their follow-ups. We developed a model that predicts the post-metastasis overall survival (PMOS) based on the clinicopathologic factors of the primary tumors and the characteristics of the distant metastasis. For validation, the survival data of 254 patients from four independent institutions were used. The median duration of the PMOS was 31.0 months. The characteristics of the initial primary tumor, such as tumor stage, hormone receptor status, and Ki-67 expression level, and the characteristics of the distant metastasis presentation including the duration of disease-free interval, the site of metastasis, and the presence of metastasis-related symptoms were independent prognostic factors determining the PMOS. The association between tumor stage and the PMOS was only seen in tumors with early relapses. The PMOS score, which was developed based on the above six factors, successfully identified patients with superior survival after metastasis. The median PMOS for patients with a PMOS score of <2 and for patients with a PMOS score of >5 were 71.0 and 12 months, respectively. The clinical significance of the PMOS score was further validated using independent multicenter datasets. We have developed a novel prediction model that can classify breast cancer patients with distant metastasis according to their survival after metastasis. Our model can be a valuable tool to identify long-term survivors who can be potential candidates for more intensive multidisciplinary approaches. Furthermore, our model can provide a more reliable survival information for both physicians and patients during their informed decision-making process. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Irradiation of Liposarcoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friedman, Milton; Egan, John W.

1960-09-01

The response to supervoltage roentgen treatment (2 Mv) of 15 liposarcozxas, in 12 patients, was analyzed, the tumors being classified according to the histologic degree of malignancy. It was found, paradoxically, that the differentiated liposarcomas were more radiosensitive than the undifferentiated lesions. Irradiation can only occasionally contribute toward a cure. The tumor lethal dose ranges from 6000 to 9000 rads in 30 to 50 days, and a useful palliative dose is 3000 to 4000 rads in 20 to 30 days. The prolonged natural life history of many liposarcomas requires a 10-year follow-up for evaluation of the treatment.

Mobile phone use and the risk of skin cancer: a nationwide cohort study in Denmark.

PubMed

Poulsen, Aslak Harbo; Friis, Søren; Johansen, Christoffer; Jensen, Allan; Frei, Patrizia; Kjaear, Susanne Krüger; Dalton, Susanne Oksbjerg; Schüz, Joachim

2013-07-15

The International Agency for Research on Cancer has classified radiofrequency radiation as possibly carcinogenic. Previous studies have focused on intracranial tumors, although the skin receives much radiation. In a nationwide cohort study, 355,701 private mobile phone subscribers in Denmark from 1987 to 1995 were followed up through 2007. We calculated incidence rate ratios (IRRs) for melanoma, basal cell carcinoma, and squamous cell carcinoma by using Poisson regression models adjusted for age, calendar period, educational level, and income. Separate IRRs for head/neck tumors and torso/leg tumors were compared (IRR ratios) to further address potential confounders. We observed no overall increased risk for basal cell carcinoma, squamous cell carcinoma, or melanoma of the head and neck. After a follow-up period of at least 13 years, the IRRs for basal cell carcinoma and squamous cell carcinoma remained near unity. Among men, the IRR for melanoma of the head and neck was 1.20 (95% confidence interval: 0.65, 2.22) after a minimum 13-year follow-up, whereas the corresponding IRR for the torso and legs was 1.16 (95% confidence interval: 0.91, 1.47), yielding an IRR ratio of 1.04 (95% confidence interval: 0.54, 2.00). A similar risk pattern was seen among women, though it was based on smaller numbers. In this large, population-based cohort study, little evidence of an increased skin cancer risk was observed among mobile phone users.

Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Kim, Renaid

2017-03-01

Understanding the key radiogenomic associations for breast cancer between DCE-MRI and micro-RNA expressions is the foundation for the discovery of radiomic features as biomarkers for assessing tumor progression and prognosis. We conducted a study to analyze the radiogenomic associations for breast cancer using the TCGA-TCIA data set. The core idea that tumor etiology is a function of the behavior of miRNAs is used to build the regression models. The associations based on regression are analyzed for three study outcomes: diagnosis, prognosis, and treatment. The diagnosis group consists of miRNAs associated with clinicopathologic features of breast cancer and significant aberration of expression in breast cancer patients. The prognosis group consists of miRNAs which are closely associated with tumor suppression and regulation of cell proliferation and differentiation. The treatment group consists of miRNAs that contribute significantly to the regulation of metastasis thereby having the potential to be part of therapeutic mechanisms. As a first step, important miRNA expressions were identified and their ability to classify the clinical phenotypes based on the study outcomes was evaluated using the area under the ROC curve (AUC) as a figure-of-merit. The key mapping between the selected miRNAs and radiomic features were determined using least absolute shrinkage and selection operator (LASSO) regression analysis within a two-loop leave-one-out cross-validation strategy. These key associations indicated a number of radiomic features from DCE-MRI to be potential biomarkers for the three study outcomes.

Selective isolation and noninvasive analysis of circulating cancer stem cells through Raman imaging.

PubMed

Cho, Hyeon-Yeol; Hossain, Md Khaled; Lee, Jin-Ho; Han, Jiyou; Lee, Hun Joo; Kim, Kyeong-Jun; Kim, Jong-Hoon; Lee, Ki-Bum; Choi, Jeong-Woo

2018-04-15

Circulating cancer stem cells (CCSCs), a rare circulating tumor cell (CTC) type, recently arose as a useful resource for monitoring and characterizing both cancers and their metastatic derivatives. However, due to the scarcity of CCSCs among hematologic cells in the blood and the complexity of the phenotype confirmation process, CCSC research can be extremely challenging. Hence, we report a nanoparticle-mediated Raman imaging method for CCSC characterization which profiles CCSCs based on their surface marker expression phenotypes. We have developed an integrated combinatorial Raman-Active Nanoprobe (RAN) system combined with a microfluidic chip to successfully process complete blood samples. CCSCs and CTCs were detected (90% efficiency) and classified in accordance with their respective surface marker expression via completely distinct Raman signals of RANs. Selectively isolated CCSCs (93% accuracy) were employed for both in vitro and in vivo tumor phenotyping to identify the tumorigenicity of the CCSCs. We utilized our new method to predict metastasis by screening blood samples from xenograft models, showing that upon CCSC detection, all subjects exhibited liver metastasis. Having highly efficient detection and noninvasive isolation capabilities, we have demonstrated that our RAN-based Raman imaging method will be valuable for predicting cancer metastasis and relapse via CCSC detection. Moreover, the exclusion of peak overlapping in CCSC analysis with our Raman imaging method will allow to expand the RAN families for various cancer types, therefore, increasing therapeutic efficacy by providing detailed molecular features of tumor subtypes. Copyright © 2017 Elsevier B.V. All rights reserved.

Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer

PubMed Central

Candido dos Reis, Francisco J.; Lynn, Stuart; Ali, H. Raza; Eccles, Diana; Hanby, Andrew; Provenzano, Elena; Caldas, Carlos; Howat, William J.; McDuffus, Leigh-Anne; Liu, Bin; Daley, Frances; Coulson, Penny; Vyas, Rupesh J.; Harris, Leslie M.; Owens, Joanna M.; Carton, Amy F.M.; McQuillan, Janette P.; Paterson, Andy M.; Hirji, Zohra; Christie, Sarah K.; Holmes, Amber R.; Schmidt, Marjanka K.; Garcia-Closas, Montserrat; Easton, Douglas F.; Bolla, Manjeet K.; Wang, Qin; Benitez, Javier; Milne, Roger L.; Mannermaa, Arto; Couch, Fergus; Devilee, Peter; Tollenaar, Robert A.E.M.; Seynaeve, Caroline; Cox, Angela; Cross, Simon S.; Blows, Fiona M.; Sanders, Joyce; de Groot, Renate; Figueroa, Jonine; Sherman, Mark; Hooning, Maartje; Brenner, Hermann; Holleczek, Bernd; Stegmaier, Christa; Lintott, Chris; Pharoah, Paul D.P.

2015-01-01

Background Citizen science, scientific research conducted by non-specialists, has the potential to facilitate biomedical research using available large-scale data, however validating the results is challenging. The Cell Slider is a citizen science project that intends to share images from tumors with the general public, enabling them to score tumor markers independently through an internet-based interface. Methods From October 2012 to June 2014, 98,293 Citizen Scientists accessed the Cell Slider web page and scored 180,172 sub-images derived from images of 12,326 tissue microarray cores labeled for estrogen receptor (ER). We evaluated the accuracy of Citizen Scientist's ER classification, and the association between ER status and prognosis by comparing their test performance against trained pathologists. Findings The area under ROC curve was 0.95 (95% CI 0.94 to 0.96) for cancer cell identification and 0.97 (95% CI 0.96 to 0.97) for ER status. ER positive tumors scored by Citizen Scientists were associated with survival in a similar way to that scored by trained pathologists. Survival probability at 15 years were 0.78 (95% CI 0.76 to 0.80) for ER-positive and 0.72 (95% CI 0.68 to 0.77) for ER-negative tumors based on Citizen Scientists classification. Based on pathologist classification, survival probability was 0.79 (95% CI 0.77 to 0.81) for ER-positive and 0.71 (95% CI 0.67 to 0.74) for ER-negative tumors. The hazard ratio for death was 0.26 (95% CI 0.18 to 0.37) at diagnosis and became greater than one after 6.5 years of follow-up for ER scored by Citizen Scientists, and 0.24 (95% CI 0.18 to 0.33) at diagnosis increasing thereafter to one after 6.7 (95% CI 4.1 to 10.9) years of follow-up for ER scored by pathologists. Interpretation Crowdsourcing of the general public to classify cancer pathology data for research is viable, engages the public and provides accurate ER data. Crowdsourced classification of research data may offer a valid solution to problems of throughput requiring human input. PMID:26288840

Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer.

