Pío del Río-Hortega: A Visionary in the Pathology of Central Nervous System Tumors

PubMed Central

Ramon y Cajal Agüeras, Santiago

2016-01-01

The last 140 years have seen considerable advances in knowledge of central nervous system tumors. However, the main tumor types had already been described during the early years of the twentieth century. The studies of Dr. Pío del Río Hortega have been ones of the most exhaustive histology and cytology-based studies of nervous system tumors. Río Hortega's work was performed using silver staining methods, which require a high level of practical skill and were therefore difficult to standardize. His technical aptitude and interest in nervous system tumors played a key role in the establishment of his classification, which was based on cell lineage and embryonic development. Río Hortega's approach was controversial when he proposed it. Current classifications are not only based on cell type and embryonic lineage, as well as on clinical characteristics, anatomical site, and age. PMID:26973470

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.

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

Differences in the molecular biology of adenocarcinoma of the esophagus, gastric cardia, and upper gastric third.

PubMed

Lehmann, Kuno; Schneider, Paul M

2010-01-01

Adenocarcinoma of the distal esophagus, gastric cardia, and upper gastric third are grouped in type I-III by the Siewert classification. This classification is based on the endoscopic localisation of the tumor center, and is the most important diagnostic tool to group these tumors. On a molecular level, there is currently no marker that would allow to differentiate the three different types. Furthermore, the Siewert classification was not uniformly used in the recent literature, making interpretation and generalization of these results difficult. However, several potential targets have been identified that may help to separate these tumors by molecular markers, and are summarized in this chapter.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

PubMed

Zhan, Tianming; Chen, Yi; Hong, Xunning; Lu, Zhenyu; Chen, Yunjie

2017-01-01

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Oligoastrocytoma

MedlinePlus

... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ...

Oligodendroglioma

MedlinePlus

... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ... Pineal Tumor Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary ...

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

[Changes of 2015 WHO Histological Classification of Lung Cancer 
and the Clinical Significance].

PubMed

Yang, Xin; Lin, Dongmei

2016-06-20

Due in part to remarkable advances over the past decade in our understanding of lung cancer, particularly in area of medical oncology, molecular biology, and radiology, there is a pressing need for a revised classification, based not on pathology alone, but rather on an integrated multidisciplinary approach to classification of lung cancer. The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The revised classification has been greatly improved in helping advance the field, increasing the impact of research, improving patient care and assisting in predicting outcome. The most significant changes will be summarized in this paper as follows: (1) main changes of lung adenocarcinoma as proposed by the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) classification, (2) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (3) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (4) grouping of neuroendocrine tumors together in one category, (5) and the current viewpoint of histologic grading of lung cancer.

Cancer classification in the genomic era: five contemporary problems.

PubMed

Song, Qingxuan; Merajver, Sofia D; Li, Jun Z

2015-10-19

Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here, we discuss the classification of cancer and the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., "lung cancer" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor cohorts, have provided not only new insights into the biological diversity of human cancers but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here, we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. (1) How many cancer subtypes are there? (2) How can we evaluate the robustness of a new classification system? (3) How are classification systems affected by intratumor heterogeneity and tumor evolution? (4) How should we interpret cancer subtypes? (5) Can multiple classification systems co-exist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Exploration of these problems is essential for data-driven refinement of cancer classification and the successful application of these concepts in precision medicine.

[New TNM classification of lung tumors].

PubMed

Wittekind, C

2014-11-01

The TNM classification of lung tumors has undergone many changes in the seventh edition published in 2010. These changes reflect current data and are based on the findings of the International Association for the Study of Lung Cancer (IASLC) from 81,495 patients and concern definitions of the T and M categories as well as stage grouping. They include a better description of regional lymph nodes of the lungs based on uniformly accepted definitions by the IASLC. The changes can lead to problems in the use of the definitions and will be discussed.

Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

NASA Astrophysics Data System (ADS)

Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

2018-02-01

Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

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

Classification of TP53 mutations and HPV predict survival in advanced larynx cancer.

PubMed

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B; Walline, Heather M; Prince, Mark E; Urba, Susan; Wolf, Gregory T; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E; Bradford, Carol

2016-09-01

Assess tumor suppressor p53 (TP53) functional mutations in the context of other biomarkers in advanced larynx cancer. Prospective analysis of pretreatment tumor TP53, human papillomavirus (HPV), Bcl-xL, and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. TP53 exons 4 through 9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl, and cyclin D1 expression. TP53 mutations were found in 22 of 58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13 of 58 (22.4%) patients, nonsense mutations in four of 58 (6.9%), and deletions in five of 58 (8.6%). High-risk HPV was found in 20 of 52 (38.5%) tumors. A classification based on Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low-risk mutations (P = 0.0315). A model including this TP53 classification, HPV status, cyclin D1, and Bcl-xL staining significantly predicts survival (P = 0.0017). EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. NA. Laryngoscope, 126:E292-E299, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

The fallopian canal: a comprehensive review and proposal of a new classification.

PubMed

Mortazavi, M M; Latif, B; Verma, K; Adeeb, N; Deep, A; Griessenauer, C J; Tubbs, R S; Fukushima, T

2014-03-01

The facial nerve follows a complex course through the skull base. Understanding its anatomy is crucial during standard skull base approaches and resection of certain skull base tumors closely related to the nerve, especially, tumors at the cerebellopontine angle. Herein, we review the fallopian canal and its implications in surgical approaches to the skull base. Furthermore, we suggest a new classification. Based on the anatomy and literature, we propose that the meatal segment of the facial nerve be included as a component of the fallopian canal. A comprehensive knowledge of the course of the facial nerve is important to those who treat patients with pathology of or near this cranial nerve.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Proposal for a new risk stratification classification for meningioma based on patient age, WHO tumor grade, size, localization, and karyotype

PubMed Central

Domingues, Patrícia Henriques; Sousa, Pablo; Otero, Álvaro; Gonçalves, Jesus Maria; Ruiz, Laura; de Oliveira, Catarina; Lopes, Maria Celeste; Orfao, Alberto; Tabernero, Maria Dolores

2014-01-01

Background Tumor recurrence remains the major clinical complication of meningiomas, the majority of recurrences occurring among WHO grade I/benign tumors. In the present study, we propose a new scoring system for the prognostic stratification of meningioma patients based on analysis of a large series of meningiomas followed for a median of >5 years. Methods Tumor cytogenetics were systematically investigated by interphase fluorescence in situ hybridization in 302 meningioma samples, and the proposed classification was further validated in an independent series of cases (n = 132) analyzed by high-density (500K) single-nucleotide polymorphism (SNP) arrays. Results Overall, we found an adverse impact on patient relapse-free survival (RFS) for males, presence of brain edema, younger patients (<55 years), tumor size >50 mm, tumor localization at intraventricular and anterior cranial base areas, WHO grade II/III meningiomas, and complex karyotypes; the latter 5 variables showed an independent predictive value in multivariate analysis. Based on these parameters, a prognostic score was established for each individual case, and patients were stratified into 4 risk categories with significantly different (P < .001) outcomes. These included a good prognosis group, consisting of approximately 20% of cases, that showed a RFS of 100% ± 0% at 10 years and a very poor-prognosis group with a RFS rate of 0% ± 0% at 10 years. The prognostic impact of the scoring system proposed here was also retained when WHO grade I cases were considered separately (P < .001). Conclusions Based on this risk-stratification classification, different strategies may be adopted for follow-up, and eventually also for treatment, of meningioma patients at different risks for relapse. PMID:24536048

Classification of TP53 Mutations and HPV Predict Survival in Advanced Larynx Cancer

PubMed Central

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B.; Walline, Heather M.; Prince, Mark E.; Urba, Susan; Wolf, Gregory T.; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E.; Bradford, Carol

2016-01-01

OBJECTIVE Assess TP53 functional mutations in the context of other biomarkers in advanced larynx cancer. STUDY DESIGN Prospective analysis of pretreatment tumor TP53, HPV, Bcl-xL and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. METHODS TP53 exons 4-9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl and cyclin D1 expression. RESULTS TP53 Mutations were found in 22/58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13/58 (22.4%) patients, nonsense mutations in 4/58 (6.9%), and deletions in 5/58 (8.6%). High risk HPV was found in 20/52 (38.5%) tumors. A classification based on crystal Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low risk mutations (p=0.0315). A model including this TP53 classification, HPV status, cyclin D1 and Bcl-xL staining significantly predicts survival (p=0.0017). CONCLUSION EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. PMID:27345657

Evaluation of a Web-Based App Demonstrating an Exclusionary Algorithmic Approach to TNM Cancer Staging

PubMed Central

2015-01-01

Background TNM staging plays a critical role in the evaluation and management of a range of different types of cancers. The conventional combinatorial approach to the determination of an anatomic stage relies on the identification of distinct tumor (T), node (N), and metastasis (M) classifications to generate a TNM grouping. This process is inherently inefficient due to the need for scrupulous review of the criteria specified for each classification to ensure accurate assignment. An exclusionary approach to TNM staging based on sequential constraint of options may serve to minimize the number of classifications that need to be reviewed to accurately determine an anatomic stage. Objective Our aim was to evaluate the usability and utility of a Web-based app configured to demonstrate an exclusionary approach to TNM staging. Methods Internal medicine residents, surgery residents, and oncology fellows engaged in clinical training were asked to evaluate a Web-based app developed as an instructional aid incorporating (1) an exclusionary algorithm that polls tabulated classifications and sorts them into ranked order based on frequency counts, (2) reconfiguration of classification criteria to generate disambiguated yes/no questions that function as selection and exclusion prompts, and (3) a selectable grid of TNM groupings that provides dynamic graphic demonstration of the effects of sequentially selecting or excluding specific classifications. Subjects were asked to evaluate the performance of this app after completing exercises simulating the staging of different types of cancers encountered during training. Results Survey responses indicated high levels of agreement with statements supporting the usability and utility of this app. Subjects reported that its user interface provided a clear display with intuitive controls and that the exclusionary approach to TNM staging it demonstrated represented an efficient process of assignment that helped to clarify distinctions between tumor, node, and metastasis classifications. High overall usefulness ratings were bolstered by supplementary comments suggesting that this app might be readily adopted for use in clinical practice. Conclusions A Web-based app that utilizes an exclusionary algorithm to prompt the assignment of tumor, node, and metastasis classifications may serve as an effective instructional aid demonstrating an efficient and informative approach to TNM staging. PMID:28410163

Applicability of the Proposed Japanese Model for the Classification of Gastric Cancer Location: The "PROTRADIST" Retrospective Study.

PubMed

Marano, Luigi; Petrillo, Marianna; Pezzella, Modestino; Patriti, Alberto; Braccio, Bartolomeo; Esposito, Giuseppe; Grassia, Michele; Romano, Angela; Torelli, Francesco; De Luca, Raffaele; Fabozzi, Alessio; Falco, Giuseppe; Di Martino, Natale

2017-06-01

The extension of lymphadenectomy for surgical treatment of gastric cancer remains discordant among European and Japanese surgeons. Kinami et al. (Kinami S, Fujimura T, Ojima E, et al. PTD classification: proposal for a new classification of gastric cancer location based on physiological lymphatic flow. Int. J. Clin. Oncol. 2008;13:320-329) proposed a new experimental classification, the "Proximal zone, Transitional zone, Distal zone" (PTD) classification, based on the physiological lymphatic flow of gastric cancer site. The aim of the present retrospective study is to assess the applicability of PTD Japanese model in gastric cancer patients of our Western surgical department. Two groups of patients with histologically documented adenocarcinoma of the stomach were retrospectively obtained: In the first group were categorized 89 patients with T1a-T1b tumor invasion; and in the second group were 157 patients with T2-T3 category. The data collected were then categorized according to the PTD classification. In the T1a-T1b group there were no lymph node metastases within the r-GA or r-GEA compartments for tumors located in the P portion, and similarly there were no lymphatic metastases within the l-GEA or p-GA compartments for tumors located in the D portion. On the contrary, in the T2-T3 group the lymph node metastases presented a diffused spreading with no statistical significance between the two classification models. Our results show that the PTD classification based on physiological lymphatic flow of the gastric cancer site is a more physiological and clinical version than the Upper, Medium And Lower classification. It represents a valuable and applicable model of cancer location that could be a guide to a tailored surgical approach in Italian patients with neoplasm confined to submucosa. Nevertheless, in order to confirm our findings, larger and prospective studies are needed.

[Relation between location of elements in periodic table and affinity for the malignant tumor (author's transl)].

PubMed

Ando, A; Hisada, K; Ando, I

1977-10-01

Affinity of many inorganic compounds for the malignant tumor was examined, using the rats which were subcutaneously transplanted with Yoshida sarcoma. And the relations between the uptake rate into the malignant tumor and in vitro binding power to the protein were investigated in these compounds. In these experiments, the bipositive ions and anions had not affinity for the tumor tissue with a few exceptions. On the other hand, Hg, Au and Bi, which have strong binding power to the protein, showed high uptake rate into the malignant tumor. As Hg++, Au+ and Bi+++ are soft acids according to classification of Lewis acids, it was thought that these elements would bind strongly to soft base (R-SH, R-S-) present in the tumor tissue. In many hard acids (according to classification of Lewis acids), the uptake rate into the tumor was shown as a function of ionic potentials (valency/ionic radii) of the metal ions. It is presumed that the chemical bond of these hard acids in the tumor tissue is ionic bond to hard base (R-COO-, R-PO3(2-), R-SO3-, R-NH2).

Molecular classification of gastric cancer.

PubMed

Röcken, Christoph

2017-03-01

Gastric cancer is among the most common cancers worldwide. Despite declining incidences, the prognosis remains dismal in Western countries and is better in Asian countries with national cancer screening programs. Complete endoscopic or surgical resection of the primary tumor with or without lymphadenectomy offers the only chance of cure in the early stage of the disease. Survival of more locally advanced gastric cancers was improved by the introduction of perioperative, adjuvant and palliative chemotherapy. However, the identification and usage of novel predictive and diagnostic targets is urgently needed. Areas covered: Recent comprehensive molecular profiling of gastric cancer proposed four molecular subtypes, i.e. Epstein-Barr virus-associated, microsatellite instable, chromosomal instable and genomically stable carcinomas. The new molecular classification will spur clinical trials exploring novel targeted therapeutics. This review summarizes recent advancements of the molecular classification, and based on that, putative pitfalls for the development of tissue-based companion diagnostics, i.e. prevalence of actionable targets and therapeutic efficacy, tumor heterogeneity and tumor evolution, impact of ethnicity on gastric cancer biology, and standards of care in the East and West. Expert commentary: The overall low prevalence of actionable targets and tumor heterogeneity are the two main obstacles of precision medicine for gastric cancer.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

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.

Perspectives on current tumor-node-metastasis (TNM) staging of cancers of the colon and rectum.

PubMed

Hu, Huankai; Krasinskas, Alyssa; Willis, Joseph

2011-08-01

Improvements in classifications of cancers based on discovery and validation of important histopathological parameters and new molecular markers continue unabated. Though still not perfect, recent updates of classification schemes in gastrointestinal oncology by the American Joint Commission on Cancer (tumor-node-metastasis [TNM] staging) and the World Health Organization further stratify patients and guide optimization of treatment strategies and better predict patient outcomes. These updates recognize the heterogeneity of patient populations with significant subgrouping of each tumor stage and use of tumor deposits to significantly "up-stage" some cancers; change staging parameters for subsets of IIIB and IIIC cancers; and introduce of several new subtypes of colon carcinomas. By the nature of the process, recent discoveries that are important to improving even routine standards of patient care, especially new advances in molecular medicine, are not incorporated into these systems. Nonetheless, these classifications significantly advance clinical standards and are welcome enhancements to our current methods of cancer reporting. Copyright © 2011 Elsevier Inc. All rights reserved.

From pituitary adenoma to pituitary neuroendocrine tumor (PitNET): an International Pituitary Pathology Club proposal.

PubMed

Asa, S L; Casar-Borota, O; Chanson, P; Delgrange, E; Earls, P; Ezzat, S; Grossman, A; Ikeda, H; Inoshita, N; Karavitaki, N; Korbonits, M; Laws, E R; Lopes, M B; Maartens, N; McCutcheon, I E; Mete, O; Nishioka, H; Raverot, G; Roncaroli, F; Saeger, W; Syro, L V; Vasiljevic, A; Villa, C; Wierinckx, A; Trouillas, J

2017-04-01

The classification of neoplasms of adenohypophysial cells is misleading because of the simplistic distinction between adenoma and carcinoma, based solely on metastatic spread and the poor reproducibility and predictive value of the definition of atypical adenomas based on the detection of mitoses or expression of Ki-67 or p53. In addition, the current classification of neoplasms of the anterior pituitary does not accurately reflect the clinical spectrum of behavior. Invasion and regrowth of proliferative lesions and persistence of hormone hypersecretion cause significant morbidity and mortality. We propose a new terminology, pituitary neuroendocrine tumor (PitNET), which is consistent with that used for other neuroendocrine neoplasms and which recognizes the highly variable impact of these tumors on patients. © 2017 Society for Endocrinology.

Performance assessment of automated tissue characterization for prostate H and E stained histopathology

NASA Astrophysics Data System (ADS)

DiFranco, Matthew D.; Reynolds, Hayley M.; Mitchell, Catherine; Williams, Scott; Allan, Prue; Haworth, Annette

2015-03-01

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H and E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.

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%.

Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches.

PubMed

Das, Arpita; Bhattacharya, Mahua

2011-01-01

In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

Serrated colorectal cancer: Molecular classification, prognosis, and response to chemotherapy

PubMed Central

Murcia, Oscar; Juárez, Miriam; Hernández-Illán, Eva; Egoavil, Cecilia; Giner-Calabuig, Mar; Rodríguez-Soler, María; Jover, Rodrigo

2016-01-01

Molecular advances support the existence of an alternative pathway of colorectal carcinogenesis that is based on the hypermethylation of specific DNA regions that silences tumor suppressor genes. This alternative pathway has been called the serrated pathway due to the serrated appearance of tumors in histological analysis. New classifications for colorectal cancer (CRC) were proposed recently based on genetic profiles that show four types of molecular alterations: BRAF gene mutations, KRAS gene mutations, microsatellite instability, and hypermethylation of CpG islands. This review summarizes what is known about the serrated pathway of CRC, including CRC molecular and clinical features, prognosis, and response to chemotherapy. PMID:27053844

The 2015 WHO Classification of Tumors of the Thymus: Continuity and Changes

PubMed Central

Marx, Alexander; Chan, John K.C.; Coindre, Jean-Michel; Detterbeck, Frank; Girard, Nicolas; Harris, Nancy L.; Jaffe, Elaine S.; Kurrer, Michael O.; Marom, Edith M.; Moreira, Andre L.; Mukai, Kiyoshi; Orazi, Attilio; Ströbel, Philipp

2015-01-01

This overview of the 4th edition of the WHO Classification of thymic tumors has two aims. First, to comprehensively list the established and new tumour entities and variants that are described in the new WHO Classification of thymic epithelial tumors, germ cell tumors, lymphomas, dendritic cell and myeloid neoplasms, and soft tissue tumors of the thymus and mediastinum; second, to highlight major differences in the new WHO Classification that result from the progress that has been made since the 3rd edition in 2004 at immunohistochemical, genetic and conceptual levels. Refined diagnostic criteria for type A, AB, B1–B3 thymomas and thymic squamous cell carcinoma are given and will hopefully improve the reproducibility of the classification and its clinical relevance. The clinical perspective of the classification has been strengthened by involving experts from radiology, thoracic surgery and oncology; by incorporating state-of-the-art PET/CT images; and by depicting prototypic cytological specimens. This makes the thymus section of the new WHO Classification of Tumours of the Lung, Pleura, Thymus and Heart a valuable tool for pathologists, cytologists and clinicians alike. The impact of the new WHO Classification on therapeutic decisions is exemplified in this overview for thymic epithelial tumors and mediastinal lymphomas, and future perspectives and challenges are discussed. PMID:26295375

Heterogeneous data fusion for brain tumor classification.

PubMed

Metsis, Vangelis; Huang, Heng; Andronesi, Ovidiu C; Makedon, Fillia; Tzika, Aria

2012-10-01

Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better disease diagnosis, prognosis, treatment and drug discovery. In this report, we present a novel machine learning framework for brain tumor classification based on heterogeneous data fusion of metabolic and molecular datasets, including state-of-the-art high-resolution magic angle spinning (HRMAS) proton (1H) magnetic resonance spectroscopy and gene transcriptome profiling, obtained from intact brain tumor biopsies. Our experimental results show that our novel framework outperforms any analysis using individual dataset.

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.

Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling.

PubMed

Karthik, Govindasamy-Muralidharan; Rantalainen, Mattias; Stålhammar, Gustav; Lövrot, John; Ullah, Ikram; Alkodsi, Amjad; Ma, Ran; Wedlund, Lena; Lindberg, Johan; Frisell, Jan; Bergh, Jonas; Hartman, Johan

2017-11-29

Transcriptomic profiling of breast tumors provides opportunity for subtyping and molecular-based patient stratification. In diagnostic applications the specimen profiled should be representative of the expression profile of the whole tumor and ideally capture properties of the most aggressive part of the tumor. However, breast cancers commonly exhibit intra-tumor heterogeneity at molecular, genomic and in phenotypic level, which can arise during tumor evolution. Currently it is not established to what extent a random sampling approach may influence molecular breast cancer diagnostics. In this study we applied RNA-sequencing to quantify gene expression in 43 pieces (2-5 pieces per tumor) from 12 breast tumors (Cohort 1). We determined molecular subtype and transcriptomic grade for all tumor pieces and analysed to what extent pieces originating from the same tumors are concordant or discordant with each other. Additionally, we validated our finding in an independent cohort consisting of 19 pieces (2-6 pieces per tumor) from 6 breast tumors (Cohort 2) profiled using microarray technique. Exome sequencing was also performed on this cohort, to investigate the extent of intra-tumor genomic heterogeneity versus the intra-tumor molecular subtype classifications. Molecular subtyping was consistent in 11 out of 12 tumors and transcriptomic grade assignments were consistent in 11 out of 12 tumors as well. Molecular subtype predictions revealed consistent subtypes in four out of six patients in this cohort 2. Interestingly, we observed extensive intra-tumor genomic heterogeneity in these tumor pieces but not in their molecular subtype classifications. Our results suggest that macroscopic intra-tumoral transcriptomic heterogeneity is limited and unlikely to have an impact on molecular diagnostics for most patients.

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.

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

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.

Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

PubMed

Siegal, Tali

2016-01-01

Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated.

Cytokines Synergize to Combat Metastatic Neuroblastoma | Center for Cancer Research

Cancer.gov

Neuroblastoma is the most common extracranial solid tumor in children, and clinical outcomes of patients with this disease are quite variable. Prognosis is particularly poor for patients with high-risk tumors (classification based on patients’ age, extent of disease spread, and other biological features).

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

PubMed

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

Tumors with unmethylated MLH1 and the CpG island methylator phenotype are associated with a poor prognosis in stage II colorectal cancer patients

PubMed Central

Fu, Tao; Liu, Yanliang; Li, Kai; Wan, Weiwei; Pappou, Emmanouil P.; Iacobuzio-Donahue, Christine A.; Kerner, Zachary; Baylin, Stephen B.; Wolfgang, Christopher L.; Ahuja, Nita

2016-01-01

We previously developed a novel tumor subtype classification model for duodenal adenocarcinomas based on a combination of the CpG island methylator phenotype (CIMP) and MLH1 methylation status. Here, we tested the prognostic value of this model in stage II colorectal cancer (CRC) patients. Tumors were assigned to CIMP+/MLH1-unmethylated (MLH1-U), CIMP+/MLH1-methylated (MLH1-M), CIMP−/MLH1-U, or CIMP−/MLH1-M groups. Age, tumor location, lymphovascular invasion, and mucin production differed among the four patient subgroups, and CIMP+/MLH1-U tumors were more likely to have lymphovascular invasion and mucin production. Kaplan-Meier analyses revealed differences in both disease-free survival (DFS) and overall survival (OS) among the four groups. In a multivariate analysis, CIMP/MLH1 methylation status was predictive of both DFS and OS, and DFS and OS were shortest in CIMP+/MLH1-U stage II CRC patients. These results suggest that tumor subtype classification based on the combination of CIMP and MLH1 methylation status is informative in stage II CRC patients, and that CIMP+/MLH1-U tumors exhibit aggressive features and are associated with poor clinical outcomes. PMID:27880934

Tumors with unmethylated MLH1 and the CpG island methylator phenotype are associated with a poor prognosis in stage II colorectal cancer patients.

PubMed

Fu, Tao; Liu, Yanliang; Li, Kai; Wan, Weiwei; Pappou, Emmanouil P; Iacobuzio-Donahue, Christine A; Kerner, Zachary; Baylin, Stephen B; Wolfgang, Christopher L; Ahuja, Nita

2016-12-27

We previously developed a novel tumor subtype classification model for duodenal adenocarcinomas based on a combination of the CpG island methylator phenotype (CIMP) and MLH1 methylation status. Here, we tested the prognostic value of this model in stage II colorectal cancer (CRC) patients. Tumors were assigned to CIMP+/MLH1-unmethylated (MLH1-U), CIMP+/MLH1-methylated (MLH1-M), CIMP-/MLH1-U, or CIMP-/MLH1-M groups. Age, tumor location, lymphovascular invasion, and mucin production differed among the four patient subgroups, and CIMP+/MLH1-U tumors were more likely to have lymphovascular invasion and mucin production. Kaplan-Meier analyses revealed differences in both disease-free survival (DFS) and overall survival (OS) among the four groups. In a multivariate analysis, CIMP/MLH1 methylation status was predictive of both DFS and OS, and DFS and OS were shortest in CIMP+/MLH1-U stage II CRC patients. These results suggest that tumor subtype classification based on the combination of CIMP and MLH1 methylation status is informative in stage II CRC patients, and that CIMP+/MLH1-U tumors exhibit aggressive features and are associated with poor clinical outcomes.

3D model-based documentation with the Tumor Therapy Manager (TTM) improves TNM staging of head and neck tumor patients.

PubMed

Pankau, Thomas; Wichmann, Gunnar; Neumuth, Thomas; Preim, Bernhard; Dietz, Andreas; Stumpp, Patrick; Boehm, Andreas

2015-10-01

Many treatment approaches are available for head and neck cancer (HNC), leading to challenges for a multidisciplinary medical team in matching each patient with an appropriate regimen. In this effort, primary diagnostics and its reliable documentation are indispensable. A three-dimensional (3D) documentation system was developed and tested to determine its influence on interpretation of these data, especially for TNM classification. A total of 42 HNC patient data sets were available, including primary diagnostics such as panendoscopy, performed and evaluated by an experienced head and neck surgeon. In addition to the conventional panendoscopy form and report, a 3D representation was generated with the "Tumor Therapy Manager" (TTM) software. These cases were randomly re-evaluated by 11 experienced otolaryngologists from five hospitals, half with and half without the TTM data. The accuracy of tumor staging was assessed by pre-post comparison of the TNM classification. TNM staging showed no significant differences in tumor classification (T) with and without 3D from TTM. However, there was a significant decrease in standard deviation from 0.86 to 0.63 via TTM ([Formula: see text]). In nodal staging without TTM, the lymph nodes (N) were significantly underestimated with [Formula: see text] classes compared with [Formula: see text] with TTM ([Formula: see text]). Likewise, the standard deviation was reduced from 0.79 to 0.69 ([Formula: see text]). There was no influence of TTM results on the evaluation of distant metastases (M). TNM staging was more reproducible and nodal staging more accurate when 3D documentation of HNC primary data was available to experienced otolaryngologists. The more precise assessment of the tumor classification with TTM should provide improved decision-making concerning therapy, especially within the interdisciplinary tumor board.

Significance and implications of FDA approval of pembrolizumab for biomarker-defined disease.

PubMed

Boyiadzis, Michael M; Kirkwood, John M; Marshall, John L; Pritchard, Colin C; Azad, Nilofer S; Gulley, James L

2018-05-14

The U.S. Food and Drug Administration (FDA) recently approved pembrolizumab, an anti- programmed cell death protein 1 cancer immunotherapeutic, for use in advanced solid tumors in patients with the microsatellite-high/DNA mismatch repair-deficient biomarker. This is the first example of a tissue-agnostic FDA approval of a treatment based on a patient's tumor biomarker status, rather than on tumor histology. Here we discuss key issues and implications arising from the biomarker-based disease classification implied by this historic approval.

The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

PubMed

Travis, William D; Brambilla, Elisabeth; Nicholson, Andrew G; Yatabe, Yasushi; Austin, John H M; Beasley, Mary Beth; Chirieac, Lucian R; Dacic, Sanja; Duhig, Edwina; Flieder, Douglas B; Geisinger, Kim; Hirsch, Fred R; Ishikawa, Yuichi; Kerr, Keith M; Noguchi, Masayuki; Pelosi, Giuseppe; Powell, Charles A; Tsao, Ming Sound; Wistuba, Ignacio

2015-09-01

The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and (c) PEComa, malignant, (12) introducing the entity pulmonary myxoid sarcoma with an EWSR1-CREB1 translocation, (13) adding the entities myoepithelioma and myoepithelial carcinomas, which can show EWSR1 gene rearrangements, (14) recognition of usefulness of WWTR1-CAMTA1 fusions in diagnosis of epithelioid hemangioendotheliomas, (15) adding Erdheim-Chester disease to the lymphoproliferative tumor, and (16) a group of tumors of ectopic origin to include germ cell tumors, intrapulmonary thymoma, melanoma and meningioma.

Clinical staging: its importance in therapeutic decisions and clinical trials.

PubMed

Denis, L J

1992-02-01

International collaboration has resulted in a revised and unified 1987 formulation for the TNM classification in solid tumors. The simplification and eliminations of most variables caused difficulties for the clinical use of the system in some tumors such as bladder cancer. The approval of the proposed adaptation covering the tumor mass, subdividing the T4 category and adapting the stage grouping, resolves these difficulties. Published reports demonstrate support for the TNM system as a clinical base for treatment decisions and prognosis. The TNMG stage and grade are important basic prognostic factors, but other prognostic factors, especially biologic tumor activity, are under clinical investigation. The TNM classification is the initial evaluation after histologic confirmation of cancer to guide treatment and prognosis. The quality of the evaluation is enhanced by precise communication on the employed methodology.

Integrated DNA methylation and copy-number profiling identify three clinically and biologically relevant groups of anaplastic glioma.

PubMed

Wiestler, Benedikt; Capper, David; Sill, Martin; Jones, David T W; Hovestadt, Volker; Sturm, Dominik; Koelsche, Christian; Bertoni, Anna; Schweizer, Leonille; Korshunov, Andrey; Weiß, Elisa K; Schliesser, Maximilian G; Radbruch, Alexander; Herold-Mende, Christel; Roth, Patrick; Unterberg, Andreas; Hartmann, Christian; Pietsch, Torsten; Reifenberger, Guido; Lichter, Peter; Radlwimmer, Bernhard; Platten, Michael; Pfister, Stefan M; von Deimling, Andreas; Weller, Michael; Wick, Wolfgang

2014-10-01

The outcome of patients with anaplastic gliomas varies considerably. Whether a molecular classification of anaplastic gliomas based on large-scale genomic or epigenomic analyses is superior to histopathology for reflecting distinct biological groups, predicting outcomes and guiding therapy decisions has yet to be determined. Epigenome-wide DNA methylation analysis, using a platform which also allows the detection of copy-number aberrations, was performed in a cohort of 228 patients with anaplastic gliomas (astrocytomas, oligoastrocytomas, and oligodendrogliomas), including 115 patients of the NOA-04 trial. We further compared these tumors with a group of 55 glioblastomas. Unsupervised clustering of DNA methylation patterns revealed two main groups correlated with IDH status: CpG island methylator phenotype (CIMP) positive (77.5 %) or negative (22.5 %). CIMP(pos) (IDH mutant) tumors showed a further separation based on copy-number status of chromosome arms 1p and 19q. CIMP(neg) (IDH wild type) tumors showed hallmark copy-number alterations of glioblastomas, and clustered together with CIMP(neg) glioblastomas without forming separate groups based on WHO grade. Notably, there was no molecular evidence for a distinct biological entity representing anaplastic oligoastrocytoma. Tumor classification based on CIMP and 1p/19q status was significantly associated with survival, allowing a better prediction of outcome than the current histopathological classification: patients with CIMP(pos) tumors with 1p/19q codeletion (CIMP-codel) had the best prognosis, followed by patients with CIMP(pos) tumors but intact 1p/19q status (CIMP-non-codel). Patients with CIMP(neg) anaplastic gliomas (GBM-like) had the worst prognosis. Collectively, our data suggest that anaplastic gliomas can be grouped by IDH and 1p/19q status into three molecular groups that show clear links to underlying biology and a significant association with clinical outcome in a prospective trial cohort.

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.

Vascular Anomalies (Part I): Classification and Diagnostics of Vascular Anomalies.

PubMed

Sadick, Maliha; Müller-Wille, René; Wildgruber, Moritz; Wohlgemuth, Walter A

2018-06-06

 Vascular anomalies are a diagnostic and therapeutic challenge. They require dedicated interdisciplinary management. Optimal patient care relies on integral medical evaluation and a classification system established by experts in the field, to provide a better understanding of these complex vascular entities.  A dedicated classification system according to the International Society for the Study of Vascular Anomalies (ISSVA) and the German Interdisciplinary Society of Vascular Anomalies (DiGGefA) is presented. The vast spectrum of diagnostic modalities, ranging from ultrasound with color Doppler, conventional X-ray, CT with 4 D imaging and MRI as well as catheter angiography for appropriate assessment is discussed.  Congenital vascular anomalies are comprised of vascular tumors, based on endothelial cell proliferation and vascular malformations with underlying mesenchymal and angiogenetic disorder. Vascular tumors tend to regress with patient's age, vascular malformations increase in size and are subdivided into capillary, venous, lymphatic, arterio-venous and combined malformations, depending on their dominant vasculature. According to their appearance, venous malformations are the most common representative of vascular anomalies (70 %), followed by lymphatic malformations (12 %), arterio-venous malformations (8 %), combined malformation syndromes (6 %) and capillary malformations (4 %).  The aim is to provide an overview of the current classification system and diagnostic characterization of vascular anomalies in order to facilitate interdisciplinary management of vascular anomalies.   · Vascular anomalies are comprised of vascular tumors and vascular malformations, both considered to be rare diseases.. · Appropriate treatment depends on correct classification and diagnosis of vascular anomalies, which is based on established national and international classification systems, recommendations and guidelines.. · In the classification, diagnosis and treatment of congenital vascular anomalies, radiology plays an integral part in patient management.. · Sadick M, Müller-Wille R, Wildgruber M et al. Vascular Anomalies (Part I): Classification and Diagnostics of Vascular Anomalies. Fortschr Röntgenstr 2018; DOI: 10.1055/a-0620-8925. © Georg Thieme Verlag KG Stuttgart · New York.

3D tissue engineered micro-tumors for optical-based therapeutic screening platform

NASA Astrophysics Data System (ADS)

Spano, Joseph L.; Schmitt, Trevor J.; Bailey, Ryan C.; Hannon, Timothy S.; Elmajdob, Mohamed; Mason, Eric M.; Ye, Guochang; Das, Soumen; Seal, Sudipta; Fenn, Michael B.

2016-03-01

Melanoma is an underserved area of cancer research, with little focus on studying the effects of tumor extracellular matrix (ECM) properties on melanoma tumor progression, metastasis, and treatment efficacy. We've developed a Raman spectral mapping-based in-vitro screening platform that allows for nondestructive in-situ, multi-time point assessment of a novel potential nanotherapeutic adjuvant, nanoceria (cerium oxide nanoparticles), for treating melanoma. We've focused primarily on understanding melanoma tumor ECM composition and how it influences cell morphology and ICC markers. Furthermore, we aim to correlate this with studies on nanotherapeutic efficacy to coincide with the goal of predicting and preventing metastasis based on ECM composition. We've compiled a Raman spectral database for substrates containing varying compositions of fibronectin, elastin, laminin, and collagens type I and IV. Furthermore, we've developed a machine learning-based semi-quantitative analysis platform utilizing dimensionality reduction with subsequent pixel classification and semi-quantitation of ECM composition using Direct Classical Least Squares for classification and estimation of the reorganization of these components by taking 2D maps using Raman spectroscopy. Gaining an understanding of how tissue properties influence ECM organization has laid the foundation for future work utilizing Raman spectroscopy to assess therapeutic efficacy and matrix reorganization imparted by nanoceria. Specifically, this will allow us to better understand the role of HIF1a in matrix reorganization of the tumor microenvironment. By studying the relationship between substrate modulus and nanoceria's ability to inhibit an ECM that is conducive to tumor formation, we endeavor to show that nanoceria may prevent or even revert tumor conducive microenvironments.

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.

EGFR Amplification and IDH Mutations in Glioblastoma Patients of the Northeast of Morocco

PubMed Central

Louati, Sara; Chbani, Laila; El Fatemi, Hind; Hammas, Nawal; Mikou, Karima; Maaroufi, Mustapha; Benzagmout, Mohammed; Boujraf, Said; El Bardai, Sanae; Giry, Marine; Marie, Yannick; Chaoui El Faiz, Mohammed; Mokhtari, Karima; Amarti, Afaf; Bennis, Sanae

2017-01-01

Glioblastomas are the most frequent and aggressive primary brain tumors which are expressing various evolutions, aggressiveness, and prognosis. Thus, the 2007 World Health Organization classification based solely on the histological criteria is no longer sufficient. It should be complemented by molecular analysis for a true histomolecular classification. The new 2016 WHO classification of tumors of the central nervous system uses molecular parameters in addition to histology to reclassify these tumors and reduce the interobserver variability. The aim of this study is to determine the prevalence of IDH mutations and EGFR amplifications in the population of the northeast region of Morocco and then to compare the results with other studies. Methods. IDH1 codon 132 and IDH2 codon 172 were directly sequenced and the amplification of exon 20 of EGFR gene was investigated by qPCR in 65 glioblastoma tumors diagnosed at the University Hospital of Fez between 2010 and 2014. Results. The R132H IDH1 mutation was observed in 8 of 65 tumor samples (12.31%). No mutation of IDH2 was detected. EGFR amplification was identified in 17 cases (26.15%). Conclusion. A systematic search of both histological and molecular markers should be requisite for a good diagnosis and a better management of glioblastomas. PMID:28785587

EGFR Amplification and IDH Mutations in Glioblastoma Patients of the Northeast of Morocco.

PubMed

Senhaji, Nadia; Louati, Sara; Chbani, Laila; El Fatemi, Hind; Hammas, Nawal; Mikou, Karima; Maaroufi, Mustapha; Benzagmout, Mohammed; Boujraf, Said; El Bardai, Sanae; Giry, Marine; Marie, Yannick; Chaoui El Faiz, Mohammed; Mokhtari, Karima; Idbaih, Ahmed; Amarti, Afaf; Bennis, Sanae

2017-01-01

Glioblastomas are the most frequent and aggressive primary brain tumors which are expressing various evolutions, aggressiveness, and prognosis. Thus, the 2007 World Health Organization classification based solely on the histological criteria is no longer sufficient. It should be complemented by molecular analysis for a true histomolecular classification. The new 2016 WHO classification of tumors of the central nervous system uses molecular parameters in addition to histology to reclassify these tumors and reduce the interobserver variability. The aim of this study is to determine the prevalence of IDH mutations and EGFR amplifications in the population of the northeast region of Morocco and then to compare the results with other studies. Methods . IDH1 codon 132 and IDH2 codon 172 were directly sequenced and the amplification of exon 20 of EGFR gene was investigated by qPCR in 65 glioblastoma tumors diagnosed at the University Hospital of Fez between 2010 and 2014. Results . The R132H IDH1 mutation was observed in 8 of 65 tumor samples (12.31%). No mutation of IDH2 was detected. EGFR amplification was identified in 17 cases (26.15%). Conclusion . A systematic search of both histological and molecular markers should be requisite for a good diagnosis and a better management of glioblastomas.

Tumor taxonomy for the developmental lineage classification of neoplasms

PubMed Central

Berman, Jules J

2004-01-01

Background The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. Methods The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. Results The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. Conclusions This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article. PMID:15571625

Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients

PubMed Central

Kirilovsky, Amos; Marliot, Florence; El Sissy, Carine; Haicheur, Nacilla; Galon, Jérôme

2016-01-01

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) tumor, nodes, metastasis (TNM) classification system based on tumor features is used for prognosis estimation and treatment recommendations in most cancers. However, the clinical outcome can vary significantly among patients within the same tumor stage and TNM classification does not predict response to therapy. Therefore, many efforts have been focused on the identification of new markers. Multiple tumor cell-based approaches have been proposed but very few have been translated into the clinic. The recent demonstration of the essential role of the immune system in tumor progression has allowed great advances in the understanding of this complex disease and in the design of novel therapies. The analysis of the immune infiltrate by imaging techniques in large patient cohorts highlighted the prognostic impact of the in situ immune cell infiltrate in tumors. Moreover, the characterization of the immune infiltrates (e.g. type, density, distribution within the tumor, phenotype, activation status) in patients treated with checkpoint-blockade strategies could provide information to predict the disease outcome. In colorectal cancer, we have developed a prognostic score (‘Immunoscore’) that takes into account the distribution of the density of both CD3+ lymphocytes and CD8+ cytotoxic T cells in the tumor core and the invasive margin that could outperform TNM staging. Currently, an international retrospective study is under way to validate the Immunoscore prognostic performance in patients with colon cancer. The use of Immunoscore in clinical practice could improve the patients’ prognostic assessment and therapeutic management. PMID:27121213

Concurrent Tumor Segmentation and Registration with Uncertainty-based Sparse non-Uniform Graphs

PubMed Central

Parisot, Sarah; Wells, William; Chemouny, Stéphane; Duffau, Hugues; Paragios, Nikos

2014-01-01

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. PMID:24717540

Round Cell Tumors: Classification and Immunohistochemistry.