PubMed

Yang, Fei; Thomas, Maria A; Dehdashti, Farrokh; Grigsby, Perry W

2013-05-01

The aim of this pilot study was to explore heterogeneity in the temporal behavior of intratumoral [(18)F]fluorodeoxyglucose (FDG) accumulation at a regional scale in patients with cervical cancer undergoing chemoradiotherapy. Included in the study were 20 patients with FIGO stages IB1 to IVA cervical cancer treated with combined chemoradiotherapy. Patients underwent FDG PET/CT before treatment, during weeks 2 and 4 of treatment, and 12 weeks after completion of therapy. Patients were classified based on response to therapy as showing a complete metabolic response (CMR), a partial metabolic response (PMR), or residual disease and the development of new disease (NEW). Based on the presence of residual primary tumor following therapy, patients were divided into two groups, CMR and PMR/NEW. Temporal profiles of intratumoral FDG heterogeneity as characterized by textural features at a regional scale were assessed and compared with those of the standardized uptake value (SUV) indices (SUVmax and SUVmean) within the context of differentiating response groups. Textural features at a regional scale with emphasis on characterizing contiguous regions of high uptake in tumors decreased significantly with time (P < 0.001) in the CMR group, while features describing contiguous regions of low uptake along with those measuring the nonuniformity of contiguous isointense regions in tumors exhibited significant temporal changes in the PMR/NEW group (P < 0.03) but showed no persistent trends with time. Both SUV indices showed significant changes during the course of the disease in both patient groups (P < 0.001 for SUVmax and SUVmean in the CMR group; P = 0.0109 and 0.0136, respectively, for SUVmax and SUVmean in the PMR/NEW group), and also decreased at a constant rate in the CMR group and decreased up to the 4th week of treatment and then increased in the PMR/NEW group. The temporal changes in the heterogeneity of intratumoral FDG distribution characterized at a regional scale using image-based textural features may provide an adjunctive or alternative option for understanding tumor response to chemoradiotherapy and interpreting FDG accumulation dynamics in patients with malignant cervical tumors during the course of the disease.

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution.

PubMed

Kim, Hoon; Zheng, Siyuan; Amini, Seyed S; Virk, Selene M; Mikkelsen, Tom; Brat, Daniel J; Grimsby, Jonna; Sougnez, Carrie; Muller, Florian; Hu, Jian; Sloan, Andrew E; Cohen, Mark L; Van Meir, Erwin G; Scarpace, Lisa; Laird, Peter W; Weinstein, John N; Lander, Eric S; Gabriel, Stacey; Getz, Gad; Meyerson, Matthew; Chin, Lynda; Barnholtz-Sloan, Jill S; Verhaak, Roel G W

2015-03-01

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ∼ 7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity. © 2015 Kim et al.; Published by Cold Spring Harbor Laboratory Press.

Early experience using the da Vinci Surgical System for the treatment of mediastinal tumors.

PubMed

Kajiwara, Naohiro; Taira, Masahiro; Yoshida, Koichi; Hagiwara, Masaru; Kakihana, Masatoshi; Usuda, Jitsuo; Uchida, Osamu; Ohira, Tatsuo; Kawate, Norihiko; Ikeda, Norihiko

2011-10-01

The da Vinci Surgical System has been used in only a few cases for treating mediastinal tumors in Japan. Recently, we used the da Vinci Surgical System for various types of anterior and middle mediastinal tumors in clinical practice. We report our early experience using the da Vinci Surgical System. Seven patients gave written informed consent to undergo robotic surgery for mediastinal tumor dissection using the da Vinci Surgical System. We evaluated the safety and feasibility of this system for the surgical treatment of mediastinal tumors. Two specialists in thoracic surgery who are certified to use the da Vinci S Surgical System and another specialist acted as an assistant performed the tumor dissection. We were able to access difficult-to-reach areas, such as the mediastinum, safely. All the resected tumors were classified as benign tumors histologically. The average da Vinci setting time was 14.0 min, the average working time was 55.7 min, and the average overall operating time was 125.9 min. The learning curve for the da Vinci setup and manipulation time was short. Robotic surgery enables mediastinal tumor dissection in certain cases more safely and easily than conventional video-assisted thoracoscopic surgery and less invasively than open thoracotomy.

Microarray gene expression profiling using core biopsies of renal neoplasia.

PubMed

Rogers, Craig G; Ditlev, Jonathon A; Tan, Min-Han; Sugimura, Jun; Qian, Chao-Nan; Cooper, Jeff; Lane, Brian; Jewett, Michael A; Kahnoski, Richard J; Kort, Eric J; Teh, Bin T

2009-01-01

We investigate the feasibility of using microarray gene expression profiling technology to analyze core biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal tumors-comprised of four histological subtypes-following radical nephrectomy using 18-gauge biopsy needles. RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription. Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1), angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology.

Patent blue V and indocyanine green for fluorescence microimaging of human peritoneal carcinomatosis using probe-based confocal laser endomicroscopy.

PubMed

Abbaci, Muriel; Dartigues, Peggy; De Leeuw, Frederic; Soufan, Ranya; Fabre, Monique; Laplace-Builhé, Corinne

2016-12-01

Peritoneal carcinomatosis is a metastatic stage aggravating abdominal and pelvic cancer dissemination. The preoperative evaluation of lesions remains difficult today. Probe-based confocal laser endomicroscopy (pCLE) provides dynamic images of tissue architecture and cellular details. This technology allows in vivo histological interpretation of tissue. The main limitation of pCLE for adoption in the clinic is the unavailability of fluorescent contrast agents. The aim of our study was to evaluate the staining performance of indocyanine green and patent blue V for histological diagnosis of pCLE images of pathological and non-pathological peritoneal tissue. We performed a correlative study with the histological gold standard on ex vivo human specimens from 25 patients operated for peritoneal carcinomatosis; 70 specimens were stained by topical application with ICG or patent blue V and then imaged with a probe-based confocal laser endomicroscope. A total of 350 pCLE images and 70 corresponding histological sections were randomly and blindly interpreted by two pathologists (PT1 and PT2). The images were first classified into two categories, tumoral versus non-tumoral, and a refined histological diagnosis was then given. All presented images were interpreted by PT1 (who received prior training on PCLE image reading) and PT2 (no training). 100 % sensitivity for PT1 and PT2 was noticed with tissues stained with ICG to differentiate tumoral and non-tumoral tissue. Global scores were always better for PT1 (major concordance between 86 and 94 %) than for PT2 (major concordance between 77 and 89 %) independently of the fluorescent dye when histological diagnosis was done on pCLE images. In conclusion, the pair ICG-pCLE offers the best combination for a non-trained pathologist for the interpretation of pCLE images from peritoneum.

Raman mapping of oral buccal mucosa: a spectral histopathology approach

NASA Astrophysics Data System (ADS)

Behl, Isha; Kukreja, Lekha; Deshmukh, Atul; Singh, S. P.; Mamgain, Hitesh; Hole, Arti R.; Krishna, C. Murali

2014-12-01

Oral cancer is one of the most common cancers worldwide. One-fifth of the world's oral cancer subjects are from India and other South Asian countries. The present Raman mapping study was carried out to understand biochemical variations in normal and malignant oral buccal mucosa. Data were acquired using WITec alpha 300R instrument from 10 normal and 10 tumors unstained tissue sections. Raman maps of normal sections could resolve the layers of epithelium, i.e. basal, intermediate, and superficial. Inflammatory, tumor, and stromal regions are distinctly depicted on Raman maps of tumor sections. Mean and difference spectra of basal and inflammatory cells suggest abundance of DNA and carotenoids features. Strong cytochrome bands are observed in intermediate layers of normal and stromal regions of tumor. Epithelium and stromal regions of normal cells are classified by principal component analysis. Classification among cellular components of normal and tumor sections is also observed. Thus, the findings of the study further support the applicability of Raman mapping for providing molecular level insights in normal and malignant conditions.

Giant Cell Tumor of Bone - An Overview

PubMed Central

Sobti, Anshul; Agrawal, Pranshu; Agarwala, Sanjay; Agarwal, Manish

2016-01-01

Giant Cell tumors (GCT) are benign tumors with potential for aggressive behavior and capacity to metastasize. Although rarely lethal, benign bone tumors may be associated with a substantial disturbance of the local bony architecture that can be particularly troublesome in peri-articular locations. Its histogenesis remains unclear. It is characterized by a proliferation of mononuclear stromal cells and the presence of many multi- nucleated giant cells with homogenous distribution. There is no widely held consensus regarding the ideal treatment method selection. There are advocates of varying surgical techniques ranging from intra-lesional curettage to wide resection. As most giant cell tumors are benign and are located near a joint in young adults, several authors favor an intralesional approach that preserves anatomy of bone in lieu of resection. Although GCT is classified as a benign lesion, few patients develop progressive lung metastases with poor outcomes. Treatment is mainly surgical. Options of chemotherapy and radiotherapy are reserved for selected cases. Recent advances in the understanding of pathogenesis are essential to develop new treatments for this locally destructive primary bone tumor. PMID:26894211

DNA Ploidy Measured on Archived Pretreatment Biopsy Material May Correlate With Prostate-Specific Antigen Recurrence After Prostate Brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keyes, Mira, E-mail: mkeyes@bccancer.bc.ca; MacAulay, Calum; Hayes, Malcolm

Purpose: To explore whether DNA ploidy of prostate cancer cells determined from archived transrectal ultrasound-guided biopsy specimens correlates with disease-free survival. Methods and Materials: Forty-seven failures and 47 controls were selected from 1006 consecutive low- and intermediate-risk patients treated with prostate {sup 125}I brachytherapy (July 1998-October 2003). Median follow-up was 7.5 years. Ten-year actuarial disease-free survival was 94.1%. Controls were matched using age, initial prostate-specific antigen level, clinical stage, Gleason score, use of hormone therapy, and follow-up (all P nonsignificant). Seventy-eight specimens were successfully processed; 27 control and 20 failure specimens contained more than 100 tumor cells were used formore » the final analysis. The Feulgen-Thionin stained cytology samples from archived paraffin blocks were used to determine the DNA ploidy of each tumor by measuring integrated optical densities. Results: The samples were divided into diploid and aneuploid tumors. Aneuploid tumors were found in 16 of 20 of the failures (80%) and 8 of 27 controls (30%). Diploid DNA patients had a significantly lower rate of disease recurrence (P=.0086) (hazard ratio [HR] 0.256). On multivariable analysis, patients with aneuploid tumors had a higher prostate-specific antigen failure rate (HR 5.13). Additionally, those with “excellent” dosimetry (V100 >90%; D90 >144 Gy) had a significantly lower recurrence rate (HR 0.25). All patients with aneuploid tumors and dosimetry classified as “nonexcellent” (V100 <90%; D90 <144 Gy) (5 of 5) had disease recurrence, compared with 40% of patients with aneuploid tumors and “excellent” dosimetry (8 of 15). In contrast, dosimetry did not affect the outcome for diploid patients. Conclusions: Using core biopsy material from archived paraffin blocks, DNA ploidy correctly classified the majority of failures and nonfailures in this study. The results suggest that DNA ploidy can be used as a useful marker for aggressiveness of localized prostate cancer. A larger study will be necessary to further confirm our hypothesis.« less

Clinical application of a color map pattern on shear-wave elastography for invasive breast cancer.

PubMed

Lee, Seokwon; Jung, Younglae; Bae, Youngtae

2016-03-01

The aim of this study was to classify the color map pattern on shear-wave elastography (SWE) and to determine its association with clinicopathological factors for clinical application in invasive breast cancer. From June to December 2014, 103 invasive breast cancers were imaged by B-mode ultrasonography (US) and SWE just before surgery. The color map pattern identified on the SWE could be classified into three main categories: type 1 (diffuse pattern), increased stiffness in the surrounding stroma and the interior lesion itself; type 2 (lateral pattern), marked peri-tumoral stiffness at the anterior and lateral portions with no or minor stiffness at the posterior portion; and type 3 (rim-off pattern), marked peri-tumoral stiffness at the anterior and posterior portion with no or minor stiffness at both lateral portions. High-grade density on mammography (grade 3-4) was more frequent in the type 1 pattern than the other pattern types (80.5% in high-grade density vs. 19.5% in low-grade density). For type 1 tumors, the extent of synchronous non-invasive cancers (pT0), ductal carcinoma in situ (DCIS), was 1.8-2.0 times wider than that measured by US or magnetic resonance imaging (MRI). For type 2 tumors, the invasive tumor components (pT size) size was 1.3 times greater than measured by MRI (p = 0.049). On the other hand, the pT size and pT0 extent of type 3 tumors were almost equal to the preoperative US and MRI measurements. In terms of immunohistochemical (IHC) profiles, type 3 tumors showed a high histologic grade (p = 0.021), poor differentiation (p = 0.009), presence of necrosis (p = 0.018), and high Ki-67 (p = 0.002). The percentage of HER2-positive cancers was relatively high within the type 2 group, and the percentage of triple negative breast cancer was relatively high in the type 3 group (p = 0.011). We expect that assessments of the SWE color map pattern will prove useful for surgical or therapeutic plan decisions and to predict prognosis in invasive breast cancer patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach

PubMed Central

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad; Sadeghi-Naini, Ali; Tran, William; Trudeau, Maureen E.; Pritchard, Kathleen; Ghandi, Sonal; Verma, Sunil; Czarnota, Gregory J.

2016-01-01

Purpose This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC). Methods Using a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival. Results Multi-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively). Conclusion This study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients. PMID:27105515

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach.

PubMed

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad; Sadeghi-Naini, Ali; Tran, William; Trudeau, Maureen E; Pritchard, Kathleen; Ghandi, Sonal; Verma, Sunil; Czarnota, Gregory J

2016-07-19

This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC). Using a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival. Multi-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively). This study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.

Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer.

PubMed

Tixier, Florent; Le Rest, Catherine Cheze; Hatt, Mathieu; Albarghach, Nidal; Pradier, Olivier; Metges, Jean-Philippe; Corcos, Laurent; Visvikis, Dimitris

2011-03-01

(18)F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer. Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body (18)F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P < 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operating-characteristic curves. Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P < 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P = 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement. Textural features of tumor metabolic distribution extracted from baseline (18)F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.

Segmentation, feature extraction, and multiclass brain tumor classification.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2013-12-01

Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS-90.74 %, GBM-88.46 %, MED-85 %, MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

Classification of breast abnormalities using artificial neural network

NASA Astrophysics Data System (ADS)

Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

2015-05-01

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

Proton and carbon ion radiotherapy for primary brain tumors delivered with active raster scanning at the Heidelberg Ion Therapy Center (HIT): early treatment results and study concepts

PubMed Central

2012-01-01

Background Particle irradiation was established at the University of Heidelberg 2 years ago. To date, more than 400 patients have been treated including patients with primary brain tumors. In malignant glioma (WHO IV) patients, two clinical trials have been set up-one investigating the benefit of a carbon ion (18 GyE) vs. a proton boost (10 GyE) in addition to photon radiotherapy (50 Gy), the other one investigating reirradiation with escalating total dose schedules starting at 30 GyE. In atypical meningioma patients (WHO °II), a carbon ion boost of 18 GyE is applied to macroscopic tumor residues following previous photon irradiation with 50 Gy. This study was set up in order to investigate toxicity and response after proton and carbon ion therapy for gliomas and meningiomas. Methods 33 patients with gliomas (n = 26) and meningiomas (n = 7) were treated with carbon ion (n = 26) and proton (n = 7) radiotherapy. In 22 patients, particle irradiation was combined with photon therapy. Temozolomide-based chemotherapy was combined with particle therapy in 17 patients with gliomas. Particle therapy as reirradiation was conducted in 7 patients. Target volume definition was based upon CT, MRI and PET imaging. Response was assessed by MRI examinations, and progression was diagnosed according to the Macdonald criteria. Toxicity was classified according to CTCAE v4.0. Results Treatment was completed and tolerated well in all patients. Toxicity was moderate and included fatigue (24.2%), intermittent cranial nerve symptoms (6%) and single episodes of seizures (6%). At first and second follow-up examinations, mean maximum tumor diameters had slightly decreased from 29.7 mm to 27.1 mm and 24.9 mm respectively. Nine glioma patients suffered from tumor relapse, among these 5 with infield relapses, causing death in 8 patients. There was no progression in any meningioma patient. Conclusions Particle radiotherapy is safe and feasible in patients with primary brain tumors. It is associated with little toxicity. A positive response of both gliomas and meningiomas, which is suggested in these preliminary data, must be evaluated in further clinical trials. PMID:22436135

Computerized detection of breast cancer using resonance-frequency-based electrical impedance spectroscopy

NASA Astrophysics Data System (ADS)

Gao, Wei; Fan, Ming; Zhao, Weijie; Zheng, Bin; Li, Lihua

2017-03-01

This study developed and tested a multi-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system aimed at detection of breast cancer. The REIS system consists of specially designed mechanical supporting device that can be easily lifted to fit women of different height, a seven probe sensor cup, and a computer providing software for system control and management. The sensor cup includes one central probe for direct contact with the nipple, and other six probes uniformly distributed at a distance of 35mm away from the center probe to enable contact with breast skin surface. It takes about 18 seconds for this system to complete a data acquisition process. We utilized this system for examination of breast cancer, collecting a dataset of 289 cases including biopsy verified 74 malignant and 215 benign tumors. After that, 23 REIS based features, including seven frequency, fifteen magnitude features were extracted, and an age feature. To reduce redundancy we selected 6 features using the evolutionary algorithm for classification. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. A multivariable logistic regression method was performed for detection of the tumors. The results of our study showed for the 23 REIS features AUC and ACC, Sensitivity and Specificity of 0.796, 0.727, 0.731 and 0.726, respectively. The AUC and ACC, Sensitivity and Specificity for the 6 REIS features of 0.840, 0.80, 0.703 and 0.833, respectively, and AUC of 0.662 and 0.619 for the frequency and magnitude based REIS features, respectively. The performance of the classifiers using all the 6 features was significantly better than solely using magnitude features (p=3.29e-08) and frequency features (5.61e-07). Smote algorithm was used to expand small samples to balance the dataset, the AUC after data balance of 0.846 increased than the original data classification performance. The results indicated that the REIS system is a promising tool for detection of breast cancer and may be acceptable for clinical implementation.

Impact of proteolytic enzymes in colorectal cancer development and progression.

PubMed

Herszényi, László; Barabás, Loránd; Hritz, István; István, Gábor; Tulassay, Zsolt

2014-10-07

Tumor invasion and metastasis is a highly complicated, multi-step phenomenon. In the complex event of tumor progression, tumor cells interact with basement membrane and extracellular matrix components. Proteolytic enzymes (proteinases) are involved in the degradation of extracellular matrix, but also in cancer invasion and metastasis. The four categories of proteinases (cysteine-, serine-, aspartic-, and metalloproteinases) are named and classified according to the essential catalytic component in their active site. We and others have shown that proteolytic enzymes play a major role not only in colorectal cancer (CRC) invasion and metastasis, but also in malignant transformation of precancerous lesions into cancer. Tissue and serum-plasma antigen concentrations of proteinases might be of great value in identifying patients with poor prognosis in CRC. Our results, in concordance with others indicate the potential tumor marker impact of proteinases for the early diagnosis of CRC. In addition, proteinases may also serve as potential target molecules for therapeutic agents.