PubMed

Sharma, Shweta; Kamala, R; Nair, Divya; Ragavendra, T Raju; Mhatre, Swapnil; Sabharwal, Robin; Choudhury, Basanta Kumar; Rana, Vivek

2017-01-01

Round cell tumors as the name suggest are comprised round cells with increased nuclear-cytoplasmic ratio. This group of tumor includes entities such as peripheral neuroectodermal tumor, rhabdomyosarcoma, synovial sarcoma, non-Hodgkin's lymphoma, neuroblastoma, hepatoblastoma, Wilms' tumor, and desmoplastic small round cell tumor. These round cells tumors are characterized by typical histological pattern, immunohistochemical, and electron microscopic features that can help in differential diagnosis. The present article describes the classification and explains the histopathology and immunohistochemistry of some important round cell tumors.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

PubMed Central

Girard, Pascal; Ioannou, Konstantinos; Klinkhardt, Ute; Munafo, Alain

2018-01-01

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework. PMID:29388396

[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.

Neuroendocrine tumors of colon and rectum: validation of clinical and prognostic values of the World Health Organization 2010 grading classifications and European Neuroendocrine Tumor Society staging systems.

PubMed

Shen, Chaoyong; Yin, Yuan; Chen, Huijiao; Tang, Sumin; Yin, Xiaonan; Zhou, Zongguang; Zhang, Bo; Chen, Zhixin

2017-03-28

This study evaluated and compared the clinical and prognostic values of the grading criteria used by the World Health Organization (WHO) and the European Neuroendocrine Tumors Society (ENETS). Moreover, this work assessed the current best prognostic model for colorectal neuroendocrine tumors (CRNETs). The 2010 WHO classifications and the ENETS systems can both stratify the patients into prognostic groups, although the 2010 WHO criteria is more applicable to CRNET patients. Along with tumor location, the 2010 WHO criteria are important independent prognostic parameters for CRNETs in both univariate and multivariate analyses through Cox regression (P<0.05). Data from 192 consecutive patients histopathologically diagnosed with CRNETs and had undergone surgical resection from January 2009 to May 2016 in a single center were retrospectively analyzed. Findings suggest that the WHO classifications are superior over the ENETS classification system in predicting the prognosis of CRNETs. Additionally, the WHO classifications can be widely used in clinical practice.

The p27Kip1 Tumor Suppressor and Multi-Step Tumorigenesis

DTIC Science & Technology

2001-08-01

Breast Cancer , Cell cycle, tumor suppressor 33 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20...in many cancers , including carcinomas of the breast , colon, lung and prostate, and lymphoma. Although these studies of p27 expression in primary...of DMBA-induced pituitary tumors in p27-/- mice precluded determination of breast cancer risk in these mice. Nevertheless, the extensive mammary tissue

Molecular Diagnostics of Gliomas Using Next Generation Sequencing of a Glioma-Tailored Gene Panel.

PubMed

Zacher, Angela; Kaulich, Kerstin; Stepanow, Stefanie; Wolter, Marietta; Köhrer, Karl; Felsberg, Jörg; Malzkorn, Bastian; Reifenberger, Guido

2017-03-01

Current classification of gliomas is based on histological criteria according to the World Health Organization (WHO) classification of tumors of the central nervous system. Over the past years, characteristic genetic profiles have been identified in various glioma types. These can refine tumor diagnostics and provide important prognostic and predictive information. We report on the establishment and validation of gene panel next generation sequencing (NGS) for the molecular diagnostics of gliomas. We designed a glioma-tailored gene panel covering 660 amplicons derived from 20 genes frequently aberrant in different glioma types. Sensitivity and specificity of glioma gene panel NGS for detection of DNA sequence variants and copy number changes were validated by single gene analyses. NGS-based mutation detection was optimized for application on formalin-fixed paraffin-embedded tissue specimens including small stereotactic biopsy samples. NGS data obtained in a retrospective analysis of 121 gliomas allowed for their molecular classification into distinct biological groups, including (i) isocitrate dehydrogenase gene (IDH) 1 or 2 mutant astrocytic gliomas with frequent α-thalassemia/mental retardation syndrome X-linked (ATRX) and tumor protein p53 (TP53) gene mutations, (ii) IDH mutant oligodendroglial tumors with 1p/19q codeletion, telomerase reverse transcriptase (TERT) promoter mutation and frequent Drosophila homolog of capicua (CIC) gene mutation, as well as (iii) IDH wildtype glioblastomas with frequent TERT promoter mutation, phosphatase and tensin homolog (PTEN) mutation and/or epidermal growth factor receptor (EGFR) amplification. Oligoastrocytic gliomas were genetically assigned to either of these groups. Our findings implicate gene panel NGS as a promising diagnostic technique that may facilitate integrated histological and molecular glioma classification. © 2016 International Society of Neuropathology.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

Surgical options in benign parotid tumors: a proposal for classification.

PubMed

Quer, Miquel; Vander Poorten, Vincent; Takes, Robert P; Silver, Carl E; Boedeker, Carsten C; de Bree, Remco; Rinaldo, Alessandra; Sanabria, Alvaro; Shaha, Ashok R; Pujol, Albert; Zbären, Peter; Ferlito, Alfio

2017-11-01

Different surgical options are currently available for treating benign tumors of the parotid gland, and the discussion on optimal treatment continues despite several meta-analyses. These options include more limited resections (extracapsular dissection, partial lateral parotidectomy) versus more extensive and traditional options (lateral parotid lobectomy, total parotidectomy). Different schools favor one option or another based on their experience, skills and tradition. This review provides a critical analysis of the literature regarding these options. The main limitation of all the studies is the bias of selection for different surgical approaches. For this reason, we propose a staging system that could facilitate clinical decision making and the comparison of results. We propose four categories based on the size of the tumor and its location within the parotid gland. Category I includes tumors up to 3 cm, which are mobile, close to the outer surface and close to the parotid borders. Category II includes deeper tumors up to 3 cm. Category III comprises tumors greater than 3 cm involving two levels of the parotid gland, and category IV tumors are greater than 3 cm and involve more than 2 levels. For each category and for the various pathologic types, a guideline of surgical extent is proposed. The objective of this classification is to facilitate prospective multicentric studies on surgical techniques in the treatment of benign parotid tumors and to enable the comparison of results of different clinical studies.

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

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

PubMed

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Molecular Classification of Melanoma

Cancer.gov

Tissue-based analyses of precursors, melanoma tumors and metastases within existing study populations to further understanding of the heterogeneity of melanoma and determine a predictive pattern of progression for dysplastic nevi.

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.

Cancer Liquid Biopsy: Is It Ready for Clinic?

PubMed

Pan, Ying; Ji, John S; Jin, Jason Gang; Kuo, Winston Patrick; Kang, Hongjun

2017-01-01

The management of cancer relies on a combination of imaging and tissue biopsy for diagnosis, monitoring, and molecular classification-based patient stratification to ensure appropriate treatment. Conventional tissue biopsy harvests tumor samples with invasive procedures, which are often difficult for patients with advanced disease. Given the well-recognized intratumor genetic heterogeneity [1], the biopsy of small tumor fragments does not necessarily represent all the genetic aberrations in the tumor, but sampling the entire tumor in each patient is not realistic. Moreover, tumors evolve all the time from local to advanced disease and by adapting to selective pressure from treatment.

Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images

PubMed Central

Mitrea, Delia; Mitrea, Paulina; Nedevschi, Sergiu; Badea, Radu; Lupsor, Monica; Socaciu, Mihai; Golea, Adela; Hagiu, Claudia; Ciobanu, Lidia

2012-01-01

The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue. PMID:22312411

Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients.

PubMed

Kirilovsky, Amos; Marliot, Florence; El Sissy, Carine; Haicheur, Nacilla; Galon, Jérôme; Pagès, Franck

2016-08-01

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) tumor, nodes, metastasis (TNM) classification system based on tumor features is used for prognosis estimation and treatment recommendations in most cancers. However, the clinical outcome can vary significantly among patients within the same tumor stage and TNM classification does not predict response to therapy. Therefore, many efforts have been focused on the identification of new markers. Multiple tumor cell-based approaches have been proposed but very few have been translated into the clinic. The recent demonstration of the essential role of the immune system in tumor progression has allowed great advances in the understanding of this complex disease and in the design of novel therapies. The analysis of the immune infiltrate by imaging techniques in large patient cohorts highlighted the prognostic impact of the in situ immune cell infiltrate in tumors. Moreover, the characterization of the immune infiltrates (e.g. type, density, distribution within the tumor, phenotype, activation status) in patients treated with checkpoint-blockade strategies could provide information to predict the disease outcome. In colorectal cancer, we have developed a prognostic score ('Immunoscore') that takes into account the distribution of the density of both CD3(+) lymphocytes and CD8(+) cytotoxic T cells in the tumor core and the invasive margin that could outperform TNM staging. Currently, an international retrospective study is under way to validate the Immunoscore prognostic performance in patients with colon cancer. The use of Immunoscore in clinical practice could improve the patients' prognostic assessment and therapeutic management. © The Japanese Society for Immunology. 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

PubMed

Bakas, Spyridon; Zeng, Ke; Sotiras, Aristeidis; Rathore, Saima; Akbari, Hamed; Gaonkar, Bilwaj; Rozycki, Martin; Pati, Sarthak; Davatzikos, Christos

2016-01-01

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

[Solitary fibrous tumors and hemangiopericytomas of the meninges: Immunophenotype and histoprognosis in a series of 17 cases].

PubMed

Savary, Caroline; Rousselet, Marie-Christine; Michalak, Sophie; Fournier, Henri-Dominique; Taris, Michaël; Loussouarn, Delphine; Rousseau, Audrey

2016-08-01

The 2007 World Health Organization (WHO) classification of tumors of the central nervous system distinguishes meningeal hemangiopericytomas (HPC) from solitary fibrous tumors (TFS). In the WHO classification of tumors of soft tissue and bone, those neoplasms are no longer separate entities since the discovery in 2013 of a common oncogenic event, i.e. the NAB2-STAT6 gene fusion. A shared histopronostic grading system, called "Marseille grading system", was recently proposed, based on hypercellularity, mitotic count and necrosis. We evaluated the immunophenotype and histoprognosis in a retrospective cohort of intracranial HPC and TFS. Fifteen initial tumors and 2 recurrences were evaluated by immunohistochemistry for STAT6, CD34, EMA, progesterone receptors and Ki67. The pronostic value of the WHO and the Marseille grading systems was tested on 12 patients with clinical follow-up. Initial tumors were 11 HPC and 4 SFT. STAT6 and CD34 were expressed in 16/17 tumors, EMA and progesterone receptors in 2 and 5 cases, respectively. The Ki67 labelling index was 6.25% in HPC and 3% in SFT. Half of the tumors recurred between 2 years and 9 years after initial diagnosis (mean time 5 years). No statistical difference in the risk of recurrence was associated with either grade (WHO or Marseille), in this small cohort. The diagnosis of HPC and TFS is facilitated by the almost constant immuno-expression of STAT6, and this justifies their common classification. The high rate of recurrence implies a very long-term follow-up because the current grading systems do not accurately predict the individual risk. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Deep learning and texture-based semantic label fusion for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

2018-02-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

PubMed

Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

2018-01-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

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

Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.

PubMed

Parisot, Sarah; Wells, William; Chemouny, Stéphane; Duffau, Hugues; Paragios, Nikos

2014-05-01

In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. Copyright © 2014 Elsevier B.V. All rights reserved.

GENE-07. MOLECULAR NEUROPATHOLOGY 2.0 - INCREASING DIAGNOSTIC ACCURACY IN PEDIATRIC NEUROONCOLOGY

PubMed Central

Sturm, Dominik; Jones, David T.W.; Capper, David; Sahm, Felix; von Deimling, Andreas; Rutkoswki, Stefan; Warmuth-Metz, Monika; Bison, Brigitte; Gessi, Marco; Pietsch, Torsten; Pfister, Stefan M.

2017-01-01

Abstract The classification of central nervous system (CNS) tumors into clinically and biologically distinct entities and subgroups is challenging. Children and adolescents can be affected by >100 histological variants with very variable outcomes, some of which are exceedingly rare. The current WHO classification has introduced a number of novel molecular markers to aid routine neuropathological diagnostics, and DNA methylation profiling is emerging as a powerful tool to distinguish CNS tumor classes. The Molecular Neuropathology 2.0 study aims to integrate genome wide (epi-)genetic diagnostics with reference neuropathological assessment for all newly-diagnosed pediatric brain tumors in Germany. To date, >350 patients have been enrolled. A molecular diagnosis is established by epigenetic tumor classification through DNA methylation profiling and targeted panel sequencing of >130 genes to detect diagnostically and/or therapeutically useful DNA mutations, structural alterations, and fusion events. Results are aligned with the reference neuropathological diagnosis, and discrepant findings are discussed in a multi-disciplinary tumor board including reference neuroradiological evaluation. Ten FFPE sections as input material are sufficient to establish a molecular diagnosis in >95% of tumors. Alignment with reference pathology results in four broad categories: a) concordant classification (~77%), b) discrepant classification resolvable by tumor board discussion and/or additional data (~5%), c) discrepant classification without currently available options to resolve (~8%), and d) cases currently unclassifiable by molecular diagnostics (~10%). Discrepancies are enriched in certain histopathological entities, such as histological high grade gliomas with a molecularly low grade profile. Gene panel sequencing reveals predisposing germline events in ~10% of patients. Genome wide (epi-)genetic analyses add a valuable layer of information to routine neuropathological diagnostics. Our study provides insight into CNS tumors with divergent histopathological and molecular classification, opening new avenues for research discoveries and facilitating optimization of clinical management for affected patients in the future.

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

PubMed

Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

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

Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

NASA Astrophysics Data System (ADS)

van der Putten, Joost; Zinger, Svitlana; van der Sommen, Fons; de With, Peter H. N.; Prokop, Mathias; Hermans, John

2018-02-01

In current clinical practice, the resectability of pancreatic ductal adenocarcinoma (PDA) is determined subjec- tively by a physician, which is an error-prone procedure. In this paper, we present a method for automated determination of resectability of PDA from a routine abdominal CT, to reduce such decision errors. The tumor features are extracted from a group of patients with both hypo- and iso-attenuating tumors, of which 29 were resectable and 21 were not. The tumor contours are supplied by a medical expert. We present an approach that uses intensity, shape, and texture features to determine tumor resectability. The best classification results are obtained with fine Gaussian SVM and the L0 Feature Selection algorithms. Compared to expert predictions made on the same dataset, our method achieves better classification results. We obtain significantly better results on correctly predicting non-resectability (+17%) compared to a expert, which is essential for patient treatment (negative prediction value). Moreover, our predictions of resectability exceed expert predictions by approximately 3% (positive prediction value).

Grading the neuroendocrine tumors of the lung: an evidence-based proposal.

PubMed

Rindi, G; Klersy, C; Inzani, F; Fellegara, G; Ampollini, L; Ardizzoni, A; Campanini, N; Carbognani, P; De Pas, T M; Galetta, D; Granone, P L; Righi, L; Rusca, M; Spaggiari, L; Tiseo, M; Viale, G; Volante, M; Papotti, M; Pelosi, G

2014-02-01

Lung neuroendocrine tumors are catalogued in four categories by the World Health Organization (WHO 2004) classification. Its reproducibility and prognostic efficacy was disputed. The WHO 2010 classification of digestive neuroendocrine neoplasms is based on Ki67 proliferation assessment and proved prognostically effective. This study aims at comparing these two classifications and at defining a prognostic grading system for lung neuroendocrine tumors. The study included 399 patients who underwent surgery and with at least 1 year follow-up between 1989 and 2011. Data on 21 variables were collected, and performance of grading systems and their components was compared by Cox regression and multivariable analyses. All statistical tests were two-sided. At Cox analysis, WHO 2004 stratified patients into three major groups with statistically significant survival difference (typical carcinoid vs atypical carcinoid (AC), P=0.021; AC vs large-cell/small-cell lung neuroendocrine carcinomas, P<0.001). Optimal discrimination in three groups was observed by Ki67% (Ki67% cutoffs: G1 <4, G2 4-<25, G3 ≥25; G1 vs G2, P=0.021; and G2 vs G3, P≤0.001), mitotic count (G1 ≤2, G2 >2-47, G3 >47; G1 vs G2, P≤0.001; and G2 vs G3, P≤0.001), and presence of necrosis (G1 absent, G2 <10% of sample, G3 >10% of sample; G1 vs G2, P≤0.001; and G2 vs G3, P≤0.001) at uni and multivariable analyses. The combination of these three variables resulted in a simple and effective grading system. A three-tiers grading system based on Ki67 index, mitotic count, and necrosis with cutoffs specifically generated for lung neuroendocrine tumors is prognostically effective and accurate.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

STRESS IN THE CLASSIFICATION OF PITUITARY TUMORS. FOCUS ON AGGRESSIVE PITUITARY ADENOMAS.

PubMed

Kovács, Kálmán; Rotondo, Fabio; Horváth, Eva; Syro, Luis V

2014-03-30

After a brief summary of the stress concept and the contribution of Dr. Hans Selye, this publication focuses on the classification of pituitary neoplasms and the difficulties to provide conclusive information on the prognosis of various pituitary tumor types. The term "aggressive pituitary tumors" was introduced. These tumors have a rapid cell proliferation rate. At present, the assessment of Ki-67 nuclear labeling index appears to be the simplest and most reliable method to evaluate tumor cell multiplication. Further studies on pituitary tumor biomarkers are needed.

[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.

CrossLink: a novel method for cross-condition classification of cancer subtypes.

PubMed

Ma, Chifeng; Sastry, Konduru S; Flore, Mario; Gehani, Salah; Al-Bozom, Issam; Feng, Yusheng; Serpedin, Erchin; Chouchane, Lotfi; Chen, Yidong; Huang, Yufei

2016-08-22

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another. To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature. We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles. A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

Management of hemorrhage in gastrointestinal stromal tumors: a review

PubMed Central

Liu, Qi; Kong, Fanmin; Zhou, Jianping; Dong, Ming; Dong, Qi

2018-01-01

Gastrointestinal stromal tumors (GISTs) are relatively common mesenchymal tumors. They originate from the wall of hollow viscera and may be found in any part of the digestive tract. The prognosis of patients with stromal tumors depends on various risk factors, including size, location, presence of mitotic figures, and tumor rupture. Emergency surgery is often required for stromal tumors with hemorrhage. The current literature suggests that stromal tumor hemorrhage indicates poor prognosis. Although the optimal treatment options for hemorrhagic GISTs are based on surgical experience, there remains controversy with regard to optimum postoperative management as well as the classification of malignant potential. This article reviews the biological characteristics, diagnostic features, prognostic factors, treatment, and postoperative management of GISTs with hemorrhage. PMID:29695930

Detecting brain tumor in pathological slides using hyperspectral imaging

PubMed Central

Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M.; Sarmiento, Roberto

2018-01-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides. PMID:29552415

Detecting brain tumor in pathological slides using hyperspectral imaging.

PubMed

Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

2018-02-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

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.

Tumor response estimation in radar-based microwave breast cancer detection.

PubMed

Kurrant, Douglas J; Fear, Elise C; Westwick, David T

2008-12-01

Radar-based microwave imaging techniques have been proposed for early stage breast cancer detection. A considerable challenge for the successful implementation of these techniques is the reduction of clutter, or components of the signal originating from objects other than the tumor. In particular, the reduction of clutter from the late-time scattered fields is required in order to detect small (subcentimeter diameter) tumors. In this paper, a method to estimate the tumor response contained in the late-time scattered fields is presented. The method uses a parametric function to model the tumor response. A maximum a posteriori estimation approach is used to evaluate the optimal values for the estimates of the parameters. A pattern classification technique is then used to validate the estimation. The ability of the algorithm to estimate a tumor response is demonstrated by using both experimental and simulated data obtained with a tissue sensing adaptive radar system.

Cancer Biochemistry and Host-Tumor Interactions: A Decimal Classification, (Categories 51.6, 51.7, and 51.8).

ERIC Educational Resources Information Center

Schneider, John H.

This is a hierarchical decimal classification of information related to cancer biochemistry, to host-tumor interactions (including cancer immunology), and to occurrence of cancer in special types of animals and plants. It is a working draft of categories taken from an extensive classification of many fields of biomedical information. Because the…

Endometrial stromal tumors: the new WHO classification.

PubMed

Conklin, Christopher M J; Longacre, Teri A

2014-11-01

Endometrial stromal tumors are rare uterine mesenchymal neoplasms that have intrigued pathologists for years, not only because they commonly pose diagnostic dilemmas, but also because the classification and pathogenesis of these tumors has been widely debated. The current World Health Organization recognizes 4 categories of endometrial stromal tumor: endometrial stromal nodule (ESN), low-grade endometrial stromal sarcoma (LG-ESS), high-grade endometrial stromal sarcoma (HG-ESS), and undifferentiated uterine sarcoma (UUS). uterine sarcoma. These categories are defined by the presence of distinct translocations as well as tumor morphology and prognosis. Specifically, the JAZF1-SUZ12 (formerly JAZF1-JJAZ1) fusion identifies a large proportion of ESN and LG-ESSs, whereas the YWHAE-FAM22 translocation identifies HG-ESSs. The latter tumors appear to have a prognosis intermediate between LG-ESS and UUS, which exhibits no specific translocation pattern. This review (1) presents the clinicopathologic features of endometrial stromal tumors; (2) discusses their immunophenotype; and (3) highlights the recent advances in molecular genetics which explain their pathogenesis and lend support for a new classification system.

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.

Issues of diagnostic review in brain tumor studies: from the Brain Tumor Epidemiology Consortium.