A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arévalo, John; Judkins, Alexander; Madabhushi, Anant; González, Fabio

2015-12-01

Convolutional neural networks (CNN) have been very successful at addressing different computer vision tasks thanks to their ability to learn image representations directly from large amounts of labeled data. Features learned from a dataset can be used to represent images from a different dataset via an approach called transfer learning. In this paper we apply transfer learning to the challenging task of medulloblastoma tumor differentiation. We compare two different CNN models which were previously trained in two different domains (natural and histopathology images). The first CNN is a state-of-the-art approach in computer vision, a large and deep CNN with 16-layers, Visual Geometry Group (VGG) CNN. The second (IBCa-CNN) is a 2-layer CNN trained for invasive breast cancer tumor classification. Both CNNs are used as visual feature extractors of histopathology image regions of anaplastic and non-anaplastic medulloblastoma tumor from digitized whole-slide images. The features from the two models are used, separately, to train a softmax classifier to discriminate between anaplastic and non-anaplastic medulloblastoma image regions. Experimental results show that the transfer learning approach produce competitive results in comparison with the state of the art approaches for IBCa detection. Results also show that features extracted from the IBCa-CNN have better performance in comparison with features extracted from the VGG-CNN. The former obtains 89.8% while the latter obtains 76.6% in terms of average accuracy.

Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography.

PubMed

Yilmaz, E; Kayikcioglu, T; Kayipmaz, S

2017-07-01

In this article, we propose a decision support system for effective classification of dental periapical cyst and keratocystic odontogenic tumor (KCOT) lesions obtained via cone beam computed tomography (CBCT). CBCT has been effectively used in recent years for diagnosing dental pathologies and determining their boundaries and content. Unlike other imaging techniques, CBCT provides detailed and distinctive information about the pathologies by enabling a three-dimensional (3D) image of the region to be displayed. We employed 50 CBCT 3D image dataset files as the full dataset of our study. These datasets were identified by experts as periapical cyst and KCOT lesions according to the clinical, radiographic and histopathologic features. Segmentation operations were performed on the CBCT images using viewer software that we developed. Using the tools of this software, we marked the lesional volume of interest and calculated and applied the order statistics and 3D gray-level co-occurrence matrix for each CBCT dataset. A feature vector of the lesional region, including 636 different feature items, was created from those statistics. Six classifiers were used for the classification experiments. The Support Vector Machine (SVM) classifier achieved the best classification performance with 100% accuracy, and 100% F-score (F1) scores as a result of the experiments in which a ten-fold cross validation method was used with a forward feature selection algorithm. SVM achieved the best classification performance with 96.00% accuracy, and 96.00% F1 scores in the experiments in which a split sample validation method was used with a forward feature selection algorithm. SVM additionally achieved the best performance of 94.00% accuracy, and 93.88% F1 in which a leave-one-out (LOOCV) method was used with a forward feature selection algorithm. Based on the results, we determined that periapical cyst and KCOT lesions can be classified with a high accuracy with the models that we built using the new dataset selected for this study. The studies mentioned in this article, along with the selected 3D dataset, 3D statistics calculated from the dataset, and performance results of the different classifiers, comprise an important contribution to the field of computer-aided diagnosis of dental apical lesions. Copyright © 2017 Elsevier B.V. All rights reserved.

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Induction of mammary tumors in rat by intraperitoneal injection of NMU: histopathology and estral cycle influence.

PubMed

Rivera, E S; Andrade, N; Martin, G; Melito, G; Cricco, G; Mohamad, N; Davio, C; Caro, R; Bergoc, R M

1994-11-11

In order to obtain an experimental model we induced mammary tumors in female Sprague-Dawley rats. The carcinogen N-nitroso-N-methylurea (NMU) was injected intraperitoneally (i.p.) at doses of 50 mg/kg body weight when animals were 50, 80 and 110 days old. Tumor sizes were measured with a caliper and their growth parameters and histopathological properties were tested. For 100 rats, 88.4% of developed lesions were ductal carcinomas, histologically classified as 52.8% cribiform variety, 30.6% solid carcinoma. Metastases in liver, spleen and lung were present. Other primary tumors were detected with low incidence. The influence of the rat estrous cycle during the first exposure to intraperitoneal NMU injection was studied. The latency period in estrus, proestrus and diestrus was 82 +/- 15, 77 +/- 18 and 79 +/- 18 days, respectively. Tumor incidence was significantly higher in estrus (95.2%) than proestrus (71.4%) or diestrus (77.4), (P < 0.01). Mean number or tumors per animal was similar among the three groups (4.4 +/- 3.2, 3.8 +/- 3.6, 3.2 +/- 1.8). The procedure described appears to be the simplest method for inducing experimental mammary tumors in rats.

Soft Tissue Tumor Immunohistochemistry Update: Illustrative Examples of Diagnostic Pearls to Avoid Pitfalls.

PubMed

Wei, Shi; Henderson-Jackson, Evita; Qian, Xiaohua; Bui, Marilyn M

2017-08-01

- Current 2013 World Health Organization classification of tumors of soft tissue arranges these tumors into 12 groups according to their histogenesis. Tumor behavior is classified as benign, intermediate (locally aggressive), intermediate (rarely metastasizing), and malignant. In our practice, a general approach to reaching a definitive diagnosis of soft tissue tumors is to first evaluate clinicoradiologic, histomorphologic, and cytomorphologic features of the tumor to generate some pertinent differential diagnoses. These include the potential line of histogenesis and whether the tumor is benign or malignant, and low or high grade. Although molecular/genetic testing is increasingly finding its applications in characterizing soft tissue tumors, currently immunohistochemistry still not only plays an indispensable role in defining tumor histogenesis, but also serves as a surrogate for underlining molecular/genetic alterations. Objective- To provide an overview focusing on the current concepts in the classification and diagnosis of soft tissue tumors, incorporating immunohistochemistry. This article uses examples to discuss how to use the traditional and new immunohistochemical markers for the diagnosis of soft tissue tumors. Practical diagnostic pearls, summary tables, and figures are used to show how to avoid diagnostic pitfalls. - Data were obtained from pertinent peer-reviewed English-language literature and the authors' first-hand experience as bone and soft tissue pathologists. - -The ultimate goal for a pathologist is to render a specific diagnosis that provides diagnostic, prognostic, and therapeutic information to guide patient care. Immunohistochemistry is integral to the diagnosis and management of soft tissue tumors.

Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma

PubMed Central

Lin, Chin-Hsing Annie; Rhodes, Christopher T.; Lin, ChenWei; Phillips, Joanna J.; Berger, Mitchel S.

2017-01-01

ABSTRACT Glioblastoma (GBM) is a highly aggressive brain cancer with limited therapeutic options. While efforts to identify genes responsible for GBM have revealed mutations and aberrant gene expression associated with distinct types of GBM, patients with GBM are often diagnosed and classified based on MRI features. Therefore, we seek to identify molecular representatives in parallel with MRI classification for group I and group II primary GBM associated with the subventricular zone (SVZ). As group I and II GBM contain stem-like signature, we compared gene expression profiles between these 2 groups of primary GBM and endogenous neural stem progenitor cells to reveal dysregulation of cell cycle, chromatin status, cellular morphogenesis, and signaling pathways in these 2 types of MRI-classified GBM. In the absence of IDH mutation, several genes associated with metabolism are differentially expressed in these subtypes of primary GBM, implicating metabolic reprogramming occurs in tumor microenvironment. Furthermore, histone lysine methyltransferase EZH2 was upregulated while histone lysine demethylases KDM2 and KDM4 were downregulated in both group I and II primary GBM. Lastly, we identified 9 common genes across large data sets of gene expression profiles among MRI-classified group I/II GBM, a large cohort of GBM subtypes from TCGA, and glioma stem cells by unsupervised clustering comparison. These commonly upregulated genes have known functions in cell cycle, centromere assembly, chromosome segregation, and mitotic progression. Our findings highlight altered expression of genes important in chromosome integrity across all GBM, suggesting a common mechanism of disrupted fidelity of chromosome structure in GBM. PMID:28278055

iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.

PubMed

Xiao, Xuan; Wang, Pu; Lin, Wei-Zhong; Jia, Jian-Hua; Chou, Kuo-Chen

2013-05-15

Antimicrobial peptides (AMPs), also called host defense peptides, are an evolutionarily conserved component of the innate immune response and are found among all classes of life. According to their special functions, AMPs are generally classified into ten categories: Antibacterial Peptides, Anticancer/tumor Peptides, Antifungal Peptides, Anti-HIV Peptides, Antiviral Peptides, Antiparasital Peptides, Anti-protist Peptides, AMPs with Chemotactic Activity, Insecticidal Peptides, and Spermicidal Peptides. Given a query peptide, how can we identify whether it is an AMP or non-AMP? If it is, can we identify which functional type or types it belong to? Particularly, how can we deal with the multi-type problem since an AMP may belong to two or more functional types? To address these problems, which are obviously very important to both basic research and drug development, a multi-label classifier was developed based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties. The novel classifier is called iAMP-2L, where "2L" means that it is a 2-level predictor. The 1st-level is to answer the 1st question above, while the 2nd-level is to answer the 2nd and 3rd questions that are beyond the reach of any existing methods in this area. For the conveniences of users, a user-friendly web-server for iAMP-2L was established at http://www.jci-bioinfo.cn/iAMP-2L. Copyright © 2013 Elsevier Inc. All rights reserved.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images

PubMed Central

Hoffman, R.A.; Kothari, S.; Phan, J.H.; Wang, M.D.

2016-01-01

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x106 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered. PMID:27532012

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images.

PubMed

Hoffman, R A; Kothari, S; Phan, J H; Wang, M D

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x10 6 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered.

Prevalence of Ectopic Breast Tissue and Tumor: A 20-Year Single Center Experience.