PubMed

Davis, Faith G; Malmer, Beatrice S; Aldape, Ken; Barnholtz-Sloan, Jill S; Bondy, Melissa L; Brännström, Thomas; Bruner, Janet M; Burger, Peter C; Collins, V Peter; Inskip, Peter D; Kruchko, Carol; McCarthy, Bridget J; McLendon, Roger E; Sadetzki, Siegal; Tihan, Tarik; Wrensch, Margaret R; Buffler, Patricia A

2008-03-01

Epidemiologists routinely conduct centralized single pathology reviews to minimize interobserver diagnostic variability, but this practice does not facilitate the combination of studies across geographic regions and institutions where diagnostic practices differ. A meeting of neuropathologists and epidemiologists focused on brain tumor classification issues in the context of protocol needs for consortial studies (http://epi.grants.cancer.gov/btec/). It resulted in recommendations relevant to brain tumors and possibly other rare disease studies. Two categories of brain tumors have enough general agreement over time, across regions, and between individual pathologists that one can consider using existing diagnostic data without further review: glioblastomas and meningiomas (as long as uniform guidelines such as those provided by the WHO are used). Prospective studies of these tumors benefit from collection of pathology reports, at a minimum recording the pathology department and classification system used in the diagnosis. Other brain tumors, such as oligodendroglioma, are less distinct and require careful histopathologic review for consistent classification across study centers. Epidemiologic study protocols must consider the study specific aims, diagnostic changes that have taken place over time, and other issues unique to the type(s) of tumor being studied. As diagnostic changes are being made rapidly, there are no readily available answers on disease classification issues. It is essential that epidemiologists and neuropathologists collaborate to develop appropriate study designs and protocols for specific hypothesis and populations.

Schwannoma

MedlinePlus

... Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions

PubMed Central

Morton, Lindsay M.; Linet, Martha S.; Clarke, Christina A.; Kadin, Marshall E.; Vajdic, Claire M.; Monnereau, Alain; Maynadié, Marc; Chiu, Brian C.-H.; Marcos-Gragera, Rafael; Costantini, Adele Seniori; Cerhan, James R.; Weisenburger, Dennis D.

2010-01-01

After publication of the updated World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues in 2008, the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) now presents an update of the hierarchical classification of lymphoid neoplasms for epidemiologic research based on the 2001 WHO classification, which we published in 2007. The updated hierarchical classification incorporates all of the major and provisional entities in the 2008 WHO classification, including newly defined entities based on age, site, certain infections, and molecular characteristics, as well as borderline categories, early and “in situ” lesions, disorders with limited capacity for clinical progression, lesions without current International Classification of Diseases for Oncology, 3rd Edition codes, and immunodeficiency-associated lymphoproliferative disorders. WHO subtypes are defined in hierarchical groupings, with newly defined groups for small B-cell lymphomas with plasmacytic differentiation and for primary cutaneous T-cell lymphomas. We suggest approaches for applying the hierarchical classification in various epidemiologic settings, including strategies for dealing with multiple coexisting lymphoma subtypes in one patient, and cases with incomplete pathologic information. The pathology materials useful for state-of-the-art epidemiology studies are also discussed. We encourage epidemiologists to adopt the updated InterLymph hierarchical classification, which incorporates the most recent WHO entities while demonstrating their relationship to older classifications. PMID:20699439

InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions.

PubMed

Turner, Jennifer J; Morton, Lindsay M; Linet, Martha S; Clarke, Christina A; Kadin, Marshall E; Vajdic, Claire M; Monnereau, Alain; Maynadié, Marc; Chiu, Brian C-H; Marcos-Gragera, Rafael; Costantini, Adele Seniori; Cerhan, James R; Weisenburger, Dennis D

2010-11-18

After publication of the updated World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues in 2008, the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) now presents an update of the hierarchical classification of lymphoid neoplasms for epidemiologic research based on the 2001 WHO classification, which we published in 2007. The updated hierarchical classification incorporates all of the major and provisional entities in the 2008 WHO classification, including newly defined entities based on age, site, certain infections, and molecular characteristics, as well as borderline categories, early and "in situ" lesions, disorders with limited capacity for clinical progression, lesions without current International Classification of Diseases for Oncology, 3rd Edition codes, and immunodeficiency-associated lymphoproliferative disorders. WHO subtypes are defined in hierarchical groupings, with newly defined groups for small B-cell lymphomas with plasmacytic differentiation and for primary cutaneous T-cell lymphomas. We suggest approaches for applying the hierarchical classification in various epidemiologic settings, including strategies for dealing with multiple coexisting lymphoma subtypes in one patient, and cases with incomplete pathologic information. The pathology materials useful for state-of-the-art epidemiology studies are also discussed. We encourage epidemiologists to adopt the updated InterLymph hierarchical classification, which incorporates the most recent WHO entities while demonstrating their relationship to older classifications.

Choroid Plexus

MedlinePlus

... Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

Spinal cord tumors: new views and future directions.

PubMed

Mechtler, Laszlo L; Nandigam, Kaveer

2013-02-01

Spinal cord tumors are uncommon neoplasms that, without treatment, can cause significant neurologic morbidity and mortality. The historic classification of spine tumors is based on the use of myelography with 3 main groups: (1) extramedullary extradural, (2) intradural extramedullary, and (3) intradural intramedullary. This chapter focuses on intramedullary spinal cord tumors (ISCTs), with an emphasis on new diagnostic imaging modalities and treatment options. The common ISCTs include ependymoma, astrocytoma and hemangioblastoma, which together account for over 90% of primary ISCTs. Rare tumors such as gangliglioma, oligodendroglioma, paraganglioma, melanocytoma, lipoma, and primary spinal cord lymphoma are also included in this review, in addition to spinal cord metastatic disease. Copyright © 2013 Elsevier Inc. All rights reserved.

Application of advanced cytometric and molecular technologies to minimal residual disease monitoring

NASA Astrophysics Data System (ADS)

Leary, James F.; He, Feng; Reece, Lisa M.

2000-04-01

Minimal residual disease monitoring presents a number of theoretical and practical challenges. Recently it has been possible to meet some of these challenges by combining a number of new advanced biotechnologies. To monitor the number of residual tumor cells requires complex cocktails of molecular probes that collectively provide sensitivities of detection on the order of one residual tumor cell per million total cells. Ultra-high-speed, multi parameter flow cytometry is capable of analyzing cells at rates in excess of 100,000 cells/sec. Residual tumor selection marker cocktails can be optimized by use of receiver operating characteristic analysis. New data minimizing techniques when combined with multi variate statistical or neural network classifications of tumor cells can more accurately predict residual tumor cell frequencies. The combination of these techniques can, under at least some circumstances, detect frequencies of tumor cells as low as one cell in a million with an accuracy of over 98 percent correct classification. Detection of mutations in tumor suppressor genes requires insolation of these rare tumor cells and single-cell DNA sequencing. Rare residual tumor cells can be isolated at single cell level by high-resolution single-cell cell sorting. Molecular characterization of tumor suppressor gene mutations can be accomplished using a combination of single- cell polymerase chain reaction amplification of specific gene sequences followed by TA cloning techniques and DNA sequencing. Mutations as small as a single base pair in a tumor suppressor gene of a single sorted tumor cell have been detected using these methods. Using new amplification procedures and DNA micro arrays it should be possible to extend the capabilities shown in this paper to screening of multiple DNA mutations in tumor suppressor and other genes on small numbers of sorted metastatic tumor cells.

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.

Lack of relevant information for tumor staging in pathology reports of primary cutaneous melanoma.

PubMed

Busam, K J

2001-05-01

For the T classification of primary cutaneous melanoma, the current American Joint Committee on Cancer staging (AJCC) system relies on tumor thickness and level of invasion. A new T classification has been proposed based on thickness and ulceration. The slides and reports of 135 departmental pathology consultations of patients referred to a major cancer center with a diagnosis of primary cutaneous invasive malignant melanoma were examined. Whether the outside pathology reports contained information on tumor thickness, level of invasion, and ulceration was recorded. Dermatopathologists had issued 76.3% of the reports and general surgical pathologists, 24.3%. Information provided was as follows: tumor thickness, 97.8%; Clark level, 71.9%; and presence or absence of ulceration, 28.1%. Of the 97 melanomas with no comment on ulceration, 17 were indeed ulcerated. Thus, the lack of a comment on ulceration cannot be equated with the absence of ulceration. The present study documents that many pathology reports on melanomas lack sufficient information for AJCC staging. Therefore, review of outside pathology material is necessary not only to confirm or revise the tumor diagnosis but also to provide clinicians with histologic parameters required for AJCC staging.

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.

Leukemia and colon tumor detection based on microarray data classification using momentum backpropagation and genetic algorithm as a feature selection method

NASA Astrophysics Data System (ADS)

Wisesty, Untari N.; Warastri, Riris S.; Puspitasari, Shinta Y.

2018-03-01

Cancer is one of the major causes of mordibility and mortality problems in the worldwide. Therefore, the need of a system that can analyze and identify a person suffering from a cancer by using microarray data derived from the patient’s Deoxyribonucleic Acid (DNA). But on microarray data has thousands of attributes, thus making the challenges in data processing. This is often referred to as the curse of dimensionality. Therefore, in this study built a system capable of detecting a patient whether contracted cancer or not. The algorithm used is Genetic Algorithm as feature selection and Momentum Backpropagation Neural Network as a classification method, with data used from the Kent Ridge Bio-medical Dataset. Based on system testing that has been done, the system can detect Leukemia and Colon Tumor with best accuracy equal to 98.33% for colon tumor data and 100% for leukimia data. Genetic Algorithm as feature selection algorithm can improve system accuracy, which is from 64.52% to 98.33% for colon tumor data and 65.28% to 100% for leukemia data, and the use of momentum parameters can accelerate the convergence of the system in the training process of Neural Network.

Colorectal Cancer Classification and Cell Heterogeneity: A Systems Oncology Approach

PubMed Central

Blanco-Calvo, Moisés; Concha, Ángel; Figueroa, Angélica; Garrido, Federico; Valladares-Ayerbes, Manuel

2015-01-01

Colorectal cancer is a heterogeneous disease that manifests through diverse clinical scenarios. During many years, our knowledge about the variability of colorectal tumors was limited to the histopathological analysis from which generic classifications associated with different clinical expectations are derived. However, currently we are beginning to understand that under the intense pathological and clinical variability of these tumors there underlies strong genetic and biological heterogeneity. Thus, with the increasing available information of inter-tumor and intra-tumor heterogeneity, the classical pathological approach is being displaced in favor of novel molecular classifications. In the present article, we summarize the most relevant proposals of molecular classifications obtained from the analysis of colorectal tumors using powerful high throughput techniques and devices. We also discuss the role that cancer systems biology may play in the integration and interpretation of the high amount of data generated and the challenges to be addressed in the future development of precision oncology. In addition, we review the current state of implementation of these novel tools in the pathological laboratory and in clinical practice. PMID:26084042

Multiplex coherent anti-Stokes Raman scattering microspectroscopy of brain tissue with higher ranking data classification for biomedical imaging

NASA Astrophysics Data System (ADS)

Pohling, Christoph; Bocklitz, Thomas; Duarte, Alex S.; Emmanuello, Cinzia; Ishikawa, Mariana S.; Dietzeck, Benjamin; Buckup, Tiago; Uckermann, Ortrud; Schackert, Gabriele; Kirsch, Matthias; Schmitt, Michael; Popp, Jürgen; Motzkus, Marcus

2017-06-01

Multiplex coherent anti-Stokes Raman scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data were collected separately in both tissue types, and the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the H&E stained reference and spontaneous Raman microscopy, proving the validity of the MCARS approach in combination with SVM.

Proposal of a new staging system for intrahepatic cholangiocarcinoma: Analysis of surgical patients from a nationwide survey of the Liver Cancer Study Group of Japan.

PubMed

Sakamoto, Yoshihiro; Kokudo, Norihiro; Matsuyama, Yutaka; Sakamoto, Michiie; Izumi, Namiki; Kadoya, Masumi; Kaneko, Shuichi; Ku, Yonson; Kudo, Masatoshi; Takayama, Tadatoshi; Nakashima, Osamu

2016-01-01

In the current American Joint Committee on Cancer/International Union Against Cancer staging system (seventh edition) for intrahepatic cholangiocarcinoma (ICC), tumor size was excluded, and periductal invasion was added as a new tumor classification-defining factor. The objective of the current report was to propose a new staging system for ICC that would be better for stratifying the survival of patients based on data from the nationwide Liver Cancer Study Group of Japan database. Of 756 patients who underwent surgical resection for ICC between 2000 and 2005, multivariate analyses of the clinicopathologic factors of 419 patients who had complete data sets were performed to elucidate relevant factors for inclusion in a new tumor classification and staging system. Overall survival data were best stratified using a cutoff value of 2 cm using a minimal P value approach to discriminate patient survival. The 5-year survival rate of 15 patients who had ICC measuring ≤ 2 cm in greatest dimension without lymph node metastasis or vascular invasion was 100%, and this cohort was defined as T1. Multivariate analysis of prognostic factors for 267 patients with lymph node-negative and metastasis-negative (N0M0) disease indicated that the number of tumors, the presence arterial invasion, and the presence major biliary invasion were independent and significant prognostic factors. The proposed new system, which included tumor number, tumor size, arterial invasion, and major biliary invasion for tumor classification, provided good stratification of overall patient survival according to disease stage. Macroscopic periductal invasion was associated with major biliary invasion and an inferior prognosis. The proposed new staging system, which includes a tumor cutoff size of 2 cm and major biliary invasion, may be useful for assigning patients to surgery. © 2015 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

"Sarcomatoid" carcinomas of the lung: a clinicopathological study of 86 cases with a new perspective on tumor classification.

PubMed

Weissferdt, Annikka; Kalhor, Neda; Correa, Arlene M; Moran, Cesar A

2017-05-01

Pulmonary sarcomatoid carcinoma includes a heterogenous group of tumors difficult to diagnose and treat. We report the clinicopathological features of 86 such tumors, including 74 pleomorphic and 12 spindle cell carcinomas, and propose a novel approach to the classification of these neoplasms in an attempt to better guide patient management. The patients were 47 men and 39 women aged 36 to 87 years (mean, 63 years) who primarily presented with shortness of breath, cough, and chest pain. Eighty-six percent of patients had a smoking history. Histologically, the pleomorphic carcinomas consisted of spindle and/or giant cells with varying proportions of conventional non-small cell carcinoma in the form of adenocarcinoma (n=29), squamous cell carcinoma (n=10), or large cell carcinoma (n=18); 17 cases contained a mix of spindle and giant cells only. The 12 spindle cell carcinomas consisted of spindle cells only. Based on the combined histopathologic and immunohistochemical features of these tumors, we were able to reanalyze the spectrum of these lesions and reclassify them accordingly. Statistical analysis revealed an overall survival at 3, 5, and 10 years of 42.9%, 34.6%, and 23.5%, respectively, and a median survival of 15 months. Log-rank test showed that in multivariate analysis, only pathological T stage was a factor associated with prognosis. The current classification of pulmonary sarcomatoid carcinomas precludes optimal triaging of these tumors with the risk of denying patients access to novel treatment. Our proposal for a reclassification of these tumors would more accurately guide patient management and facilitate targeted therapies. Copyright © 2016 Elsevier Inc. All rights reserved.

Toward automatic segmentation and quantification of tumor and stroma in whole-slide images of H and E stained rectal carcinomas

NASA Astrophysics Data System (ADS)

Geessink, Oscar G. F.; Baidoshvili, Alexi; Freling, Gerard; Klaase, Joost M.; Slump, Cornelis H.; van der Heijden, Ferdinand

2015-03-01

Visual estimation of tumor and stroma proportions in microscopy images yields a strong, Tumor-(lymph)Node- Metastasis (TNM) classification-independent predictor for patient survival in colorectal cancer. Therefore, it is also a potent (contra)indicator for adjuvant chemotherapy. However, quantification of tumor and stroma through visual estimation is highly subject to intra- and inter-observer variability. The aim of this study is to develop and clinically validate a method for objective quantification of tumor and stroma in standard hematoxylin and eosin (H and E) stained microscopy slides of rectal carcinomas. A tissue segmentation algorithm, based on supervised machine learning and pixel classification, was developed, trained and validated using histological slides that were prepared from surgically excised rectal carcinomas in patients who had not received neoadjuvant chemotherapy and/or radiotherapy. Whole-slide scanning was performed at 20× magnification. A total of 40 images (4 million pixels each) were extracted from 20 whole-slide images at sites showing various relative proportions of tumor and stroma. Experienced pathologists provided detailed annotations for every extracted image. The performance of the algorithm was evaluated using cross-validation by testing on 1 image at a time while using the other 39 images for training. The total classification error of the algorithm was 9.4% (SD = 3.2%). Compared to visual estimation by pathologists, the algorithm was 7.3 times (P = 0.033) more accurate in quantifying tissues, also showing 60% less variability. Automatic tissue quantification was shown to be both reliable and practicable. We ultimately intend to facilitate refined prognostic stratification of (colo)rectal cancer patients and enable better personalized treatment.

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.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

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

Aghaei, Faranak; Tan, Maxine; Liu, Hong

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from bothmore » tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.« less

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prognostic role of tumor necrosis in patients undergoing curative resection for gastric gastrointestinal stromal tumor: a multicenter analysis of 740 cases in China.

PubMed

Liu, Xuechao; Qiu, Haibo; Zhang, Peng; Feng, Xingyu; Chen, Tao; Li, Yong; Tao, Kaixiong; Li, Guoxin; Sun, Xiaowei; Zhou, Zhiwei

2017-12-01

Tumor necrosis is associated with poor clinical outcomes in many malignancies. We aimed to determine whether tumor necrosis was an independent predictor of outcomes in gastric gastrointestinal stromal tumors (GISTs). We retrospectively analyzed data from 740 patients undergoing curative resection for gastric GIST at four centers between 2001 and 2015. Disease-free survival (DFS) was estimated with the Kaplan-Meier method, and associations with prognosis were assessed with Cox regression models. Tumor necrosis was present in 122 cases (16.5%). The prevalence of tumor necrosis increased with higher risk-stratification, including 0.7%, 7.4%, 17.3%, and 39.3% for very low-, low-, intermediate- and high-risk tumors, respectively (P < 0.001). Tumor necrosis was associated with aggressive tumor biology, such as larger tumor size, higher mitotic index, tumor rupture, and presence of nuclear atypia (all P < 0.05). Multivariate analysis revealed that tumor necrosis was an independent predictor of unfavorable DFS (HR: 2.641; 95% CI: 1.359-5.131; P = 0.004). When stratified by the modified National Institutes of Health (NIH) classification, tumor necrosis still independently predicted DFS in high-risk patients (P = 0.001) but not in non-high-risk patients (P = 0.349). The 5-year DFS rate in high-risk patients with and without tumor necrosis was 56.5% and 82.9%, respectively (P = 0.004). Notably, the prognostic significance of tumor necrosis was maintained when the patients were stratified by age, sex, tumor location, tumor size, and mitotic index (All P < 0.05). Tumor necrosis is a useful predictor of outcomes in gastric GIST, especially in high-risk patients. Based on these results, we recommend that the current NIH classification should be further improved and expanded to include tumor necrosis as a valuable prognostic indicator. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

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.

Plexiform neurofibroma tissue classification

NASA Astrophysics Data System (ADS)

Weizman, L.; Hoch, L.; Ben Sira, L.; Joskowicz, L.; Pratt, L.; Constantini, S.; Ben Bashat, D.

2011-03-01

Plexiform Neurofibroma (PN) is a major complication of NeuroFibromatosis-1 (NF1), a common genetic disease that involving the nervous system. PNs are peripheral nerve sheath tumors extending along the length of the nerve in various parts of the body. Treatment decision is based on tumor volume assessment using MRI, which is currently time consuming and error prone, with limited semi-automatic segmentation support. We present in this paper a new method for the segmentation and tumor mass quantification of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically detects the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets yield a mean volume overlap difference of 25% as compared to manual segmentation by expert radiologist with a mean computation and interaction time of 12 minutes vs. over an hour for manual annotation. Since the user interaction in the segmentation process is minimal, our method has the potential to successfully become part of the clinical workflow.

Cancer classification using the Immunoscore: a worldwide task force.

PubMed

Galon, Jérôme; Pagès, Franck; Marincola, Francesco M; Angell, Helen K; Thurin, Magdalena; Lugli, Alessandro; Zlobec, Inti; Berger, Anne; Bifulco, Carlo; Botti, Gerardo; Tatangelo, Fabiana; Britten, Cedrik M; Kreiter, Sebastian; Chouchane, Lotfi; Delrio, Paolo; Arndt, Hartmann; Asslaber, Martin; Maio, Michele; Masucci, Giuseppe V; Mihm, Martin; Vidal-Vanaclocha, Fernando; Allison, James P; Gnjatic, Sacha; Hakansson, Leif; Huber, Christoph; Singh-Jasuja, Harpreet; Ottensmeier, Christian; Zwierzina, Heinz; Laghi, Luigi; Grizzi, Fabio; Ohashi, Pamela S; Shaw, Patricia A; Clarke, Blaise A; Wouters, Bradly G; Kawakami, Yutaka; Hazama, Shoichi; Okuno, Kiyotaka; Wang, Ena; O'Donnell-Tormey, Jill; Lagorce, Christine; Pawelec, Graham; Nishimura, Michael I; Hawkins, Robert; Lapointe, Réjean; Lundqvist, Andreas; Khleif, Samir N; Ogino, Shuji; Gibbs, Peter; Waring, Paul; Sato, Noriyuki; Torigoe, Toshihiko; Itoh, Kyogo; Patel, Prabhu S; Shukla, Shilin N; Palmqvist, Richard; Nagtegaal, Iris D; Wang, Yili; D'Arrigo, Corrado; Kopetz, Scott; Sinicrope, Frank A; Trinchieri, Giorgio; Gajewski, Thomas F; Ascierto, Paolo A; Fox, Bernard A

2012-10-03

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

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.