PubMed

Famá, Fausto; Cicciú, Marco; Sindoni, Alessandro; Scarfó, Paola; Pollicino, Andrea; Giacobbe, Giuseppa; Buccheri, Giancarlo; Taranto, Filippo; Palella, Jessica; Gioffré-Florio, Maria

2016-08-01

Ectopic breast tissue, which includes both supernumerary breast and aberrant breast tissue, is the most common congenital breast abnormality. Ectopic breast cancers are rare neoplasms that occur in 0.3% to 0.6% of all cases of breast cancer. We retrospectively report, using a large series of breast abnormalities diagnosed and treated, our clinical experience on the management of the ectopic breast cancer. In 2 decades, we observed 327 (2.7%) patients with ectopic breast tissue out of a total of 12,177 subjects undergoing a breast visit for lesions. All patients were classified into 8 classes, according to the classification of Kajava, and assessed by a physician examination, ultrasounds, and, when appropriate, further studies with fine needle aspiration cytology and mammography. All specimens were submitted to the anatomo-pathologist. The most frequent benign histological diagnosis was fibrocystic disease. A rare granulosa cell tumor was also found in the right anterior thoracic wall of 1 patient. Four malignancies were also diagnosed in 4 women: an infiltrating lobular cancer in 1 patient with a lesion classified as class I, and an infiltrating apocrine carcinoma, an infiltrating ductal cancer, and an infiltrating ductal cancer with tubular pattern, occurring in 3 patients with lesions classified as class IV. Only 1 recurrence was observed. We recommend an earlier surgical approach for patients with lesions from class I to IV. Copyright © 2016 Elsevier Inc. All rights reserved.

Microarray gene expression profiling using core biopsies of renal neoplasia

PubMed Central

Rogers, Craig G.; Ditlev, Jonathon A.; Tan, Min-Han; Sugimura, Jun; Qian, Chao-Nan; Cooper, Jeff; Lane, Brian; Jewett, Michael A.; Kahnoski, Richard J.; Kort, Eric J.; Teh, Bin T.

2009-01-01

We investigate the feasibility of using microarray gene expression profiling technology to analyze core biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal tumors—comprised of four histological subtypes—following radical nephrectomy using 18-gauge biopsy needles. RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription. Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1), angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology. PMID:19966938

3-D photoacoustic and pulse echo imaging of prostate tumor progression in the mouse window chamber

NASA Astrophysics Data System (ADS)

Bauer, Daniel R.; Olafsson, Ragnar; Montilla, Leonardo G.; Witte, Russell S.

2011-02-01

Understanding the tumor microenvironment is critical to characterizing how cancers operate and predicting their response to treatment. We describe a novel, high-resolution coregistered photoacoustic (PA) and pulse echo (PE) ultrasound system used to image the tumor microenvironment. Compared to traditional optical systems, the platform provides complementary contrast and important depth information. Three mice are implanted with a dorsal skin flap window chamber and injected with PC-3 prostate tumor cells transfected with green fluorescent protein. The ensuing tumor invasion is mapped during three weeks or more using simultaneous PA and PE imaging at 25 MHz, combined with optical and fluorescent techniques. Pulse echo imaging provides details of tumor structure and the surrounding environment with 100-μm3 resolution. Tumor size increases dramatically with an average volumetric growth rate of 5.35 mm3/day, correlating well with 2-D fluorescent imaging (R = 0.97, p < 0.01). Photoacoustic imaging is able to track the underlying vascular network and identify hemorrhaging, while PA spectroscopy helps classify blood vessels according to their optical absorption spectrum, suggesting variation in blood oxygen saturation. Photoacoustic and PE imaging are safe, translational modalities that provide enhanced depth resolution and complementary contrast to track the tumor microenvironment, evaluate new cancer therapies, and develop molecular contrast agents in vivo.

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas.

PubMed

Pyka, Thomas; Gempt, Jens; Hiob, Daniela; Ringel, Florian; Schlegel, Jürgen; Bette, Stefanie; Wester, Hans-Jürgen; Meyer, Bernhard; Förster, Stefan

2016-01-01

Amino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas. One hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival. All FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation did not hold in multivariate analysis. Determination of uptake heterogeneity in pre-therapeutic FET-PET using textural features proved valuable for the (sub-)grading of high-grade glioma as well as prediction of tumor progression and patient survival, and showed improved performance compared to standard parameters such as TBR and tumor volume. Our results underscore the importance of intratumoral heterogeneity in the biology of high-grade glial cell tumors and may contribute to individual therapy planning in the future, although they must be confirmed in prospective studies before incorporation into clinical routine.

Prognostic significance of visceral pleural invasion in the forthcoming (seventh) edition of TNM classification for lung cancer.

PubMed

Shim, Hyo Sup; Park, In Kyu; Lee, Chang Young; Chung, Kyung Young

2009-08-01

The next revision to the TNM classification for lung cancer (the seventh edition) is scheduled to be released in 2009. However, the definition of visceral pleural invasion (VPI), which is a non-size-based T2 descriptor, still lacks in detail, and its validation is not included. We analyzed 1046 cases of non-small cell lung cancer (NSCLC) with T1, T2, or T3 diseases from 1990 to 2005, and subclassified into p0-p3 according to the degrees of pleural invasion. Survival analyses were performed using Kaplan-Meier method. Then, all patients were subdivided into nine groups according to tumor size and pleural invasion, and we compared survival differences, primarily focusing on T2a and T2b diseases according to the seventh edition. There was no survival difference between patients with p1 and p2, thus we regarded p1 or p2 as VPI. There was survival difference between two groups, which are expected to be classified as T2b. The behavior of tumors larger than 5cm but 7cm or less with VPI was similar to T3 tumors. VPI is a poor prognostic factor of NSCLC, and the penetration through the elastic layer of the visceral pleura regardless of its exposure on the pleural surface (pl and p2) should be defined as VPI. This study also indicates that VPI influences T stage dependent on tumor size, and it can be suggested that tumors of larger than 5cm but 7cm or less with VPI should be upgraded to T3 stage.

The influence of dental treatment on the development of osteoradionecrosis after radiotherapy by modern irradiation techniques.

PubMed

Schweyen, Ramona; Stang, Andreas; Wienke, Andreas; Eckert, Alexander; Kuhnt, Thomas; Hey, Jeremias

2017-11-01

The aim of this study was to analyze the influence of dental treatment on the development of osteoradionecrosis (ORN) of the jaw. This study included the data of 776 patients who underwent 3D-CRT or IMRT because of head and neck cancer. Sex, dental status before and after radiotherapy (RT), tumor site, bone surgery during tumor operation, concomitant chemotherapy, and the development of an advanced ORN were documented for each patient. The patients' dentitions before and after RT were classified into four groups with regard to the number and localization of the remaining teeth. Differences between the patients with ORN and patients without ORN with regard to the teeth's condition before and after RT, and with regard to the extent of dental treatment were determined descriptively. Cox proportional hazards regression to study the association between dentition and the development of ORN. The extent of dental treatment in patients with and without ORN did not differ in a clinically relevant way. The highest risk of developing ORN was observed in patients who had undergone primary bone surgery during the tumor operation (HR = 5.58, 95%CI 2.91-10.7) and patients who had a tumor in the oral cavity (HR = 4.84, 95%CI 1.37-17.11). Based on the results of this study, tumor localization and its required treatment are prognostic factors for the development of ORN. After implementing a consequent dental treatment scheme, no influence of dentition on the risk of developing ORN could be demonstrated. Patients with a lower risk could prospectively benefit from a more moderate dental treatment scheme.

SU-D-201-04: Study On the Impact of Tumor Shape and Size On Drug Delivery to Pancreatic Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soltani, M; Bazmara, H; Sefidgar, M

Purpose: Drug delivery to solid tumors can be expressed physically using transport phenomena such as convection and diffusion for the drug of interest within extracellular matrices. We aimed to carefully model these phenomena, and to investigate the effect of tumor shape and size on drug delivery to solid tumors in the pancreas. Methods: In this study, multiple tumor geometries as obtained from clinical PET/CT images were considered. An advanced numerical method was used to simultaneously solve fluid flow and solute transport equations. Data from n=45 pancreatic cancer patients with non-resectable locoregional disease were analyzed, and geometrical information from the tumorsmore » including size, shape, and aspect ratios were classified. To investigate effect of tumor shape, tumors with similar size but different shapes were selected and analyzed. Moreover, to investigate effect of tumor size, tumors with similar shapes but different sizes, ranging from 1 to 77 cm{sup 3}, were selected and analyzed. A hypothetical tumor similar to one of the analyzed tumors, but scaled to reduce its size below 0.2 cm{sup 3}, was also analyzed. Results: The results showed relatively similar average drug concentration profiles in tumors with different sizes. Generally, smaller tumors had higher absolute drug concentration. In the hypothetical tumor, with volume less than 0.2 cm{sup 3}, the average drug concentration was 20% higher in comparison to its counterparts. For the various real tumor geometries, however, the maximum difference between average drug concentrations was 10% for the smallest and largest tumors. Moreover, the results demonstrated that for pancreatic tumors the shape is not significant. The negligible difference of drug concentration in different tumor shapes was due to the minimum effect of convection in pancreatic tumors. Conclusion: In tumors with different sizes, smaller tumors have higher drug delivery; however, the impact of tumor shape in the case of pancreatic tumors is not significant.« less

MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation.

PubMed

Cheerla, Nikhil; Gevaert, Olivier

2017-01-13

The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome. Here we go a step further, using tissue miRNA and clinical data across 21 cancers from the 'The Cancer Genome Atlas' (TCGA) database. We use machine learning techniques to create an accurate pan-cancer diagnosis system, and a prediction model for treatment outcomes. Finally, using these models, we create a web-based tool that diagnoses cancer and recommends the best treatment options. We achieved 97.2% accuracy for classification using a support vector machine classifier with radial basis. The accuracies improved to 99.9-100% when climbing up the embryonic tree and classifying cancers at different stages. We define the accuracy as the ratio of the total number of instances correctly classified to the total instances. The classifier also performed well, achieving greater than 80% sensitivity for many cancer types on independent validation datasets. Many miRNAs selected by our feature selection algorithm had strong previous associations to various cancers and tumor progression. Then, using miRNA, clinical and treatment data and encoding it in a machine-learning readable format, we built a prognosis predictor model to predict the outcome of treatment with 85% accuracy. We used this model to create a tool that recommends personalized treatment regimens. Both the diagnosis and prognosis model, incorporating semi-supervised learning techniques to improve their accuracies with repeated use, were uploaded online for easy access. Our research is a step towards the final goal of diagnosing cancer and predicting treatment recommendations using non-invasive blood tests.

Are all pelvic (nonuterine) serous carcinomas of tubal origin?

PubMed

Przybycin, Christopher G; Kurman, Robert J; Ronnett, Brigitte M; Shih, Ie-Ming; Vang, Russell

2010-10-01

It has been proposed that the presence of tubal intraepithelial carcinoma (TIC), in association with one-third to nearly half of pelvic serous carcinomas, is evidence of fallopian tube origin for high-grade serous carcinomas that would have been otherwise classified as primary ovarian or peritoneal. To address this hypothesis, we evaluated a series of 114 consecutive pelvic (nonuterine) gynecologic carcinomas at our institution (2006 to 2008) to determine the frequency of TIC in 52 cases in which all the resected fallopian tube tissue was examined microscopically. These 52 cases were classified as ovarian (n=37), peritoneal (n=8), or fallopian tube (n=7) in origin as per conventional criteria based on disease distribution. The presence of TIC and its location and relationship to invasive carcinoma in the fallopian tubes and ovaries were assessed. Among the 45 cases of ovarian/peritoneal origin, carcinoma subtypes included 41 high-grade serous, 1 endometrioid, 1 mucinous, 1 high-grade, not otherwise specified, and 1 malignant mesodermal mixed tumor. TIC was identified in 24 cases (59%) of high-grade serous carcinoma but not among any of the other subtypes; therefore, the term serous TIC (STIC) is a more specific appellation. STICs were located in the fimbriated end of the tube in 22 cases (92%) and in the ampulla in 2 (8%); they were unilateral in 21 (88%) and bilateral in 3 (13%). STICs in the absence of an associated invasive carcinoma in the same tube were detected in 7 cases (30%) and with invasive carcinoma in the same tube in 17 (71%). Unilateral STICs were associated with bilateral ovarian involvement in 15 cases and unilateral (ipsilateral) ovarian involvement in 5 (the remaining case with a unilateral STIC had a primary peritoneal tumor with no ovarian involvement); the bilateral STICs were all associated with bilateral ovarian involvement. Six of the 7 primary tubal tumors were high-grade serous carcinomas, and 4 of these 6 (67%) had STICs. Based on conventional criteria, 70%, 17%, and 13% of high-grade serous carcinomas qualified for classification as ovarian, peritoneal, and tubal in origin, respectively; however, using STIC as a supplemental criterion to define a case as tubal in origin, the distribution was modified to 28%, 8%, and 64%, respectively. Features of tumors in the ovary that generally suggest metastatic disease (bilaterality, small size, nodular growth pattern, and surface plaques) were identified with similar frequency in cases with and without STIC and were, therefore, not predictive of tubal origin. The findings, showing that nearly 60% of high-grade pelvic (nonuterine) serous carcinomas are associated with STICs, are consistent with the proposal that the fallopian tube is the source of a majority of these tumors. If these findings can be validated by molecular studies that definitively establish that STIC is the earliest form of carcinoma rather than intraepithelial spread from adjacent invasive serous carcinoma of ovarian or peritoneal origin, they will have important clinical implications for screening, treatment, and prevention.

Specificity and sensitivity of ⁹⁹mTc-EDDA/HYNIC-Tyr³-octreotide (⁹⁹mTc-TOC) for imaging neuroendocrine tumors.

PubMed

Sepúlveda-Méndez, Jesús; de Murphy, Consuelo Arteaga; Pedraza-López, Martha; Murphy-Stack, Eduardo; Rojas-Bautista, Juan Carlos; González-Treviño, Ofelia

2012-01-01

Gastroenteropancreatic neuroendocrine tumors (NETs) are cancers originating from neuroendocrine organs such as the pancreas, pituitary, thyroid, and adrenal glands and tumors arising from the diffuse neuroendocrine cells that are widely distributed throughout the body. NETs express somatostatin (SS) and contain a high density of SS receptors; therefore, they can be specifically targeted with SS-based radiopharmaceuticals. The aim of this research was to determine the validity in terms of specificity, sensitivity, and the agreement beyond chance with the biopsy (gold standard) of the ⁹⁹mTc-EDDA-HYNIC-Tyr³octreotide (⁹⁹mTc-TOC) to image and localize NETs and their metastases. Freeze-dried kits containing 0.0125 mg HYNIC-octreotide and co-ligands were easily labeled and quality controlled within the hospital radiopharmacy. Fifty-six consecutive Mexican patients with a previous presumptive diagnosis of NETs underwent several clinical and laboratory studies and were referred to the Nuclear Medicine Department for a routine scan with ⁹⁹mTc-TOC. The patients were injected with 500-600 MBq ⁹⁹mTc-TOC, and whole-body images were obtained 2 h later with a SPECT or a SPECT/CT camera. Two nuclear medicine physicians observed the images and classified them as 17 negative and 39 positive. After correlating the image of each patient with our 'gold standard' (biopsy, clinical history, morphological images, and tumor marker assays), the ⁹⁹mTc-TOC images were classified by the same two physicians as 12 true negatives, five false negatives, 38 true positives and one false positive. The validity of ⁹⁹mTc-TOC in terms of relative frequencies with corresponding 95% confidence intervals were as follows: 92.3% (64-100%) specificity; 88.4% (78-97%) sensitivity; and the agreement beyond chance was 73% (60-84%). The positive predictive value was 97.4% (87-100%); the negative predicted value was 70.6% (48-93%); the accuracy was 89.3% (89-97%); and the prevalence was 76.8% (64-87%). Because of these high values, we strongly recommend scintigraphy with the Mexican-produced ⁹⁹mTc-TOC for the localization of NETs and their metastases, and we conclude that it is a good tool for detecting neuroendocrine disease in a Mexican population.

Cytological Study of Grade 3 Endometrioid Adenocarcinoma of Endometrial Origin: Cytoarchitecture and Features of Cell Clusters Assessed With Endometrial Brushing Cytology--Focusing on a comparison with endometrioid adenocarcinoma Grade 1, 2.

PubMed

Matsui, Naruaki; Kajiwara, Hiroshi; Morishita, Akihiro; Tsukada, Hitomi; Nakazawa, Kazumi; Miyazawa, Masaki; Mikami, Mikio; Nakamura, Naoya; Sato, Shinkichi

2015-06-20

Aim of study was to clarify the cytological characteristics of grade 3 endometrioid adenocarcinoma of endometrial origin (G3 EA) by endometrial brushing cytology. The subjects were 11 patients in whom G3 EA was diagnosed by review of preoperative cytological specimens obtained at our hospital and related institutions between 2000 and 2010. These patients were investigated with respect to the preoperative cytological diagnosis, background changes, cell cluster patterns, and individual cellular findings. Background changes were classified as inflammatory or tumorous, while cell clusters were classified as overlapping cell cluster, sheet-like cell cluster, clump of high dense gland, papillary, or other cell cluster. Cellular findings were investigated by comparing the incidence of squamous and clear cell metaplasia, the nuclear rounding rate, and the nuclear area with the findings in a control group (35 patients with G1-2 EA). Background changes were classified as inflammatory in 63.6% and necrotic in 36.4%. The cell clusters were classified as overlapping cell cluster in 44.8%, cell cluster in 21.7%, clump of high dense gland in 10.0%, papillary in 4.0%, and other cell cluster in 19.5%. The incidence of squamous and clear cell metaplasia was 27.2% and 18.1%, respectively. The mean nuclear rounding rate was 0.97, and the mean nuclear area was 55.98 µm2. Investigation of the cytoarchitecture of G3 EA with endometrial brushing cytology revealed overlapping cell cluster and tumor cells of a relatively uniform size. These findings suggest that it is necessary to recognize that there are differences between the cytological findings of G3 EA and the usual features of G1-2 EA.

Peripheral vascular tumors and vascular malformations: imaging (magnetic resonance imaging and conventional angiography), pathologic correlation and treatment options.

PubMed

El-Merhi, Fadi; Garg, Deepak; Cura, Marco; Ghaith, Ola

2013-02-01

Vascular anomalies are classified into vascular tumors (infantile hemangioma) and vascular malformations. Vascular malformations are divided into slow flow and high flow subtypes. Magnetic resonance imaging helps in classification and assessing extent and distribution. Conventional angiography also known as digital subtraction angiography is pivotal in assessment of fine vascular details and treatment planning. Imaging correlates well with histopathology. We review recent development in imaging techniques of various vascular anomalies most of which are affecting the peripheral system which potentially may broaden understanding of their diagnosis, classification and treatment.

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

2014-03-01

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.

Sporadic pediatric meningiomas: a neuroradiological and neuropathological study of 15 cases.