Development of an Automated Modality-Independent Elastographic Image Analysis System for Tumor Screening

DTIC Science & Technology

2006-02-01

further develop modality-independent elastography as a system that is able to reproducibly detect regions of increased stiffness within the breast based...tested on a tissue-like polymer phantom. elastography , breast cancer screening, image processing 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...is a map of the breast (or other tissue of interest) that reflects material inhomogeneity, such as in the case of a tumor mass that disrupts the

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

PubMed

Jones, Timothy L; Byrnes, Tiernan J; Yang, Guang; Howe, Franklyn A; Bell, B Anthony; Barrick, Thomas R

2015-03-01

There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-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.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. PMID:29554126

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.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

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%).

SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data

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

Coroller, T; Bi, W; Abedalthagafi, M

Purpose: The clinical management of meningioma is guided by its grade and biologic behavior. Currently, diagnosis of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor behavior are needed. We investigated the association between imaging features extracted from preoperative gadolinium-enhanced T1-weighted MRI and meningioma grade. Methods: We retrospectively examined the pre-operative MRI for 139 patients with de novo WHO grade I (63%) and grade II (37%) meningiomas. We investigated the predictive power of ten semantic radiologic features as determined by a neuroradiologist, fifteen radiomic features, and tumor location. Conventional (volume and diameter) imaging featuresmore » were added for comparison. AUC was computed for continuous and χ{sup 2} for discrete variables. Classification was done using random forest. Performance was evaluated using cross validation (1000 iterations, 75% training and 25% validation). All p-values were adjusted for multiple testing. Results: Significant association was observed between meningioma grade and tumor location (p<0.001) and two semantic features including intra-tumoral heterogeneity (p<0.001) and overt hemorrhage (p=0.01). Conventional (AUC 0.61–0.67) and eleven radiomic (AUC 0.60–0.70) features were significant from random (p<0.05, Noether test). Median AUC values for classification of tumor grade were 0.57, 0.71, 0.72 and 0.77 respectively for conventional, radiomic, location, and semantic features after using random forest. By combining all imaging data (semantic, radiomic, and location), the median AUC was 0.81, which offers superior predicting power to that of conventional imaging descriptors for meningioma as well as radiomic features alone (p<0.05, permutation test). Conclusion: We demonstrate a strong association between radiologic features and meningioma grade. Pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.« less

Pulmonary tumor measurements from x-ray computed tomography in one, two, and three dimensions.

PubMed

Villemaire, Lauren; Owrangi, Amir M; Etemad-Rezai, Roya; Wilson, Laura; O'Riordan, Elaine; Keller, Harry; Driscoll, Brandon; Bauman, Glenn; Fenster, Aaron; Parraga, Grace

2011-11-01

We evaluated the accuracy and reproducibility of three-dimensional (3D) measurements of lung phantoms and patient tumors from x-ray computed tomography (CT) and compared these to one-dimensional (1D) and two-dimensional (2D) measurements. CT images of three spherical and three irregularly shaped tumor phantoms were evaluated by three observers who performed five repeated measurements. Additionally, three observers manually segmented 29 patient lung tumors five times each. Follow-up imaging was performed for 23 tumors and response criteria were compared. For a single subject, imaging was performed on nine occasions over 2 years to evaluate multidimensional tumor response. To evaluate measurement accuracy, we compared imaging measurements to ground truth using analysis of variance. For estimates of precision, intraobserver and interobserver coefficients of variation and intraclass correlations (ICC) were used. Linear regression and Pearson correlations were used to evaluate agreement and tumor response was descriptively compared. For spherical shaped phantoms, all measurements were highly accurate, but for irregularly shaped phantoms, only 3D measurements were in high agreement with ground truth measurements. All phantom and patient measurements showed high intra- and interobserver reproducibility (ICC >0.900). Over a 2-year period for a single patient, there was disagreement between tumor response classifications based on 3D measurements and those generated using 1D and 2D measurements. Tumor volume measurements were highly reproducible and accurate for irregular, spherical phantoms and patient tumors with nonuniform dimensions. Response classifications obtained from multidimensional measurements suggest that 3D measurements provide higher sensitivity to tumor response. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

Mammographic mass classification based on possibility theory

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2017-03-01

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

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.

The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection.

PubMed

Sun, Yingqiang; Lu, Chengbo; Li, Xiaobo

2018-05-17

The gene expression profile has the characteristics of a high dimension, low sample, and continuous type, and it is a great challenge to use gene expression profile data for the classification of tumor samples. This paper proposes a cross-entropy based multi-filter ensemble (CEMFE) method for microarray data classification. Firstly, multiple filters are used to select the microarray data in order to obtain a plurality of the pre-selected feature subsets with a different classification ability. The top N genes with the highest rank of each subset are integrated so as to form a new data set. Secondly, the cross-entropy algorithm is used to remove the redundant data in the data set. Finally, the wrapper method, which is based on forward feature selection, is used to select the best feature subset. The experimental results show that the proposed method is more efficient than other gene selection methods and that it can achieve a higher classification accuracy under fewer characteristic genes.

Cysts

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Astrocytoma

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Chondrosarcoma

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

Ependymoma

MedlinePlus

... Pituitary Tumor PNET Schwannoma 2016 WHO Classification Risk Factors Brain Tumor Facts Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information Brain Anatomy Brain ...

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.

Surgical and molecular pathology of pancreatic neoplasms.

PubMed

Hackeng, Wenzel M; Hruban, Ralph H; Offerhaus, G Johan A; Brosens, Lodewijk A A

2016-06-07

Histologic characteristics have proven to be very useful for classifying different types of tumors of the pancreas. As a result, the major tumor types in the pancreas have long been classified based on their microscopic appearance. Recent advances in whole exome sequencing, gene expression profiling, and knowledge of tumorigenic pathways have deepened our understanding of the underlying biology of pancreatic neoplasia. These advances have not only confirmed the traditional histologic classification system, but also opened new doors to early diagnosis and targeted treatment. This review discusses the histopathology, genetic and epigenetic alterations and potential treatment targets of the five major malignant pancreatic tumors - pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumor, solid-pseudopapillary neoplasm, acinar cell carcinoma and pancreatoblastoma.

The International Neuroblastoma Risk Group (INRG) Classification System: An INRG Task Force Report

PubMed Central

Cohn, Susan L.; Pearson, Andrew D.J.; London, Wendy B.; Monclair, Tom; Ambros, Peter F.; Brodeur, Garrett M.; Faldum, Andreas; Hero, Barbara; Iehara, Tomoko; Machin, David; Mosseri, Veronique; Simon, Thorsten; Garaventa, Alberto; Castel, Victoria; Matthay, Katherine K.

2009-01-01

Purpose Because current approaches to risk classification and treatment stratification for children with neuroblastoma (NB) vary greatly throughout the world, it is difficult to directly compare risk-based clinical trials. The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Patients and Methods The statistical and clinical significance of 13 potential prognostic factors were analyzed in a cohort of 8,800 children diagnosed with NB between 1990 and 2002 from North America and Australia (Children's Oncology Group), Europe (International Society of Pediatric Oncology Europe Neuroblastoma Group and German Pediatric Oncology and Hematology Group), and Japan. Survival tree regression analyses using event-free survival (EFS) as the primary end point were performed to test the prognostic significance of the 13 factors. Results Stage, age, histologic category, grade of tumor differentiation, the status of the MYCN oncogene, chromosome 11q status, and DNA ploidy were the most highly statistically significant and clinically relevant factors. A new staging system (INRG Staging System) based on clinical criteria and tumor imaging was developed for the INRG Classification System. The optimal age cutoff was determined to be between 15 and 19 months, and 18 months was selected for the classification system. Sixteen pretreatment groups were defined on the basis of clinical criteria and statistically significantly different EFS of the cohort stratified by the INRG criteria. Patients with 5-year EFS more than 85%, more than 75% to ≤ 85%, ≥ 50% to ≤ 75%, or less than 50% were classified as very low risk, low risk, intermediate risk, or high risk, respectively. Conclusion By defining homogenous pretreatment patient cohorts, the INRG classification system will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world and the development of international collaborative studies. PMID:19047291

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.

Intracranial solitary fibrous tumors/hemangiopericytomas: first report of malignant progression.

PubMed

Apra, Caroline; Mokhtari, Karima; Cornu, Philippe; Peyre, Matthieu; Kalamarides, Michel

2018-06-01

OBJECTIVE Meningeal solitary fibrous tumors/hemangiopericytomas (MSFTs/HPCs) are rare intracranial tumors resembling meningiomas. Their classification was redefined in 2016 by the World Health Organization (WHO) as benign Grade I fibrohyaline type, intermediate Grade II hypercellular type, and malignant highly mitotic Grade III. This grouping is based on common histological features and identification of a common NAB2-STAT6 fusion. METHODS The authors retrospectively identified 49 cases of MSFT/HPC. Clinical data were obtained from the medical records, and all cases were analyzed according to this new 2016 WHO grading classification in order to identify malignant transformations. RESULTS Recurrent surgery was performed in 18 (37%) of 49 patients. Malignant progression was identified in 5 (28%) of these 18 cases, with 3 Grade I and 2 Grade II tumors progressing to Grade III, 3-13 years after the initial surgery. Of 31 Grade III tumors treated in this case series, 16% (5/31) were proved to be malignant progressions from lower-grade tumors. CONCLUSIONS Low-grade MSFTs/HPCs can transform into higher grades as shown in this first report of such progression. This is a decisive argument in favor of a common identity for MSFT and meningeal HPC. High-grade MSFTs/HPCs tend to recur more often and be associated with reduced overall survival. Malignant progression could be one mechanism explaining some recurrences or metastases, and justifying long-term follow-up, even for patients with Grade I tumors.

Aided diagnosis methods of breast cancer based on machine learning

NASA Astrophysics Data System (ADS)

Zhao, Yue; Wang, Nian; Cui, Xiaoyu

2017-08-01

In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

[Analysis of the factors contributing to diabetes insipidus after surgeries for craniopharyngiomas].

PubMed

Luo, Shi; Pan, Jun; Qi, Song-Tao; Fang, Lu-Xiong; Fan, Jun; Liu, Bao-Guo

2009-03-01

To analyze the factors contributing to the occurrence of diabetes insipidus after operations for craniopharyngiomas. A total of 121 cases of diabetes insipidus following surgeries for craniopharyngiomas were retrospectively analyzed and the factors associated with postoperative diabetes insipidus were analyzed. The incidence of diabetes insipidus was 27.3% (33/121 cases) before the operation, 89.9% (107/1119) early after the operation and 39.8%(37/93) in later stages after the operation. The occurrence of early postoperative diabetes insipidus showed a significant relation to the classification and calcification of the craniopharyngioma. Patients with supradiaphragmatic and extraventricular tumors had the lowest incidence of postoperative diabetes insipidus. Late postoperative diabetes insipidus was closely correlated to such factors as age, classification of craniopharyngioma, and intraoperative treatment of the pituitary stalk, but not to the scope of tumor resection or tumor calcification. Late diabetes insipidus was more frequent in children and patients with severed pituitary stalk. The incidence of late postoperative diabetes insipidus was significantly higher in patients with supradiaphragmatic and extra-intraventricular tumors than in those with tumors beneath the diaphragma sellae and extraventricular tumors. Postoperative diabetes insipidus following surgeries for craniopharyngiomas is closely related to the tumor classification, calcification and pituitary stalk protection.

Esophageal cancer: anatomic particularities, staging, and imaging techniques.

PubMed

Encinas de la Iglesia, J; Corral de la Calle, M A; Fernández Pérez, G C; Ruano Pérez, R; Álvarez Delgado, A

2016-01-01

Cancer of the esophagus is a tumor with aggressive behavior that is usually diagnosed in advanced stages. The absence of serosa allows it to spread quickly to neighboring mediastinal structures, and an extensive lymphatic drainage network facilitates tumor spread even in early stages. The current TNM classification, harmonized with the classification for gastric cancer, provides new definitions for the anatomic classification, adds non-anatomic characteristics of the tumor, and includes tumors of the gastroesophageal junction. Combining endoscopic ultrasound, computed tomography, positron emission tomography, and magnetic resonance imaging provides greater accuracy in determining the initial clinical stage, and these imaging techniques play an essential role in the selection, planning, and evaluation of treatment. In this article, we review some particularities that explain the behavior of this tumor and we describe the current TNM staging system; furthermore, we discuss the different imaging tests available for its evaluation and include a diagnostic algorithm. Copyright © 2016 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI

NASA Astrophysics Data System (ADS)

Ahmed, S.; Iftekharuddin, K. M.; Ogg, R. J.; Laningham, F. H.

2009-02-01

Our previous works suggest that fractal-based texture features are very useful for detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. In this work, we investigate and compare efficacy of our texture features such as fractal and multifractional Brownian motion (mBm), and intensity along with another useful level-set based shape feature in PF tumor segmentation. We study feature selection and ranking using Kullback -Leibler Divergence (KLD) and subsequent tumor segmentation; all in an integrated Expectation Maximization (EM) framework. We study the efficacy of all four features in both multimodality as well as disparate MRI modalities such as T1, T2 and FLAIR. Both KLD feature plots and information theoretic entropy measure suggest that mBm feature offers the maximum separation between tumor and non-tumor tissues in T1 and FLAIR MRI modalities. The same metrics show that intensity feature offers the maximum separation between tumor and non-tumor tissue in T2 MRI modality. The efficacies of these features are further validated in segmenting PF tumor using both single modality and multimodality MRI for six pediatric patients with over 520 real MR images.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

New classification of epilepsy-related neoplasms: The clinical perspective.

PubMed

Kasper, Burkhard S; Kasper, Ekkehard M

2017-02-01

Neoplastic CNS lesions are a common cause of focal epilepsy refractory to anticonvulsant treatment, i.e. long-term epilepsy-associated tumors (LEATs). Epileptogenic tumors encompass a variety of intriguing lesions, e.g. dysembryoplastic neuroepithelial tumors or gangliogliomas, which differ from more common CNS neoplasms in their clinical context as well as on histopathology. Long-term epilepsy-associated tumor classification is a rapidly evolving issue in surgical neuropathology, with new entities still being elucidated. One major issue to be resolved is the inconsistent tissue criteria applied to LEAT accounting for high diagnostic variability between individual centers and studies, a problem recently leading to a proposal for a new histopathological classification by Blümcke et al. in Acta Neuropathol. 2014; 128: 39-54. While a new approach to tissue diagnosis is appreciated and needed, histomorphological criteria alone will not suffice and we here approach the situation of encountering a neoplastic lesion in an epilepsy patient from a clinical perspective. Clinical scenarios to be supported by an advanced LEAT classification will be illustrated and discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiplex profiling of tumor-associated proteolytic activity in serum of colorectal cancer patients.

PubMed

Yepes, Diego; Costina, Victor; Pilz, Lothar R; Hofheinz, Ralf; Neumaier, Michael; Findeisen, Peter

2014-06-01

The monitoring of tumor-associated protease activity in blood specimens has recently been proposed as new diagnostic tool in cancer research. In this paper, we describe the screening of a peptide library for identification of reporter peptides (RPs) that are selectively cleaved in serum specimens from colorectal cancer patients and investigate the benefits of RP multiplexing. A library of 144 RPs was constructed that contained amino acid sequences of abundant plasma proteins. Proteolytic cleavage of RPs was monitored with MS. Five RPs that were selectively cleaved in serum specimens from tumor patients were selected for further validation in serum specimens of colorectal tumor patients (n = 30) and nonmalignant controls (n = 60). RP spiking and subsequent quantification of proteolytic fragments with LC-MS showed good reproducibility with CVs always below 26%. The linear discriminant analysis and PCA revealed that a combination of RPs for diagnostic classification is superior to single markers. Classification accuracy reached 88% (79/90) when all five markers were combined. Functional protease profiling with RPs might improve the laboratory-based diagnosis, monitoring and prognosis of malignant disease, and has to be evaluated thoroughly in future studies. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition.

PubMed

Chen, WenXue; Lou, HaiYan; Zhang, HongPing; Nie, Xiu; Lan, WenXian; Yang, YongXia; Xiang, Yun; Qi, JianPin; Lei, Hao; Tang, HuiRu; Chen, FenEr; Deng, Feng

2011-07-01

Clinical data have shown that survival rates vary considerably among brain tumor patients, according to the type and grade of the tumor. Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS (1)H NMRS) can provide important information on tumor biology and metabolism. These metabolic fingerprints can then be used for tumor classification and grading, with great potential value for tumor diagnosis. We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies, including two astrocytomas (grade I), 12 astrocytomas (grade II), eight anaplastic astrocytomas (grade III), three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS (1)H NMRS. The results were correlated with pathological features using multivariate data analysis, including principal component analysis (PCA). There were significant differences in the levels of N-acetyl-aspartate (NAA), creatine, myo-inositol, glycine and lactate between tumors of different grades (P<0.05). There were also significant differences in the ratios of NAA/creatine, lactate/creatine, myo-inositol/creatine, glycine/creatine, scyllo-inositol/creatine and alanine/creatine (P<0.05). A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%. HRMAS (1)H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Microscale Laminar Vortices for High-Purity Extraction and Release of Circulating Tumor Cells.

PubMed

Hur, Soojung Claire; Che, James; Di Carlo, Dino

2017-01-01

Circulating tumor cells (CTCs) are disseminated tumor cells that reflect the tumors of origin and can provide a liquid biopsy that would potentially enable noninvasive tumor profiling, treatment monitoring, and identification of targeted treatments. Accurate and rapid purification of CTCs holds great potential to improve cancer care but the task remains technically challenging. Microfluidic isolation of CTCs within microscale vortices enables high-throughput and size-based purification of rare CTCs from bodily fluids. Collected cells are highly pure, viable, and easily accessible, allowing seamless integration with various downstream applications. Here, we describe how to fabricate the High-Throughput Vortex Chip (Vortex-HT) and to process diluted whole blood for CTC collection. Lastly, immunostaining and imaging protocols for CTC classification and corresponding CTC image galleries are reported.

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

What does the 4th edition of the World Health Organization Classification of Head and Neck Tumors (2017) bring new about mucosal melanomas?

PubMed

Xavier Júnior, José Cândido Caldeira; Ocanha-Xavier, Juliana Polizel

2018-03-01

The recently published 4th Edition of the World Health Organization Classification of Head and Neck Tumors addresses the most relevant and updated aspects of tumor biology, including clinical presentation, histopathology, immunohistochemistry, and prognosis of head and neck tumors. The objective of the present study is to compare these updates to the 3rd edition of that book with regard to mucosal melanomas and to highlight the potential factors that differ those tumors from cutaneous melanomas. We observed progress in the understanding of oral and sinonasal mucosal melanomas, which also present themselves, in the molecular scope, differently form cutaneous melanomas.

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

A Filter Feature Selection Method Based on MFA Score and Redundancy Excluding and It's Application to Tumor Gene Expression Data Analysis.

PubMed

Li, Jiangeng; Su, Lei; Pang, Zenan

2015-12-01

Feature selection techniques have been widely applied to tumor gene expression data analysis in recent years. A filter feature selection method named marginal Fisher analysis score (MFA score) which is based on graph embedding has been proposed, and it has been widely used mainly because it is superior to Fisher score. Considering the heavy redundancy in gene expression data, we proposed a new filter feature selection technique in this paper. It is named MFA score+ and is based on MFA score and redundancy excluding. We applied it to an artificial dataset and eight tumor gene expression datasets to select important features and then used support vector machine as the classifier to classify the samples. Compared with MFA score, t test and Fisher score, it achieved higher classification accuracy.

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.

BRAIN TUMOR SEGMENTATION WITH SYMMETRIC TEXTURE AND SYMMETRIC INTENSITY-BASED DECISION FORESTS.

PubMed

Bianchi, Anthony; Miller, James V; Tan, Ek Tsoon; Montillo, Albert

2013-04-01

Accurate automated segmentation of brain tumors in MR images is challenging due to overlapping tissue intensity distributions and amorphous tumor shape. However, a clinically viable solution providing precise quantification of tumor and edema volume would enable better pre-operative planning, treatment monitoring and drug development. Our contributions are threefold. First, we design efficient gradient and LBPTOP based texture features which improve classification accuracy over standard intensity features. Second, we extend our texture and intensity features to symmetric texture and symmetric intensity which further improve the accuracy for all tissue classes. Third, we demonstrate further accuracy enhancement by extending our long range features from 100mm to a full 200mm. We assess our brain segmentation technique on 20 patients in the BraTS 2012 dataset. Impact from each contribution is measured and the combination of all the features is shown to yield state-of-the-art accuracy and speed.