PubMed

Huntoon, Kristin; Pluto, Charles P; Ruess, Lynne; Boué, Daniel R; Pierson, Christopher R; Rusin, Jerome A; Leonard, Jeffrey

2017-08-01

OBJECTIVE Sporadic meningiomas have been classified in many different ways. Radiographically, these lesions can be described as occurring in either typical or atypical locations. The purpose of this study was to determine if there are any histopathological differences between sporadic meningiomas that arise in these varying locations in children. METHODS The neuroimaging, histopathological findings, and clinical records in patients with sporadic pediatric meningiomas not associated with neurofibromatosis Type 2 or prior radiation therapy were retrospectively reviewed. Tumors were classified by radiological findings as either typical or atypical, and they were categorized histopathologically by using the latest WHO nomenclature and grading criteria. RESULTS Fifteen sporadic meningiomas in pediatric patients were biopsied or resected at the authors' institution between 1989 and 2013. Five (33%) were typical in radiographic appearance and/or location and 10 (67%) were atypical. Four (80%) typical meningiomas were WHO Grade I tumors. Most (60%) of the atypical meningiomas were WHO Grade II or III. CONCLUSIONS This study is the largest series of sporadic pediatric meningiomas in atypical locations to date. Although sporadic meningiomas are relatively infrequent in children, those with atypical imaging, specifically those with apparently intraparenchymal and intraosseous locations, may be more common than previously recognized. In this study, pediatric sporadic meningiomas arising in atypical locations, in particular intraparenchymal meningiomas, may be of higher histopathological grade. The authors' findings should alert clinicians to the potential for more aggressive clinical behavior in these tumors.

Fractal analysis of scatter imaging signatures to distinguish breast pathologies

NASA Astrophysics Data System (ADS)

Eguizabal, Alma; Laughney, Ashley M.; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.; López-Higuera, José M.; Conde, Olga M.

2013-02-01

Fractal analysis combined with a label-free scattering technique is proposed for describing the pathological architecture of tumors. Clinicians and pathologists are conventionally trained to classify abnormal features such as structural irregularities or high indices of mitosis. The potential of fractal analysis lies in the fact of being a morphometric measure of the irregular structures providing a measure of the object's complexity and self-similarity. As cancer is characterized by disorder and irregularity in tissues, this measure could be related to tumor growth. Fractal analysis has been probed in the understanding of the tumor vasculature network. This work addresses the feasibility of applying fractal analysis to the scattering power map (as a physical modeling) and principal components (as a statistical modeling) provided by a localized reflectance spectroscopic system. Disorder, irregularity and cell size variation in tissue samples is translated into the scattering power and principal components magnitude and its fractal dimension is correlated with the pathologist assessment of the samples. The fractal dimension is computed applying the box-counting technique. Results show that fractal analysis of ex-vivo fresh tissue samples exhibits separated ranges of fractal dimension that could help classifier combining the fractal results with other morphological features. This contrast trend would help in the discrimination of tissues in the intraoperative context and may serve as a useful adjunct to surgeons.

Anomaly detection for medical images based on a one-class classification

NASA Astrophysics Data System (ADS)

Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

2018-02-01

Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.

Prevalence profile of odontogenic cysts and tumors on Brazilian sample after the reclassification of odontogenic keratocyst.

PubMed

Jaeger, Filipe; de Noronha, Mariana Saturnino; Silva, Maiza Luiza Vieira; Amaral, Márcio Bruno Figueiredo; Grossmann, Soraya de Mattos Carmago; Horta, Martinho Campolina Rebello; de Souza, Paulo Eduardo Alencar; de Aguiar, Maria Cássia Ferreira; Mesquita, Ricardo Alves

2017-02-01

The aim of this study was to evaluate the impact of the reclassification of odontogenic keratocyst (OKC) as a tumor on the prevalence profile of odontogenic cysts (OCs) and odontogenic tumors (OTs). Two referral Oral and Maxillofacial Pathology services in Brazil were evaluated. All cases diagnosed as OCs or OTs were selected and classified according to the 1992 WHO-classification (cases before 2005 WHO classification of tumors excluding OKC) and the 2005 WHO classification of tumors, going forward including cases of odontogenic keratocyst tumor (KCOT). The frequency and prevalence of OCs and OTs were compared before and after the reclassification. Among 27,854 oral biopsies, 4920 (17.66%) were OCs and 992 (3.56%) were OTs. The prevalence of OTs before 2005 WHO classification of tumors was 2.04%, while the prevalence after 2005 WHO classification was 11.51% (p < 0.0001). Before 2006, the most frequent tumor diagnosed was odontoma with 194 cases (39.67%), and after 2005 WHO classification of tumors the KCOT was the most frequent with 207 cases (41.07%). The increase in the prevalence of OTs after 2005 WHO is related to the improvement of pathology services and to the inclusion of KCOT in the OTs group. Copyright © 2016 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Diagnostic Ability of Percutaneous Needle Biopsy Immediately After Radiofrequency Ablation for Malignant Lung Tumors: An Initial Experience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasegawa, Takaaki, E-mail: t-hasegawa@aichi-cc.jp; Kondo, Chiaki; Sato, Yozo

PurposeTo evaluate the safety and diagnostic ability of percutaneous needle biopsy performed immediately after lung radiofrequency ablation (RFA).Materials and MethodsFrom May 2013 to April 2014, percutaneous needle biopsy was performed immediately after RFA for 3 patients (2 men and 1 woman, aged 57–76 years) who had lung tumors measuring 1.3–2.6 cm in diameter. All patients had prior history of malignancy, and all tumors were radiologically diagnosed as malignant. Obtained specimens were pathologically classified using standard hematoxylin and eosin staining.ResultsWe completed three planned sessions of RFA followed by percutaneous needle biopsy, all of which obtained tumor tissue that could be pathologically diagnosed. Twomore » tumors were metastatic from renal clear cell carcinoma and rectal adenocarcinoma, respectively; one tumor was primary lung adenocarcinoma. There was no death or major complication related to the procedures. Although pneumothorax occurred in two patients, these resolved without the need for aspiration or chest tube placement. Tumor seeding was not observed, but 21 months after the procedure, one case developed local tumor progression that was treated by additional RFA.ConclusionPathologic diagnosis was possible by needle biopsy immediately after RFA for lung tumors. This technique may reduce the risks and efforts of performing biopsy and RFA on separate occasions.« less

The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer

PubMed Central

2013-01-01

Background We have recently reported on the changes in plasma free amino acid (PFAA) profiles in lung cancer patients and the efficacy of a PFAA-based, multivariate discrimination index for the early detection of lung cancer. In this study, we aimed to verify the usefulness and robustness of PFAA profiling for detecting lung cancer using new test samples. Methods Plasma samples were collected from 171 lung cancer patients and 3849 controls without apparent cancer. PFAA levels were measured by high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Results High reproducibility was observed for both the change in the PFAA profiles in the lung cancer patients and the discriminating performance for lung cancer patients compared to previously reported results. Furthermore, multivariate discriminating functions obtained in previous studies clearly distinguished the lung cancer patients from the controls based on the area under the receiver-operator characteristics curve (AUC of ROC = 0.731 ~ 0.806), strongly suggesting the robustness of the methodology for clinical use. Moreover, the results suggested that the combinatorial use of this classifier and tumor markers improves the clinical performance of tumor markers. Conclusions These findings suggest that PFAA profiling, which involves a relatively simple plasma assay and imposes a low physical burden on subjects, has great potential for improving early detection of lung cancer. PMID:23409863

Analysis of dual tree M-band wavelet transform based features for brain image classification.

PubMed

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

2018-04-29

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification. © 2018 International Society for Magnetic Resonance in Medicine.

Histopathology of the tissue adhering to the multiple tine expandable electrodes used for radiofrequency ablation of hepatocellular carcinoma predicts local recurrence.

PubMed

Ishikawa, Toru; Kubota, Tomoyuki; Abe, Hiroyuki; Nagashima, Aiko; Hirose, Kanae; Togashi, Tadayuki; Seki, Keiichi; Honma, Terasu; Yoshida, Toshiaki; Kamimura, Tomoteru; Nemoto, Takeo; Takeda, Keiko; Ishihara, Noriko

2012-01-01

To assess the ability to predict the local recurrence of hepatocellular carcinoma by analyzing tissues adhering to the radiofrequency ablation probe after complete ablation. From May 2002 to March 2011, tissue specimens adhering to the radiofrequency ablation probe from 284 radiofrequency ablation sessions performed for hepatocellular carcinomas ≤3 cm in size were analyzed. The specimens were classified as either viable tumor tissue or complete necrosis, and the local recurrence rates were calculated using the Kaplan-Meier method. From the tumors ≤3 cm in size, viable tissue was present in 6 (2.1%) of 284 specimens, and the local recurrence rates after 1 and 2 years of follow-up were 6.7% and 11.2%, respectively. Local recurrence developed significantly earlier in the viable tissue group. The recurrence rate was not significantly different based on whether transcatheter arterial chemoembolization was performed. The histopathology of the tissue adhering to the radiofrequency ablation probes used for hepatocellular carcinoma treatment can predict local recurrence. Additional aggressive treatment for patients with viable tissue can therefore improve the overall survival.

Using molecular diagnostic testing to personalize the treatment of patients with gastrointestinal stromal tumors.

PubMed

Bannon, Amber E; Klug, Lillian R; Corless, Christopher L; Heinrich, Michael C

2017-05-01

The diagnosis and treatment of gastrointestinal stromal tumor (GIST) has emerged as a paradigm for modern cancer treatment ('precision medicine'), as it highlights the importance of matching molecular defects with specific therapies. Over the past two decades, the molecular classification and diagnostic work up of GIST has been radically transformed, accompanied by the development of molecular therapies for specific subgroups of GIST. This review summarizes the developments in the field of molecular diagnosis of GIST, particularly as they relate to optimizing medical therapy. Areas covered: Based on an extensive literature search of the molecular and clinical aspects of GIST, the authors review the most important developments in this field with an emphasis on the differential diagnosis of GIST including mutation testing, therapeutic implications of each molecular subtype, and emerging technologies relevant to the field. Expert commentary: The use of molecular diagnostics to classify GIST has been shown to be successful in optimizing patient treatment, but these methods remain under-utilized. In order to facilitate efficient and comprehensive molecular testing, the authors have developed a decision tree to aid clinicians.