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

Metabolic Tumor Burden Assessed by Dual Time Point [18F]FDG PET/CT in Locally Advanced Breast Cancer: Relation with Tumor Biology.

PubMed

Garcia-Vicente, Ana María; Pérez-Beteta, Julián; Pérez-García, Víctor Manuel; Molina, David; Jiménez-Londoño, German Andrés; Soriano-Castrejón, Angel; Martínez-González, Alicia

2017-08-01

The aim of the study was to investigate the influence of dual time point 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography/x-ray computed tomography (PET/CT) on the standard uptake value (SUV) and volume-based metabolic variables of breast lesions and their relation with biological characteristics and molecular phenotypes. Retrospective analysis including 67 patients with locally advanced breast cancer (LABC). All patients underwent a dual time point [ 18 F]FDG PET/CT, 1 h (PET-1) and 3 h (PET-2) after [ 18 F]FDG administration. Tumors were segmented following a three-dimensional methodology. Semiquantitative metabolic variables (SUV max , SUV mean , and SUV peak ) and volume-based variables (metabolic tumor volume, MTV, and total lesion glycolysis, TLG) were obtained. Biologic prognostic parameters, such as the hormone receptors status, p53, HER2 expression, proliferation rate (Ki-67), and grading were obtained. Molecular phenotypes and risk-classification [low: luminal A, intermediate: luminal B HER2 (-) or luminal B HER2 (+), and high: HER2 pure or triple negative] were established. Relations between clinical and biological variables with the metabolic parameters were studied. The relevance of each metabolic variable in the prediction of phenotype risk was assessed using a multivariate analysis. SUV-based variables and TLG obtained in the PET-1 and PET-2 showed high and significant correlations between them. MTV and SUV variables (SUV max , SUV mean , and SUV peak ) where only marginally correlated. Significant differences were found between mean SUV variables and TLG obtained in PET-1 and PET-2. High and significant associations were found between metabolic variables obtained in PET-1 and their homonymous in PET-2. Based on that, only relations of PET-1 variables with biological tumor characteristics were explored. SUV variables showed associations with hormone receptors status (p < 0.001 and p = 0.001 for estrogen and progesterone receptor, respectively) and risk-classification according to phenotype (SUV max , p = 0.003; SUV mean , p = 0.004; SUV peak , p = 0.003). As to volume-based variables, only TLG showed association with hormone receptors status (estrogen, p < 0.001; progesterone, p = 0.031), risk-classification (p = 0.007), and grade (p = 0.036). Hormone receptor negative tumors, high-grade tumors, and high-risk phenotypes showed higher TLG values. No association was found between the metabolic variables and Ki-67, HER2, or p53 expression. Statistical differences were found between mean SUV-based variables and TLG obtained in the dual time point PET/CT. Most of PET-derived parameters showed high association with molecular factors of breast cancer. However, dual time point PET/CT did not offer any added value to the single PET acquisition with respect to the relations with biological variables, based on PET-1 SUV, and volume-based variables were predictors of those obtained in PET-2.

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

PubMed

Goos, Jeroen A C M; Coupé, Veerle M H; van de Wiel, Mark A; Diosdado, Begoña; Delis-Van Diemen, Pien M; Hiemstra, Annemieke C; de Cuba, Erienne M V; Beliën, Jeroen A M; Menke-van der Houven van Oordt, C Willemien; Geldof, Albert A; Meijer, Gerrit A; Hoekstra, Otto S; Fijneman, Remond J A

2016-01-12

Prognosis of patients with colorectal cancer liver metastasis (CRCLM) is estimated based on clinicopathological models. Stratifying patients based on tumor biology may have additional value. Tissue micro-arrays (TMAs), containing resected CRCLM and corresponding primary tumors from a multi-institutional cohort of 507 patients, were immunohistochemically stained for 18 candidate biomarkers. Cross-validated hazard rate ratios (HRRs) for overall survival (OS) and the proportion of HRRs with opposite effect (P(HRR < 1) or P(HRR > 1)) were calculated. A classifier was constructed by classification and regression tree (CART) analysis and its prognostic value determined by permutation analysis. Correlations between protein expression in primary tumor-CRCLM pairs were calculated. Based on their putative prognostic value, EGFR (P(HRR < 1) = .02), AURKA (P(HRR < 1) = .02), VEGFA (P(HRR < 1) = .02), PTGS2 (P(HRR < 1) = .01), SLC2A1 (P(HRR > 1) < 01), HIF1α (P(HRR > 1) = .06), KCNQ1 (P(HRR > 1) = .09), CEA (P (HRR > 1) = .05) and MMP9 (P(HRR < 1) = .07) were included in the CART analysis (n = 201). The resulting classifier was based on AURKA, PTGS2 and MMP9 expression and was associated with OS (HRR 2.79, p < .001), also after multivariate analysis (HRR 3.57, p < .001). The prognostic value of the biomarker-based classifier was superior to the clinicopathological model (p = .001). Prognostic value was highest for colon cancer patients (HRR 5.71, p < .001) and patients not treated with systemic therapy (HRR 3.48, p < .01). Classification based on protein expression in primary tumors could be based on AURKA expression only (HRR 2.59, p = .04). A classifier was generated for patients with CRCLM with improved prognostic value compared to the standard clinicopathological prognostic parameters, which may aid selection of patients who may benefit from adjuvant systemic therapy.

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.

Lucien J. Rubinstein: enduring contributions to neuro-oncology.

PubMed

Mut, Melike; Lopes, M Beatriz S; Shaffrey, Mark

2005-04-15

Dr. Lucien Rubinstein is best remembered for his significant contributions to the field of neuropathology, particularly in the classification of nervous system tumors. His accomplishments in basic neuro-oncology and in the formulation of diagnostic principles reflected a unique talent for synthesizing fundamental clinicopathological concepts based on skillful diagnostic investigation and a thorough understanding of neurobiology. Dr. Rubinstein was the leader in the establishment of cell cultures from central nervous system (CNS) tumors. He meticulously analyzed both light and electron microscopic features of CNS tumors, recorded his findings, and patiently drew sketches to be shared generously with his colleagues and students. As a pioneer in neuropathology, in his work Dr. Rubinstein set the foundation for many enduring concepts in neurosurgery, neuro-oncology, neurology, and basic tumor biology.

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.

Novel, improved grading system(s) for IDH-mutant astrocytic gliomas.

PubMed

Shirahata, Mitsuaki; Ono, Takahiro; Stichel, Damian; Schrimpf, Daniel; Reuss, David E; Sahm, Felix; Koelsche, Christian; Wefers, Annika; Reinhardt, Annekathrin; Huang, Kristin; Sievers, Philipp; Shimizu, Hiroaki; Nanjo, Hiroshi; Kobayashi, Yusuke; Miyake, Yohei; Suzuki, Tomonari; Adachi, Jun-Ichi; Mishima, Kazuhiko; Sasaki, Atsushi; Nishikawa, Ryo; Bewerunge-Hudler, Melanie; Ryzhova, Marina; Absalyamova, Oksana; Golanov, Andrey; Sinn, Peter; Platten, Michael; Jungk, Christine; Winkler, Frank; Wick, Antje; Hänggi, Daniel; Unterberg, Andreas; Pfister, Stefan M; Jones, David T W; van den Bent, Martin; Hegi, Monika; French, Pim; Baumert, Brigitta G; Stupp, Roger; Gorlia, Thierry; Weller, Michael; Capper, David; Korshunov, Andrey; Herold-Mende, Christel; Wick, Wolfgang; Louis, David N; von Deimling, Andreas

2018-04-23

According to the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO), IDH-mutant astrocytic gliomas comprised WHO grade II diffuse astrocytoma, IDH-mutant (AII IDHmut ), WHO grade III anaplastic astrocytoma, IDH-mutant (AAIII IDHmut ), and WHO grade IV glioblastoma, IDH-mutant (GBM IDHmut ). Notably, IDH gene status has been made the major criterion for classification while the manner of grading has remained unchanged: it is based on histological criteria that arose from studies which antedated knowledge of the importance of IDH status in diffuse astrocytic tumor prognostic assessment. Several studies have now demonstrated that the anticipated differences in survival between the newly defined AII IDHmut and AAIII IDHmut have lost their significance. In contrast, GBM IDHmut still exhibits a significantly worse outcome than its lower grade IDH-mutant counterparts. To address the problem of establishing prognostically significant grading for IDH-mutant astrocytic gliomas in the IDH era, we undertook a comprehensive study that included assessment of histological and genetic approaches to prognosis in these tumors. A discovery cohort of 211 IDH-mutant astrocytic gliomas with an extended observation was subjected to histological review, image analysis, and DNA methylation studies. Tumor group-specific methylation profiles and copy number variation (CNV) profiles were established for all gliomas. Algorithms for automated CNV analysis were developed. All tumors exhibiting 1p/19q codeletion were excluded from the series. We developed algorithms for grading, based on molecular, morphological and clinical data. Performance of these algorithms was compared with that of WHO grading. Three independent cohorts of 108, 154 and 224 IDH-mutant astrocytic gliomas were used to validate this approach. In the discovery cohort several molecular and clinical parameters were of prognostic relevance. Most relevant for overall survival (OS) was CDKN2A/B homozygous deletion. Other parameters with major influence were necrosis and the total number of CNV. Proliferation as assessed by mitotic count, which is a key parameter in 2016 CNS WHO grading, was of only minor influence. Employing the parameters most relevant for OS in our discovery set, we developed two models for grading these tumors. These models performed significantly better than WHO grading in both the discovery and the validation sets. Our novel algorithms for grading IDH-mutant astrocytic gliomas overcome the challenges caused by introduction of IDH status into the WHO classification of diffuse astrocytic tumors. We propose that these revised approaches be used for grading of these tumors and incorporated into future WHO criteria.

Monitoring of peri-distal gastrectomy carbohydrate antigen 19-9 level in gastric juice and its significance

PubMed Central

Xu, A-Man; Huang, Lei; Han, Wen-Xiu; Wei, Zhi-Jian

2014-01-01

Gastric carcinoma is one of the most common and deadly malignancies nowadays, and carbohydrate antigen 19-9 (CA 19-9) in gastric juice has been rarely studied. To compare peri-distal gastrectomy (DG) gastric juice and serum CA 19-9 and reveal its significance, we selected 67 patients diagnosed with gastric carcinoma who underwent DG, and collected their perioperative gastric juice whose CA 19-9 was detected, with serum CA 19-9 monitored as a comparison. We found that: gastric juice CA 19-9 pre-gastrectomy was significantly correlated with tumor TNM classification, regarding tumor size, level of gastric wall invaded, differentiated grade and number of metastatic lymph nodes as influencing factors, while serum CA 19-9 revealed little information; gastric juice CA 19-9 was significantly correlated with radical degree, and regarded number of resected lymph nodes and classification of cutting edge as impact factors; thirteen patients whose gastric juice CA 19-9 rose post-DG showed features indicating poor prognosis; the difference of gastric juice CA 19-9 between pre- and post-gastrectomy was correlated with tumor TNM classification and radical degree, and regarded tumor size, number of resected metastatic and normal lymph nodes, sum of distances from tumor to cutting edges and classification of cutting edge as influential factors. We conclude that peri-DG gastric juice CA 19-9 reveals much information about tumor and radical gastrectomy, and may indicate prognosis; while serum CA 19-9 has limited significance. PMID:24482710

Neoplasms of the Neuroendocrine Pancreas: An Update in the Classification, Definition, and Molecular Genetic Advances.

PubMed

Guilmette, Julie M; Nosé, Vania

2018-06-14

This review focuses on discussing the main modifications of the recently published 2017 WHO Classification of Neoplasms of the Neuroendocrine Pancreas (panNEN). Recent updates separate pancreatic neuroendocrine tumors into 2 broad categories: well-differentiated pancreatic neuroendocrine tumors (panNET) and poorly differentiated pancreatic neuroendocrine carcinoma (panNEC), and incorporates a new subcategory of "well-differentiated high-grade NET (G3)" to the well-differentiated NET category. This new classification algorithm aims to improve the prediction of clinical outcomes and survival and help clinicians select better therapeutic strategies for patient care and management. In addition, these neuroendocrine neoplasms are capable of producing large quantity of hormones leading to clinical hormone hypersecretion syndromes. These functioning tumors include, insulinomas, glucagonomas, somatostatinomas, gastrinomas, VIPomas, serotonin-producing tumors, and ACTH-producing tumors. Although most panNENs arise as sporadic diseases, a subset of these heterogeneous tumors present as parts on inherited genetic syndromes, such as multiple endocrine neoplasia type 1, von Hippel-Lindau, neurofibromatosis type 1, tuberous sclerosis, and glucagon cell hyperplasia and neoplasia syndromes. Characteristic clinical and morphologic findings for certain functioning and syndromic panNENs should alert both pathologists and clinicians as appropriate patient management and possible genetic counseling may be necessary.

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors

PubMed Central

Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L.; Coll, Jean-Luc

2016-01-01

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines. PMID:26892874

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors.

PubMed

Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L; Coll, Jean-Luc

2016-02-19

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines.

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.

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.

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.

Submucosal invasion and risk of lymph node invasion in early Barrett’s cancer: potential impact of different classification systems on patient management

PubMed Central

Fotis, Dimitrios; Doukas, Michael; Wijnhoven, Bas PL; Didden, Paul; Biermann, Katharina; Bruno, Marco J

2015-01-01

Background Due to the high mortality and morbidity rates of esophagectomy, endoscopic mucosal resection (EMR) is increasingly used for the curative treatment of early low risk Barrett’s adenocarcinoma. Objective This retrospective cohort study aimed to assess the prevalence of lymph node metastases (LNM) in submucosal (T1b) esophageal adenocarcinomas (EAC) in relation to the absolute depth of submucosal tumor invasion and demonstrate the efficacy of EMR for low risk (well and moderately differentiated without lymphovascular invasion) EAC with sm1 invasion (submucosal invasion ≤500 µm) according to the Paris classification. Methods The pathology reports of patients undergoing endoscopic resection and surgery from January 1994 until December 2013 at one center were reviewed and 54 patients with submucosal invasion were included. LNM were evaluated in surgical specimens and by follow up examinations in case of EMR. Results No LNM were observed in 10 patients with sm1 adenocarcinomas that underwent endoscopic resection. Three of them underwent supplementary endoscopic eradication therapy with a median follow up of 27 months for patients with sm1 tumors. In the surgical series two patients (29%) with sm1 invasion according to the pragmatic classification (subdivision of the submucosa into three equal thirds), staged as sm2-3 in the Paris classification, had LNM. The rate of LNM for surgical patients with low risk sm1 tumors was 10% according to the pragmatic classification and 0% according to Paris classification. Conclusion Different classifications of the tumor invasion depth lead to different LNM risks and treatment strategies for sm1 adenocarcinomas. Patients with low risk sm1 adenocarcinomas appear to be suitable candidates for EMR. PMID:26668743

MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status.

PubMed

Bady, Pierre; Sciuscio, Davide; Diserens, Annie-Claire; Bloch, Jocelyne; van den Bent, Martin J; Marosi, Christine; Dietrich, Pierre-Yves; Weller, Michael; Mariani, Luigi; Heppner, Frank L; Mcdonald, David R; Lacombe, Denis; Stupp, Roger; Delorenzi, Mauro; Hegi, Monika E

2012-10-01

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg12434587 [corrected] and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.

Deep learning for brain tumor classification

NASA Astrophysics Data System (ADS)

Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

2017-03-01

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

The prognostic value of natural killer cell infiltration in resected pulmonary adenocarcinoma.

PubMed

Takanami, I; Takeuchi, K; Giga, M

2001-06-01

Natural cytotoxicity caused by mediated natural killer cells is believed to play an important role in host-cancer defense mechanisms. Immunohistochemically, we have detected natural killer cells in tissue specimens from patients with pulmonary adenocarcinoma and have assessed their clinical characteristics. Using the monoclonal antibody for CD57 specific marker for natural killer cells, we quantified natural killer cell infiltration in 150 patients with pulmonary adenocarcinoma who underwent curative tumor resection to investigate the relationship between natural killer cell counts and clinicopathologic factors and prognosis. The natural killer cell count was significantly related to the regulation of tumor progression, involving T classification, N classification, and stage (P =.01 for T classification or stage; P =.02 for N classification). A significant difference in the rate of patient survival was detected between those patients whose tumors had either high or low natural killer cell counts in both the overall and stage I groups (P =.0002 for the overall group; P =.049 for the stage I group). These data indicate that natural killer infiltration may contribute to the regulation of tumor progression and that the natural killer cell count can serve as a useful prognostic marker in overall and stage I pulmonary adenocarcinoma.

Comparison of the current AJCC-TNM numeric-based with a new anatomical location-based lymph node staging system for gastric cancer: A western experience.

PubMed

Galizia, Gennaro; Lieto, Eva; Auricchio, Annamaria; Cardella, Francesca; Mabilia, Andrea; Diana, Anna; Castellano, Paolo; De Vita, Ferdinando; Orditura, Michele

2017-01-01

In gastric cancer, the current AJCC numeric-based lymph node staging does not provide information on the anatomical extent of the disease and lymphadenectomy. A new anatomical location-based node staging, proposed by Choi, has shown better prognostic performance, thus soliciting Western world validation. Data from 284 gastric cancers undergoing radical surgery at the Second University of Naples from 2000 to 2014 were reviewed. The lymph nodes were reclassified into three groups (lesser and greater curvature, and extraperigastric nodes); presence of any metastatic lymph node in a given group was considered positive, prompting a new N and TNM stage classification. Receiver-operating-characteristic (ROC) curves for censored survival data and bootstrap methods were used to compare the capability of the two models to predict tumor recurrence. More than one third of node positive patients were reclassified into different N and TNM stages by the new system. Compared to the current staging system, the new classification significantly correlated with tumor recurrence rates and displayed improved indices of prognostic performance, such as the Bayesian information criterion and the Harrell C-index. Higher values at survival ROC analysis demonstrated a significantly better stratification of patients by the new system, mostly in the early phase of the follow-up, with a worse prognosis in more advanced new N stages, despite the same current N stage. This study suggests that the anatomical location-based classification of lymph node metastasis may be an important tool for gastric cancer prognosis and should be considered for future revision of the TNM staging system.

Residual tumor size and IGCCCG risk classification predict additional vascular procedures in patients with germ cell tumors and residual tumor resection: a multicenter analysis of the German Testicular Cancer Study Group.

PubMed

Winter, Christian; Pfister, David; Busch, Jonas; Bingöl, Cigdem; Ranft, Ulrich; Schrader, Mark; Dieckmann, Klaus-Peter; Heidenreich, Axel; Albers, Peter

2012-02-01

Residual tumor resection (RTR) after chemotherapy in patients with advanced germ cell tumors (GCT) is an important part of the multimodal treatment. To provide a complete resection of residual tumor, additional surgical procedures are sometimes necessary. In particular, additional vascular interventions are high-risk procedures that require multidisciplinary planning and adequate resources to optimize outcome. The aim was to identify parameters that predict additional vascular procedures during RTR in GCT patients. A retrospective analysis was performed in 402 GCT patients who underwent 414 RTRs in 9 German Testicular Cancer Study Group (GTCSG) centers. Overall, 339 of 414 RTRs were evaluable with complete perioperative data sets. The RTR database was queried for additional vascular procedures (inferior vena cava [IVC] interventions, aortic prosthesis) and correlated to International Germ Cell Cancer Collaborative Group (IGCCCG) classification and residual tumor volume. In 40 RTRs, major vascular procedures (23 IVC resections with or without prosthesis, 11 partial IVC resections, and 6 aortic prostheses) were performed. In univariate analysis, the necessity of IVC intervention was significantly correlated with IGCCCG (14.1% intermediate/poor vs 4.8% good; p=0.0047) and residual tumor size (3.7% size < 5 cm vs 17.9% size ≥ 5 cm; p < 0.0001). In multivariate analysis, IVC intervention was significantly associated with residual tumor size ≥ 5 cm (odds ratio [OR]: 4.61; p=0.0007). In a predictive model combining residual tumor size and IGCCCG classification, every fifth patient (20.4%) with a residual tumor size ≥ 5 cm and intermediate or poor prognosis needed an IVC intervention during RTR. The need for an aortic prosthesis showed no correlation to either IGCCCG (p=0.1811) or tumor size (p=0.0651). The necessity for IVC intervention during RTR is correlated to residual tumor size and initial IGCCCG classification. Patients with high-volume residual tumors and intermediate or poor risk features must initially be identified as high-risk patients for vascular procedures and therefore should be referred to specialized surgical centers with the ad hoc possibility of vascular interventions. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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

Substrate optimization and clinical validation of reporter peptides for MS-based protease profiling in serum specimens: a new approach for diagnosis of malignant disease.