2015 Guidance on cancer immunotherapy development in early-phase clinical studies.

PubMed

2015-12-01

The development of cancer immunotherapies is progressing rapidly with a variety of technological approaches. They consist of "cancer vaccines", which are based on the idea of vaccination, "effector cell therapy", classified as passive immunotherapy, and "inhibition of immunosuppression", which intends to break immunological tolerance to autoantigens or immunosuppressive environments characterizing antitumor immune responses. Recent reports showing clinical evidence of efficacy of immune checkpoint inhibitors and adoptive immunotherapies with tumor-infiltrating lymphocytes and tumor-specific receptor gene-modified T cells indicate the beginning of a new era for cancer immunotherapy. This guidance summarizes ideas that will be helpful to those who plan to develop cancer immunotherapy. The aims of this guidance are to discuss and offer important points in early phase clinical studies of innovative cancer immunotherapy, with future progress in this field, and to contribute to the effective development of cancer immunotherapy aligned with the scope of regulatory science. This guidance covers cancer vaccines, effector cell therapy, and inhibition of immunosuppression, including immune checkpoint inhibitors. © 2015 The Authors. Cancer Science published by Wiley Publishing Asia Pty Ltd on behalf of Japanese Cancer Association.

Zero mortality in more than 300 hepatic resections: validity of preoperative volumetric analysis.

PubMed

Itoh, Shinji; Shirabe, Ken; Taketomi, Akinobu; Morita, Kazutoyo; Harimoto, Norifumi; Tsujita, Eiji; Sugimachi, Keishi; Yamashita, Yo-Ichi; Gion, Tomonobu; Maehara, Yoshihiko

2012-05-01

We reviewed a series of patients who underwent hepatic resection at our institution, to investigate the risk factors for postoperative complications after hepatic resection of liver tumors and for procurement of living donor liver transplantation (LDLT) grafts. Between April 2004 and August 2007, we performed 304 hepatic resections for liver tumors or to procure grafts for LDLT. Preoperative volumetric analysis was done using 3-dimensional computed tomography (3D-CT) prior to major hepatic resection. We compared the clinicopathological factors between patients with and without postoperative complications. There was no operative mortality. According to the 3D-CT volumetry, the mean error ratio between the actual and the estimated remnant liver volume was 13.4%. Postoperative complications developed in 96 (31.6%) patients. According to logistic regression analysis, histological liver cirrhosis and intraoperative blood loss >850 mL were significant risk factors of postoperative complications after hepatic resection. Meticulous preoperative evaluation based on volumetric analysis, together with sophisticated surgical techniques, achieved zero mortality and minimized intraoperative blood loss, which was classified as one of the most significant predictors of postoperative complications after major hepatic resection.

Primary Ewing's sarcoma of the sinonasal tract in adults: A challenging disease.

PubMed

Lombardi, Davide; Mattavelli, Davide; Redaelli De Zinis, Luca O; Accorona, Remo; Morassi, Maria L; Facchetti, Fabio; Ferrari, Vittorio; Farina, Davide; Bertulli, Rossella; Nicolai, Piero

2017-03-01

Sinonasal localization of Ewing's sarcoma in adults is an exceedingly rare event. The clinical records of 5 patients with primary sinonasal Ewing's sarcoma treated from 1992 to 2012 were retrospectively analyzed. All pathologic slides were reviewed by 2 experienced pathologists. All patients underwent multimodality treatments. Median age was 36 years (range, 25-52 years). At referral, 2 patients had the original diagnosis changed by review of the histologic slides. Tumors were classified as T4aN0M0 (4 patients) and T2N0M0 (1 patient). Median follow-up was 110 months (range, 70-139 months). Only 1 patient, who started treatment elsewhere based on an incorrect histologic diagnosis, experienced multiple recurrences and eventually died of widespread metastasis. Correct pathologic diagnosis can have a crucial impact on treatment planning and outcome. Multimodality therapy is the key for long-term successful results. Because of the rarity of the tumor, referral to highly experienced care centers is strongly recommended. © 2016 Wiley Periodicals, Inc. Head Neck 39: E45-E50, 2017. © 2016 Wiley Periodicals, Inc.

Urban-rural differences in breast cancer incidence by hormone receptor status across 6 years in Egypt

PubMed Central

Dey, Subhojit; Soliman, Amr S.; Hablas, Ahmad; Seifeldin, Ibrahim A.; Ismail, Kadry; Ramadan, Mohamed; El-Hamzawy, Hesham; Wilson, Mark L.; Banerjee, Mousumi; Boffetta, Paolo; Harford, Joe; Merajver, Sofia D.

2009-01-01

Breast cancer incidence is higher in developed countries with higher rates of estrogen receptor positive (ER+) tumors. ER+ tumors are caused by estrogenic exposures although known exposures explain approximately 50% of breast cancer risk. Unknown risk factors causing high breast cancer incidence exist that are estrogenic and development-related. Xenoestrogens are such risk factors but are difficult to study since developed countries lack unexposed populations. Developing countries have urban-rural populations with differential exposure to xenoestrogens. This study assessed urban-rural breast cancer incidence classified by hormone receptor status using data from Gharbiah population-based cancer registry in Egypt from 2001 to 2006. Urban ER+ incidence rate (per 100,000 women) was 2-4 times (IRR = 3.36, 95% CI = 4.84, 2.34) higher than rural incidence rate. ER− incidence rate was 2-3 times (IRR = 1.86, 95% CI = 2.38, 1.45) higher in urban areas than in rural areas. Our findings indicate that urban women may probably have a higher exposure to xenoestrogens. PMID:19548084

Does prostate acinar adenocarcinoma with Gleason Score 3+3=6 have the potential to metastasize?

PubMed

Montironi, Rodolfo; Scarpelli, Marina; Mazzucchelli, Roberta; Lopez-Beltran, Antonio; Santoni, Matteo; Briganti, Alberto; Montorsi, Francesco; Cheng, Liang

2014-10-18

There is a worldwide debate involving clinicians, uropathologists as well as patients and their families on whether Gleason score 6 adenocarcinoma should be labelled as cancer. We report a case of man diagnosed with biopsy Gleason score 6 acinar adenocarcinoma and classified as low risk (based on a PSA of 5 ng/mL and stage cT2a) whose radical prostatectomy specimen initially showed organ confined Gleason score 3+3=6, WHO nuclear grade 3, acinar adenocarcinoma with lymphovascular invasion and secondary deposit in a periprostatic lymph node. When deeper sections were cut to the point that almost all the slice present in the paraffin block was sectioned, a small tumor area (<5% of the whole tumor) of Gleason pattern 4 (poorly formed glands) was found in an extraprostatic position. The epilogue was that the additional finding changed the final Gleason score to 3+3=6 with tertiary pattern 4 and the stage to pT3a. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_190.

Terahertz spectroscopic investigation of human gastric normal and tumor tissues

NASA Astrophysics Data System (ADS)

Hou, Dibo; Li, Xian; Cai, Jinhui; Ma, Yehao; Kang, Xusheng; Huang, Pingjie; Zhang, Guangxin

2014-09-01

Human dehydrated normal and cancerous gastric tissues were measured using transmission time-domain terahertz spectroscopy. Based on the obtained terahertz absorption spectra, the contrasts between the two kinds of tissue were investigated and techniques for automatic identification of cancerous tissue were studied. Distinctive differences were demonstrated in both the shape and amplitude of the absorption spectra between normal and tumor tissue. Additionally, some spectral features in the range of 0.2~0.5 THz and 1~1.5 THz were revealed for all cancerous gastric tissues. To systematically achieve the identification of gastric cancer, principal component analysis combined with t-test was used to extract valuable information indicating the best distinction between the two types. Two clustering approaches, K-means and support vector machine (SVM), were then performed to classify the processed terahertz data into normal and cancerous groups. SVM presented a satisfactory result with less false classification cases. The results of this study implicate the potential of the terahertz technique to detect gastric cancer. The applied data analysis methodology provides a suggestion for automatic discrimination of terahertz spectra in other applications.

Clinicopathologic features of ovarian neoplasms with emphasis on borderline ovarian tumors: an institutional perspective.

PubMed

Hashmi, Atif Ali; Hussain, Zubaida Fida; Bhagwani, Aneel Roy; Edhi, Muhammad Muzzammil; Faridi, Naveen; Hussain, Syed Danish; Khan, Mehmood

2016-04-06

Ovarian cancer is the most lethal gynecologic malignancy and it represents third most common malignancy in Karachi (after breast and oral cancer). Due to lack of well established cancer registry in our country, changing trends of ovarian tumors has not been determined. Therefore we aimed to establish the current trends and classification of ovarian tumors in our setup according to latest WHO guidelines. We retrospectively analyzed 162 cases of ovarian tumors that underwent surgical resection from January 2009 till December 2014. Specimens were received in histopathology department, Liaquat National hospital and cases were examined by senior histopathologists and classified according to latest WHO guidelines. Various histopathologic parameters including capsular invasion, omental and lymph node meatstasis along with uterine and fallopian tube involvement were determined apart from tumor type and grade. Mean age at diagnosis was 35.8 years (± 15.5). surface epithelial tumors were most common, 109 cases (67.2%) followed by germ cell tumors, 44 cases (27.1%) and sex cord stromal tumors, 8 cases (4.9%). Serous tumors were most common surface epithelial tumors with 90% benign morphology. On the other hand, mucinous tumors showed a higher percentage of borderline and malignant features (16.7 and 14.6% respectively). Higher incidence of capsular invasion and omental metastasis was noted in endometroid and serous carcinoma compared to mucinous tumors. We noted a higher frequency of young age ovarian cancers in our set up. Serous and endometroid carcinomas were found to be associated with adverse prognostic factors like capsular invasion and omental metastasis. Moreover a significantly higher proportion of ovarian tumors constitute mucinous histology including borderline tumors. Whether this represents a changing trend towards biology of these tumors in this part of the world needs to be uncovered by further studies.