PubMed

Yepes, Diego; Jacob, Anette; Dauber, Marc; Costina, Victor; Hofheinz, Ralf; Neumaier, Michael; Findeisen, Peter

2011-07-01

The progression of many solid tumors is characterized by the release of tumor-associated proteases, such as cancer procoagulant, MMP2 and MMP7. Consequently, the detection of tumor-specific proteolytic activity in serum specimens has recently been proposed as a new diagnostic tool in oncology. However, tumor-associated proteases are highly diluted in serum specimens and it is challenging to identify substrates that are specifically cleaved. In this study, we describe the systematic optimization of a synthetic peptide substrate using a positional scanning synthetic combinatorial library (PS-SCL) approach. The initial reporter peptide (RP) comprises of the cleavage site, WKPYDAAD, that is part of the coagulation factor X, the natural substrate of the tumor-associated cysteine protease cancer procoagulant (EC 3.4.22.26). Specifically, the amino acid substitution of aspartatic acid (D) in position P1' against asparagine (N) improved the processing of respective RPs in serum specimens from patients with colorectal tumors compared to healthy controls. Proteolytic fragments of RPs accumulated during prolonged incubation with serum specimens and were quantified with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Finally, the optimized RP with the cleaved motif WKPYNAAD was combined with the RPs, VPLSLTMG and IPVSLRSG, that were cleaved by the tumor-associated proteases, MMP2 and MMP7, respectively. The diagnostic accuracy of MS-based protease profiling was evaluated for this triplex RP mix in a cohort of 50 serum specimens equally divided into colorectal cancer patients and healthy control individuals. Multiparametric analysis showed an AUC value of 0.90 for the receiver operating characteristic curve and was superior to the classification accuracy of the single markers. Our results demonstrate that RPs for MS-based protease profiling can systematically be optimized with a PS-SCL. Furthermore, the combination of different RPs can additionally increase the classification accuracy of functional protease profiling, and this in turn could lead to improved diagnosis, monitoring and prognosis of malignant disease.

Relation between the location of elements in the periodic table and tumor-uptake rate.

PubMed

Ando, A; Ando, I; Hiraki, T; Hisada, K

1985-01-01

The bipositive ions and anions, with few exceptions, indicated a low tumor uptake rate. On the other hand, compounds of Hg, Au and Bi, which have a strong binding power to protein, showed a high tumor uptake rate. As Hg2+, Au+ and Bi3+ are soft acids according to the classification of Lewis acids, it was thought that these ions would bind strongly to soft bases (R-SH, R-S-) present in tumor tissue. For many hard acids such as 85Sr2+, 67Ga3+, 181Hf4+, and 95Nb5+, tumor uptake rates are shown as a function of ionic potentials (valency/ionic radii) of the metal ions. Considering the present data and previously reported results, it was presumed that hard acids of trivalence, quadrivalence and pentavalence would replace calcium in the calcium salts of hard bases (calcium salts of acid mucopolysaccharides, etc.). Ionic potentials of alkaline metals and Tl were small, but the tumor-uptake rate of these elements indicated various values. As Ge and Sb are bound by covalent bonds to chloride, GeCl4 and SbCl3 behaved differently from many metallic compounds in tumor tissue.

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.

Cystic renal tumors: new entities and novel concepts.

PubMed

Moch, Holger

2010-05-01

Cystic renal neoplasms and renal epithelial stromal tumors are diagnostically challenging and represent some novel tumor entities. In this article, clinical and pathologic features of established and novel entities are discussed. Predominantly cystic renal tumors include cystic nephroma/mixed epithelial and stromal tumor, synovial sarcoma, and multilocular cystic renal cell carcinoma. These entities are own tumor entities of the 2004 WHO classification of renal tumors. Tubulocystic carcinoma and acquired cystic disease-associated renal cell carcinoma are neoplasms with an intrinsically cystic growth pattern. Both tumor types should be included in a future WHO classification as novel entities owing to their characteristic features. Cysts and clear cell renal cell carcinoma frequently coexist within the kidneys of patients with von Hippel-Lindau disease. Sporadic clear cell renal cell carcinomas often contain cysts, usually as a minor component. Some clear cell renal cell carcinomas have prominent cysts, and multilocular cystic renal cell carcinoma is composed almost exclusively of cysts. Recent molecular findings suggest that clear cell renal cancer may develop through cyst-dependent and cyst-independent molecular pathways.

Within-brain classification for brain tumor segmentation.

PubMed

Havaei, Mohammad; Larochelle, Hugo; Poulin, Philippe; Jodoin, Pierre-Marc

2016-05-01

In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

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.

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

PubMed

Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

2017-10-01

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Inter-reader reproducibility of dynamic contrast-enhanced magnetic resonance imaging in patients with non-small cell lung cancer treated with bevacizumab and erlotinib.

PubMed

van den Boogaart, Vivian E M; de Lussanet, Quido G; Houben, Ruud M A; de Ruysscher, Dirk; Groen, Harry J M; Marcus, J Tim; Smit, Egbert F; Dingemans, Anne-Marie C; Backes, Walter H

2016-03-01

Objectives When evaluating anti-tumor treatment response by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) it is necessary to assure its validity and reproducibility. This has not been well addressed in lung tumors. Therefore we have evaluated the inter-reader reproducibility of response classification by DCE-MRI in patients with non-small cell lung cancer (NSCLC) treated with bevacizumab and erlotinib enrolled in a multicenter trial. Twenty-one patients were scanned before and 3 weeks after start of treatment with DCE-MRI in a multicenter trial. The scans were evaluated by two independent readers. The primary lung tumor was used for response assessment. Responses were assessed in terms of relative changes in tumor mean trans endothelial transfer rate (K(trans)) and its heterogeneity in terms of the spatial standard deviation. Reproducibility was expressed by the inter-reader variability, intra-class correlation coefficient (ICC) and dichotomous response classification. The inter-reader variability and ICC for the relative K(trans) were 5.8% and 0.930, respectively. For tumor heterogeneity the inter-reader variability and ICC were 0.017 and 0.656, respectively. For the two readers the response classification for relative K(trans) was concordant in 20 of 21 patients (k=0.90, p<0.0001) and for tumor heterogeneity in 19 of 21 patients (k=0.80, p<0.0001). Strong agreement was seen with regard to the inter-reader variability and reproducibility of response classification by the two readers of lung cancer DCE-MRI scans. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The International Society of Urological Pathology (ISUP) Vancouver Classification of Renal Neoplasia.

PubMed

Srigley, John R; Delahunt, Brett; Eble, John N; Egevad, Lars; Epstein, Jonathan I; Grignon, David; Hes, Ondrej; Moch, Holger; Montironi, Rodolfo; Tickoo, Satish K; Zhou, Ming; Argani, Pedram

2013-10-01

The classification working group of the International Society of Urological Pathology consensus conference on renal neoplasia was in charge of making recommendations regarding additions and changes to the current World Health Organization Classification of Renal Tumors (2004). Members of the group performed an exhaustive literature review, assessed the results of the preconference survey and participated in the consensus conference discussion and polling activities. On the basis of the above inputs, there was consensus that 5 entities should be recognized as new distinct epithelial tumors within the classification system: tubulocystic renal cell carcinoma (RCC), acquired cystic disease-associated RCC, clear cell (tubulo) papillary RCC, the MiT family translocation RCCs (in particular t(6;11) RCC), and hereditary leiomyomatosis RCC syndrome-associated RCC. In addition, there are 3 rare carcinomas that were considered as emerging or provisional new entities: thyroid-like follicular RCC; succinate dehydrogenase B deficiency-associated RCC; and ALK translocation RCC. Further reports of these entities are required to better understand the nature and behavior of these highly unusual tumors. There were a number of new concepts and suggested modifications to the existing World Health Organization 2004 categories. Within the clear cell RCC group, it was agreed upon that multicystic clear cell RCC is best considered as a neoplasm of low malignant potential. There was agreement that subtyping of papillary RCC is of value and that the oncocytic variant of papillary RCC should not be considered as a distinct entity. The hybrid oncocytic chromophobe tumor, which is an indolent tumor that occurs in 3 settings, namely Birt-Hogg-Dubé Syndrome, renal oncocytosis, and as a sporadic neoplasm, was placed, for the time being, within the chromophobe RCC category. Recent advances related to collecting duct carcinoma, renal medullary carcinoma, and mucinous spindle cell and tubular RCC were elucidated. Outside of the epithelial category, advances in our understanding of angiomyolipoma, including the epithelioid and epithelial cystic variants, were considered. In addition, the apparent relationship between cystic nephroma and mixed epithelial and stromal tumor was discussed, with the consensus that these tumors form a spectrum of neoplasia. Finally, it was thought that the synovial sarcoma should be removed from the mixed epithelial and mesenchymal category and placed within the sarcoma group. The new classification is to be referred to as the International Society of Urological Pathology Vancouver Classification of Renal Neoplasia.

Molecular classification of soft tissue sarcomas and its clinical applications

PubMed Central

Jain, Shilpa; Xu, Ruliang; Prieto, Victor G; Lee, Peng

2010-01-01

Sarcomas are a heterogeneous group of tumors that are traditionally classified according to the morphology and type of tissue that they resemble, such as rhabdomyosarcoma, which resembles skeletal muscle. However, the cell of origin is unclear in numerous sarcomas. Molecular genetics analyses have not only assisted in understanding the molecular mechanism in sarcoma pathogenesis but also demonstrated new relationships within different types of sarcomas leading to a more proper classification of sarcomas. Molecular classification based on the genetic alteration divides sarcomas into two main categories: (i) sarcomas with specific genetic alterations; which can further be subclassified based on a) reciprocal translocations resulting in oncogenic fusion transcripts (e.g. EWSR1-FLI1 in Ewing sarcoma) and b) specific oncogenic mutations (e.g. KIT and PDGFRA mutations in gastrointestinal stromal tumors) and (ii) sarcomas displaying multiple, complex karyotypic abnormalities with no specific pattern, including leiomyo-sarcoma, and pleomorphic liposarcoma. These specific genetic alterations are an important adjunct to standard morphological and immunohistochemical diagnoses, and in some cases have a prognostic value, e. g., Ewing family tumors, synovial sarcoma, and alveolar rhabdomyosarcoma. In addition, these studies may also serve as markers to detect minimal residual disease and can aid in staging or monitor the efficacy of therapy. Furthermore, sarcoma-specific fusion genes and other emerging molecular events may also represent potential targets for novel therapeutic approaches such as Gleevec for dermatofibrosarcoma protuberans. Therefore, increased understanding of the molecular biology of sarcomas is leading towards development of newer and more effective treatment regimens. The review focuses on recent advances in molecular genetic alterations having an impact on diagnostics, prognostication and clinical management of selected sarcomas. PMID:20490332

Using X-Ray In-Line Phase-Contrast Imaging for the Investigation of Nude Mouse Hepatic Tumors

PubMed Central

Zhang, Lu; Luo, Shuqian

2012-01-01

The purpose of this paper is to report the noninvasive imaging of hepatic tumors without contrast agents. Both normal tissues and tumor tissues can be detected, and tumor tissues in different stages can be classified quantitatively. We implanted BEL-7402 human hepatocellular carcinoma cells into the livers of nude mice and then imaged the livers using X-ray in-line phase-contrast imaging (ILPCI). The projection images' texture feature based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet transforms (DTCWT) were extracted to discriminate normal tissues and tumor tissues. Different stages of hepatic tumors were classified using support vector machines (SVM). Images of livers from nude mice sacrificed 6 days after inoculation with cancer cells show diffuse distribution of the tumor tissue, but images of livers from nude mice sacrificed 9, 12, or 15 days after inoculation with cancer cells show necrotic lumps in the tumor tissue. The results of the principal component analysis (PCA) of the texture features based on GLCM of normal regions were positive, but those of tumor regions were negative. The results of PCA of the texture features based on DTCWT of normal regions were greater than those of tumor regions. The values of the texture features in low-frequency coefficient images increased monotonically with the growth of the tumors. Different stages of liver tumors can be classified using SVM, and the accuracy is 83.33%. Noninvasive and micron-scale imaging can be achieved by X-ray ILPCI. We can observe hepatic tumors and small vessels from the phase-contrast images. This new imaging approach for hepatic cancer is effective and has potential use in the early detection and classification of hepatic tumors. PMID:22761929

Proposal for a new T-stage classification system for distal cholangiocarcinoma: a 10-institution study from the U.S. Extrahepatic Biliary Malignancy Consortium.

PubMed

Postlewait, Lauren M; Ethun, Cecilia G; Le, Nina; Pawlik, Timothy M; Buettner, Stefan; Poultsides, George; Tran, Thuy; Idrees, Kamran; Isom, Chelsea A; Fields, Ryan C; Krasnick, Bradley; Weber, Sharon M; Salem, Ahmed; Martin, Robert C G; Scoggins, Charles; Shen, Perry; Mogal, Harveshp D; Schmidt, Carl; Beal, Eliza; Hatzaras, Ioannis; Vitiello, Gerardo; Cardona, Kenneth; Maithel, Shishir K

2016-10-01

Seventh AJCC distal cholangiocarcinoma T-stage classification inadequately separates patients by survival. This retrospective study aimed to define a novel T-stage system to better stratify patients after resection. Curative-intent pancreaticoduodenectomies for distal cholangiocarcinoma (1/2000-5/2015) at 10 US institutions were included. Relationships between tumor characteristics and overall survival (OS) were assessed and incorporated into a novel T-stage classification. 176 patients (median follow-up: 24mo) were included. Current AJCC T-stage was not associated with OS (T1: 23mo, T2: 20mo, T3: 25mo, T4: 12mo; p = 0.355). Tumor size ≥3 cm and presence of lymphovascular invasion (LVI) were associated with decreased OS on univariate and multivariable analyses. Patients were stratified into 3 groups [T1: size <3 cm and (-)LVI (n = 69; 39.2%); T2: size ≥3 cm and (-)LVI or size <3 cm and (+)LVI (n = 82; 46.6%); and T3: size ≥3 cm and (+)LVI (n = 25; 14.2%)]. Each progressive proposed T-stage was associated with decreased median OS (T1: 35mo; T2: 20mo; T3: 8mo; p = 0.002). Current AJCC distal cholangiocarcinoma T-stage does not adequately stratify patients by survival. This proposed T-stage classification, based on tumor size and LVI, better differentiates patient outcomes after resection and could be considered for incorporation into the next AJCC distal cholangiocarcinoma staging system. Copyright © 2016 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

American Society of Neuroradiology

MedlinePlus

... Tumors of the Central Nervous System: A Practical Approach for Gliomas, Part 1. Basic Tumor Genetics The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A Practical Approach for Gliomas, Part 2. Isocitrate Dehydrogenase Status—Imaging ...

An intraoperative diagnosis of parotid gland tumors using Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Yan, Bing; Wen, Zhining; Li, Yi; Li, Longjiang; Xue, Lili

2014-11-01

The preoperative and intraoperative diagnosis of parotid gland tumors is difficult, but is important for their surgical management. In order to explore an intraoperative diagnostic method, Raman spectroscopy is applied to detect the normal parotid gland and tumors, including pleomorphic adenoma, Warthin’s tumor and mucoepidermoid carcinoma. In the 600-1800 cm-1 region of the Raman shift, there are numerous spectral differences between the parotid gland and tumors. Compared with Raman spectra of the normal parotid gland, the Raman spectra of parotid tumors show an increase of the peaks assigned to nucleic acids and proteins, but a decrease of the peaks related to lipids. Spectral differences also exist between the spectra of parotid tumors. Based on these differences, a remarkable classification and diagnosis of the parotid gland and tumors are carried out by support vector machine (SVM), with high accuracy (96.7~100%), sensitivity (93.3~100%) and specificity (96.7~100%). Raman spectroscopy combined with SVM has a great potential to aid the intraoperative diagnosis of parotid tumors and could provide an accurate and rapid diagnostic approach.

Update from the 4th Edition of the World Health Organization Classification of Head and Neck Tumours: Odontogenic and Maxillofacial Bone Tumors.

PubMed

Wright, John M; Vered, Marilena

2017-03-01

The 4th edition of the World Health Organization's Classification of Head and Neck Tumours was published in January of 2017. This article provides a summary of the changes to Chapter 4 Tumours of the oral cavity and mobile tongue and Chapter 8 Odontogenic and maxillofacial bone tumours. Odontogenic cysts which were eliminated from the 3rd 2005 edition were included in the 4th edition as well as other unique allied conditons of the jaws. Many new tumors published since 2005 have been included in the 2017 classification.

Long-term outcome of 2920 patients with cancers of the esophagus and esophagogastric junction: evaluation of the New Union Internationale Contre le Cancer/American Joint Cancer Committee staging system.

PubMed

Gertler, Ralf; Stein, Hubert J; Langer, Rupert; Nettelmann, Marc; Schuster, Tibor; Hoefler, Heinz; Siewert, Joerg-Ruediger; Feith, Marcus

2011-04-01

We analyzed the long-term outcome of patients operated for esophageal cancer and evaluated the new seventh edition of the tumor-node-metastasis classification for cancers of the esophagus. Retrospective analysis and new classification. Data of a single-center cohort of 2920 patients operated for cancers of the esophagus according to the seventh edition are presented. Statistical methods to evaluate survival and the prognostic performance of the staging systems included Kaplan-Meier analyses and time-dependent receiver-operating-characteristic-analysis. Union Internationale Contre le Cancer stage, R-status, histologic tumor type and age were identified as independent prognostic factors for cancers of the esophagus. Grade and tumor site, additional parameters in the new American Joint Cancer Committee prognostic groupings, were not significantly correlated with survival. Esophageal adenocarcinoma showed a significantly better long-term prognosis after resection than squamous cell carcinoma (P < 0.0001). The new number-dependent N-classification proved superior to the former site-dependent classification with significantly decreasing prognosis with the increasing number of lymph node metastases (P < 0.001). The new subclassification of T1 tumors also revealed significant differences in prognosis between pT1a and pT1b patients (P < 0.001). However, the multiple new Union Internationale Contre le Cancer and American Joint Cancer Committee subgroupings did not prove distinctive for survival between stages IIA and IIB, between IIIA and IIIB, and between IIIC and IV. The new seventh edition of the tumor-node-metastasis classification improved the predictive ability for cancers of the esophagus; however, stage groups could be condensed to a clinically relevant number. Differences in patient characteristics, pathogenesis, and especially survival clearly identify adenocarcinomas and squamous cell carcinoma of the esophagus as 2 separate tumor entities requiring differentiated therapeutic concepts.

The Consensus Molecular Subtypes of Colorectal Cancer

PubMed Central

Guinney, Justin; Dienstmann, Rodrigo; Wang, Xin; de Reyniès, Aurélien; Schlicker, Andreas; Soneson, Charlotte; Marisa, Laetitia; Roepman, Paul; Nyamundanda, Gift; Angelino, Paolo; Bot, Brian M.; Morris, Jeffrey S.; Simon, Iris M.; Gerster, Sarah; Fessler, Evelyn; de Sousa e Melo, Felipe; Missiaglia, Edoardo; Ramay, Hena; Barras, David; Homicsko, Krisztian; Maru, Dipen; Manyam, Ganiraju C.; Broom, Bradley; Boige, Valerie; Perez-Villamil, Beatriz; Laderas, Ted; Salazar, Ramon; Gray, Joe W.; Hanahan, Douglas; Tabernero, Josep; Bernards, Rene; Friend, Stephen H.; Laurent-Puig, Pierre; Medema, Jan Paul; Sadanandam, Anguraj; Wessels, Lodewyk; Delorenzi, Mauro; Kopetz, Scott; Vermeulen, Louis; Tejpar, Sabine

2015-01-01

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, evident metabolic dysregulation; and CMS4 (Mesenchymal, 23%), prominent transforming growth factor β activation, stromal invasion, and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intra-tumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC – with clear biological interpretability – and the basis for future clinical stratification and subtype–based targeted interventions. PMID:26457759

Cloud-scale genomic signals processing classification analysis for gene expression microarray data.

PubMed

Harvey, Benjamin; Soo-Yeon Ji

2014-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA sequence data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological inference by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale classification analysis of microarray data using Wavelet thresholding in a Cloud environment to identify significantly expressed features. This paper proposes a novel methodology that uses Wavelet based Denoising to initialize a threshold for determination of significantly expressed genes for classification. Additionally, this research was implemented and encompassed within cloud-based distributed processing environment. The utilization of Cloud computing and Wavelet thresholding was used for the classification 14 tumor classes from the Global Cancer Map (GCM). The results proved to be more accurate than using a predefined p-value for differential expression classification. This novel methodology analyzed Wavelet based threshold features of gene expression in a Cloud environment, furthermore classifying the expression of samples by analyzing gene patterns, which inform us of biological processes. Moreover, enabling researchers to face the present and forthcoming challenges that may arise in the analysis of data in functional genomics of large microarray datasets.

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

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.

Childhood Brain and Spinal Cord Tumors Treatment Overview (PDQ®)—Health Professional Version

Cancer.gov

Pediatric primary brain and CNS tumors are a diverse group of diseases that together constitute the most common solid tumor of childhood. Get detailed information about the diagnosis, classification, prognosis, and treatment of childhood brain and spinal cord tumors in this comprehensive summary for clinicians.

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

PubMed Central

Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible. PMID:29666661

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

PubMed

Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

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

Clear-cell differentiation and lymphatic invasion, but not the revised TNM classification, predict lymph node metastases in pT1 penile cancer: a clinicopathologic study of 76 patients from a low incidence area.

PubMed

Mannweiler, Sebastian; Sygulla, Stephan; Tsybrovskyy, Oleksiy; Razmara, Yas; Pummer, Karl; Regauer, Sigrid

2013-10-01

Prediction of lymph node (LN) metastases in penile invasive cancer relies on clinical features and histologic characteristics of the primary tumor. Correct prediction, however, is difficult, as only 50% patients undergoing lymphadenectomies will have LN metastases. In 2009, the tumor, nodes, metastases (TNM) classification for staging of early penile cancers was revised. We tested the predictive accuracy of the revised TNM classification in a low incidence area for penile carcinoma. The presence of LN metastases in 76 men with pT1 penile cancers was correlated with the 2009 TNM subclassification, which is based on a combined evaluation of tumor grade and lymphatic invasion, but also with individual parameters, such as histologic grade, lymphatic invasion, perineural invasion, invasion depth, growth pattern and human papilloma virus (HPV) status. 76pT1 penile cancers were reclassified into 31pT1a squamous cell carcinomas (SCC) and 45pT1b (41 SCC; 4 clear-cell carcinomas); 12/22 men (55%; 8 SCC, 4 clear-cell carcinomas) undergoing lymphadenectomy for enlarged inguinal lymph nodes had metastases, 54 patients without enlarged LN and lymphadenectomies had no LN metastases during follow-up of median 47 months. Statistically, clear cell differentiation of the primary carcinoma was highly associated with metastases (100% clear-cell carcinomas vs. 11% SCC) and poor survival (50% vs. 5.5%). Among conventional SCC, only lymphatic invasion showed a highly significant association with metastases with 100% specificity. The 2009 TNM classification, tumor grade alone, perineural invasion, growth pattern, invasion depth or HPV status could not predict LN status. Lymphadenectomy for enlarged LN resulted in 100% sensitivity and 42% predictive probability for identifying metastases and a 16% false positive rate. Statistically, survival correlated significantly with clear-cell differentiation and with lymphatic invasion in both clear-cell carcinomas and conventional SCC. Penile clear-cell carcinomas are more aggressive cancers than SCC. Our observation suggests a benefit of a prophylactic lymphadenectomy for patients with clear-cell carcinomas. Among conventional SCC, only lymphatic invasion predicted LN metastases. Neither tumor grade alone nor perineural invasion, growth pattern, depth of invasion, and subgrouping according to the revised TNM classification correlated with metastases. Clinical evaluation of the LN status was superior to histologic risk stratification. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

TNM: evolution and relation to other prognostic factors.

PubMed

Sobin, Leslie H

2003-01-01

The TNM Classification describes the anatomic extent of cancer. TNM's ability to separately classify the individual tumor (T), node (N), and metastasis (M) elements and then group them into stages differs from other cancer staging classifications (e.g., Dukes), which are only concerned with summarized groups. The objectives of the TNM Classification are to aid the clinician in the planning of treatment, give some indication of prognosis, assist in the evaluation of the results of treatment, and facilitate the exchange of information. During the past 50 years, the TNM system has evolved under the influence of advances in diagnosis and treatment. Radiographic imaging (e.g., endoscopic ultrasound for the depth of invasion of esophageal and rectal tumors) has improved the accuracy of the clinical T, N, and M classifications. Advances in treatment have necessitated more detail in some T4 categories. Developments in multimodality therapy have increased the importance of the "y" symbol and the R (residual tumor) classification. New surgical techniques have resulted in the elaboration of the sentinel node (sn) symbol. The use of immunohistochemistry has resulted in the classification of isolated tumor cells and their distinction from micrometastasis. The most important challenge facing users of the TNM Classification is how it should interface with the large number of non-anatomic prognostic factors that are currently in use or under study. As non-anatomic prognostic factors become widely used, the TNM system provides an inviting foundation upon which to build a prognostic classification; however, this carries a risk that the system will be overwhelmed by a variety of prognostic data. An anatomic extent-of-disease classification is needed to aid practitioners in selecting the initial therapeutic approach, stratifying patients for therapeutic studies, evaluating non-anatomic prognostic factors at specific anatomic stages, comparing the weight of non-anatomic factors with extent of disease, and communicating the extent of disease data in a uniform manner. Methods are needed to express the overall prognosis without losing the vital anatomic content of TNM. These methods should be able to integrate multiple prognostic factors, including TNM, while permitting the TNM system to remain intact and distinct. This article discusses examples of such approaches.

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

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

Detection of bladder metabolic artifacts in (18)F-FDG PET imaging.

PubMed

Roman-Jimenez, Geoffrey; Crevoisier, Renaud De; Leseur, Julie; Devillers, Anne; Ospina, Juan David; Simon, Antoine; Terve, Pierre; Acosta, Oscar

2016-04-01

Positron emission tomography using (18)F-fluorodeoxyglucose ((18)F-FDG-PET) is a widely used imaging modality in oncology. It enables significant functional information to be included in analyses of anatomical data provided by other image modalities. Although PET offers high sensitivity in detecting suspected malignant metabolism, (18)F-FDG uptake is not tumor-specific and can also be fixed in surrounding healthy tissue, which may consequently be mistaken as cancerous. PET analyses may be particularly hampered in pelvic-located cancers by the bladder׳s physiological uptake potentially obliterating the tumor uptake. In this paper, we propose a novel method for detecting (18)F-FDG bladder artifacts based on a multi-feature double-step classification approach. Using two manually defined seeds (tumor and bladder), the method consists of a semi-automated double-step clustering strategy that simultaneously takes into consideration standard uptake values (SUV) on PET, Hounsfield values on computed tomography (CT), and the distance to the seeds. This method was performed on 52 PET/CT images from patients treated for locally advanced cervical cancer. Manual delineations of the bladder on CT images were used in order to evaluate bladder uptake detection capability. Tumor preservation was evaluated using a manual segmentation of the tumor, with a threshold of 42% of the maximal uptake within the tumor. Robustness was assessed by randomly selecting different initial seeds. The classification averages were 0.94±0.09 for sensitivity, 0.98±0.01 specificity, and 0.98±0.01 accuracy. These results suggest that this method is able to detect most (18)F-FDG bladder metabolism artifacts while preserving tumor uptake, and could thus be used as a pre-processing step for further non-parasitized PET analyses. Copyright © 2016. Published by Elsevier Ltd.

Computerized Interpretation of Dynamic Breast MRI

DTIC Science & Technology

2006-05-01

correction, tumor segmentation , extraction of computerized features that help distinguish between benign and malignant lesions, and classification. Our...for assessing tumor extent in 3D. The primary feature used for 3D tumor segmentation is the postcontrast enhancement vector. Tumor segmentation is a...Appendix B. 4. Investigation of methods for automatic tumor segmentation We developed an automatic method for assessing tumor extent in 3D. The

A pragmatic clinicopathobiological grouping/staging system for gliomas: proposal of the Indian TNM subcommittee on brain tumors.

PubMed

Gupta, Tejpal; Sarin, Rajiv; Jalali, Rakesh; Sharma, Suash; Kurkure, Purna; Goel, Atul

2009-01-01

There is no universally accepted staging system for primary brain tumors wherein prognostication is mainly based on complex composite indices. To develop a simple, pragmatic, and widely applicable grouping/staging system for gliomas, the most common primary brain tumor. An expert neurooncology panel with representation from radiation oncology, neurosurgery, pathology, radiology, and medical oncology had several rounds of discussion on issues pertinent to brain tumor staging. The trade off was between the accuracy of prognostic categorization and a pragmatic, widely applicable approach. The Tumor-Node-Metastasis staging was considered irrelevant for gliomas that seldom metastasize to lymphatics or outside the neuraxis. Instead, a 4-point staging/grouping system is proposed, using histological grade as the main prognostic variable and at least one stage migration based on other unfavorable features such as tumor location (brainstem); age (<5 years for all grades, >50 years for high-grade, and >40 years for low-grade gliomas); poor neurological performance status (NPS 2-4); multicentricity and/or gliomatosis; and adverse biological parameters (proliferative index, angiogenesis markers, apoptotic index, cytogenetic abnormalities, and molecular markers). In absence of a grouping/staging system for primary brain tumors, prognostification is mostly based on complex composite indices. The proposed clinicopathobiological grouping/staging system for gliomas is a simple, pragmatic, and user-friendly tool with a potential to fulfill the objectives of staging classification.

Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology

NASA Astrophysics Data System (ADS)

Sharma, Harshita; Zerbe, Norman; Heim, Daniel; Wienert, Stephan; Lohmann, Sebastian; Hellwich, Olaf; Hufnagl, Peter

2016-03-01

This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

Inverse imaging of the breast with a material classification technique.

PubMed

Manry, C W; Broschat, S L

1998-03-01

In recent publications [Chew et al., IEEE Trans. Blomed. Eng. BME-9, 218-225 (1990); Borup et al., Ultrason. Imaging 14, 69-85 (1992)] the inverse imaging problem has been solved by means of a two-step iterative method. In this paper, a third step is introduced for ultrasound imaging of the breast. In this step, which is based on statistical pattern recognition, classification of tissue types and a priori knowledge of the anatomy of the breast are integrated into the iterative method. Use of this material classification technique results in more rapid convergence to the inverse solution--approximately 40% fewer iterations are required--as well as greater accuracy. In addition, tumors are detected early in the reconstruction process. Results for reconstructions of a simple two-dimensional model of the human breast are presented. These reconstructions are extremely accurate when system noise and variations in tissue parameters are not too great. However, for the algorithm used, degradation of the reconstructions and divergence from the correct solution occur when system noise and variations in parameters exceed threshold values. Even in this case, however, tumors are still identified within a few iterations.

Histological, molecular and functional subtypes of breast cancers

PubMed Central

Malhotra, Gautam K; Zhao, Xiangshan; Band, Hamid

2010-01-01

Increased understanding of the molecular heterogeneity that is intrinsic to the various subtypes of breast cancer will likely shape the future of breast cancer diagnosis, prognosis and treatment. Advances in the field over the last several decades have been remarkable and have clearly translated into better patient care as evidenced by the earlier detection, better prognosis and new targeted therapies. There have been two recent advances in the breast cancer research field that have lead to paradigm shifts: first, the identification of intrinsic breast tumor subtypes, which has changed the way we think about breast cancer and second, the recent characterization of cancer stem cells (CSCs), which are suspected to be responsible for tumor initiation, recurrence and resistance to therapy. These findings have opened new exciting avenues to think about breast cancer therapeutic strategies. While these advances constitute major paradigm shifts within the research realm, the clinical arena has yet to adopt and apply our understanding of the molecular basis of the disease to early diagnosis, prognosis and therapy of breast cancers. Here, we will review the current clinical approach to classification of breast cancers, newer molecular-based classification schemes and potential future of biomarkers representing a functional classification of breast cancer. PMID:21057215

Intravital third harmonic generation microscopy of collective melanoma cell invasion

PubMed Central

Weigelin, Bettina; Bakker, Gert-Jan; Friedl, Peter

2012-01-01

Cancer cell invasion is an adaptive process based on cell-intrinsic properties to migrate individually or collectively, and their adaptation to encountered tissue structure acting as barrier or providing guidance. Whereas molecular and physical mechanisms of cancer invasion are well-studied in 3D in vitro models, their topographic relevance, classification and validation toward interstitial tissue organization in vivo remain incomplete. Using combined intravital third and second harmonic generation (THG, SHG), and three-channel fluorescence microscopy in live tumors, we here map B16F10 melanoma invasion into the dermis with up to 600 µm penetration depth and reconstruct both invasion mode and tissue tracks to establish invasion routes and outcome. B16F10 cells preferentially develop adaptive invasion patterns along preformed tracks of complex, multi-interface topography, combining single-cell and collective migration modes, without immediate anatomic tissue remodeling or destruction. The data suggest that the dimensionality (1D, 2D, 3D) of tissue interfaces determines the microanatomy exploited by invading tumor cells, emphasizing non-destructive migration along microchannels coupled to contact guidance as key invasion mechanisms. THG imaging further detected the presence and interstitial dynamics of tumor-associated microparticles with submicron resolution, revealing tumor-imposed conditioning of the microenvironment. These topographic findings establish combined THG, SHG and fluorescence microscopy in intravital tumor biology and provide a template for rational in vitro model development and context-dependent molecular classification of invasion modes and routes. PMID:29607252

Pathological and Molecular Evaluation of Pancreatic Neoplasms

PubMed Central

Rishi, Arvind; Goggins, Michael; Wood, Laura D.; Hruban, Ralph H.

2015-01-01

Pancreatic neoplasms are morphologically and genetically heterogeneous and include wide variety of neoplasms ranging from benign to malignant with an extremely poor clinical outcome. Our understanding of these pancreatic neoplasms has improved significantly with recent advances in cancer sequencing. Awareness of molecular pathogenesis brings in new opportunities for early detection, improved prognostication, and personalized gene-specific therapies. Here we review the pathological classification of pancreatic neoplasms from their molecular and genetic perspective. All of the major tumor types that arise in the pancreas have been sequenced, and a new classification that incorporates molecular findings together with pathological findings is now possible (Table 1). This classification has significant implications for our understanding of why tumors aggregate in some families, for the development of early detection tests, and for the development of personalized therapies for patients with established cancers. Here we describe this new classification using the framework of the standard histological classification. PMID:25726050

Comparison of the current AJCC-TNM numeric-based with a new anatomical location-based lymph node staging system for gastric cancer: A western experience

PubMed Central

Auricchio, Annamaria; Cardella, Francesca; Mabilia, Andrea; Diana, Anna; Castellano, Paolo; De Vita, Ferdinando; Orditura, Michele

2017-01-01

Background In gastric cancer, the current AJCC numeric-based lymph node staging does not provide information on the anatomical extent of the disease and lymphadenectomy. A new anatomical location-based node staging, proposed by Choi, has shown better prognostic performance, thus soliciting Western world validation. Study design Data from 284 gastric cancers undergoing radical surgery at the Second University of Naples from 2000 to 2014 were reviewed. The lymph nodes were reclassified into three groups (lesser and greater curvature, and extraperigastric nodes); presence of any metastatic lymph node in a given group was considered positive, prompting a new N and TNM stage classification. Receiver-operating-characteristic (ROC) curves for censored survival data and bootstrap methods were used to compare the capability of the two models to predict tumor recurrence. Results More than one third of node positive patients were reclassified into different N and TNM stages by the new system. Compared to the current staging system, the new classification significantly correlated with tumor recurrence rates and displayed improved indices of prognostic performance, such as the Bayesian information criterion and the Harrell C-index. Higher values at survival ROC analysis demonstrated a significantly better stratification of patients by the new system, mostly in the early phase of the follow-up, with a worse prognosis in more advanced new N stages, despite the same current N stage. Conclusions This study suggests that the anatomical location-based classification of lymph node metastasis may be an important tool for gastric cancer prognosis and should be considered for future revision of the TNM staging system. PMID:28380037

Application of Sal classification to parotid gland fine-needle aspiration cytology: 10-year retrospective analysis of 312 patients.

PubMed

Kilavuz, Ahmet Erdem; Songu, Murat; İmre, Abdulkadir; Arslanoğlu, Secil; Özkul, Yilmaz; Pinar, Ercan; Ateş, Düzgün

2018-05-01

The accuracy of fine-needle aspiration biopsy (FNAB) is controversial in parotid tumors. We aimed to compare FNAB results with the final histopathological diagnosis and to apply the "Sal classification" to our data and discuss its results and its place in parotid gland cytology. The FNAB cytological findings and final histological diagnosis were assessed retrospectively in 2 different scenarios based on the distribution of nondefinitive cytology, and we applied the Sal classification and determined malignancy rate, sensitivity, and specificity for each category. In 2 different scenarios FNAB sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were found to be 81%, 87%, 54.7%, and 96.1%; and 65.3%, 100%, 100%, and 96.1%, respectively. The malignancy rates and sensitivity and specificity were also calculated and discussed for each Sal category. We believe that the Sal classification has a great potential to be a useful tool in classification of parotid gland cytology. © 2018 Wiley Periodicals, Inc.

Treatment of congential vascular disorders: classification, step program, and therapeutic procedures

NASA Astrophysics Data System (ADS)

Philipp, Carsten M.; Poetke, Margitta; Engel-Murke, Frank; Waldschmidt, J.; Berlien, Hans-Peter

1994-02-01

Because of the different step programs concerning the preoperative diagnostic and the onset of therapy for the various types of congenital vascular disorders (CVD) a clear classification is important. One has to discern the vascular malformations, including the port wine stain, from the real hemangiomas which are vascular tumors. As former classification, mostly based on histological findings, showed little evidence to a clinical step program, we developed a descriptive classification which allows an early differentiation between the two groups of CVD. In most cases this can be done by a precise medical history of the onset and development of the disorder, a close look to the clinical signs and by Duplex-Ultrasound and MRI-diagnostic. With this protocol and the case adapted use of different lasers and laser techniques we have not seen any severe complications as skin necrosis or nerve lesions.

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.

Imaging in pleural mesothelioma: a review of the 11th International Conference of the International Mesothelioma Interest Group.

PubMed

Armato, Samuel G; Labby, Zacariah E; Coolen, Johan; Klabatsa, Astero; Feigen, Malcolm; Persigehl, Thorsten; Gill, Ritu R

2013-11-01

Imaging of malignant pleural mesothelioma (MPM) is essential to the diagnosis, assessment, and monitoring of this disease. The complex morphology and growth pattern of MPM, however, create unique challenges for image acquisition and interpretation. These challenges have captured the attention of investigators around the world, some of whom presented their work at the 2012 International Conference of the International Mesothelioma Interest Group (iMig 2012) in Boston, Massachusetts, USA, September 2012. The measurement of tumor thickness on computed tomography (CT) scans is the current standard of care in the assessment of MPM tumor response to therapy; in this context, variability among observers in the measurement task and in the tumor response classification categories derived from such measurements was reported. Alternate CT-based tumor response criteria, specifically direct measurement of tumor volume change and change in lung volume as a surrogate for tumor response, were presented. Dynamic contrast-enhanced CT has a role in other settings, but investigation into its potential use for imaging mesothelioma tumor perfusion only recently has been initiated. Magnetic resonance imaging (MRI) and positron-emission tomography (PET) are important imaging modalities in MPM and complement the information provided by CT. The pointillism sign in diffusion-weighted MRI was reported as a potential parameter for the classification of pleural lesions as benign or malignant, and PET parameters that measure tumor activity and functional tumor volume were presented as indicators of patient prognosis. Also reported was the use of PET/CT in the management of patients who undergo high-dose radiation therapy. Imaging for MPM impacts everything from initial patient diagnosis to the outcomes of clinical trials; iMig 2012 captured this broad range of imaging applications as investigators exploit technology and implement multidisciplinary approaches toward the benefit of MPM patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Preoperative assessment of microvascular invasion in hepatocellular carcinoma

NASA Astrophysics Data System (ADS)

Chakraborty, Jayasree; Zheng, Jian; Gönen, Mithat; Jarnagin, William R.; DeMatteo, Ronald P.; Do, Richard K. G.; Simpson, Amber L.

2017-03-01

Hepatocellular carcinoma (HCC) is the most common liver cancer and the third leading cause of cancer-related death worldwide.1 Resection or liver transplantation may be curative in patients with early-stage HCC but early recurrence is common.2, 3 Microvascular invasion (MVI) is one of the most important predictors of early recurrence.3 The identification of MVI prior to surgery would optimally select patients for potentially curative resection or liver transplant. However, MVI can only be diagnosed by microscopic assessment of the resected tumor. The aim of the present study is to apply CT-based texture analysis to identify pre-operative imaging predictors of MVI in patients with HCC. Texture features are derived from CT and analyzed individually as well as in combination, to evaluate their ability to predict MVI. A two-stage classification is employed: HCC tumors are automatically categorized into uniform or heterogenous groups followed by classification into the presence or absence of MVI. We achieve an area under the receiver operating characteristic curve (AUC) of 0.76 and accuracy of 76.7% for uniform lesions and AUC of 0.79 and accuracy of 74.06% for heterogeneous tumors. These results suggest that MVI can be accurately and objectively predicted from preoperative CT scans.

[A revolution postponed indefinitely.WHO classification of tumors of the breast 2012: the main changes compared to the 3rd edition (2003)].

PubMed

Nenutil, Rudolf

2015-01-01

In 2012, the new classification of the fourth series WHO blue books of breast tumors was released. The current version represents a fluent evolution, compared to the third edition. Some limited changes regarding terminology, definitions and the inclusion of some diagnostic units were adopted. The information about the molecular biology and genetic background of breast carcinoma has been enriched substantially.

Apocrine hidradenocarcinoma of the scalp: a classification conundrum.

PubMed

Cohen, Marc; Cassarino, David S; Shih, Hubert B; Abemayor, Elliot; St John, Maie

2009-03-01

The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions.

Apocrine Hidradenocarcinoma of the Scalp: A Classification Conundrum

PubMed Central

Cassarino, David S.; Shih, Hubert B.; Abemayor, Elliot; John, Maie St.

2008-01-01

Introduction The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. Methods A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Results Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Conclusion Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions. PMID:20596988

Detection of canine skin and subcutaneous tumors by visible and near-infrared diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Cugmas, Blaž; Plavec, Tanja; Bregar, Maksimilijan; Naglič, Peter; Pernuš, Franjo; Likar, Boštjan; Bürmen, Miran

2015-03-01

Cancer is the main cause of canine morbidity and mortality. The existing evaluation of tumors requires an experienced veterinarian and usually includes invasive procedures (e.g., fine-needle aspiration) that can be unpleasant for the dog and the owner. We investigate visible and near-infrared diffuse reflectance spectroscopy (DRS) as a noninvasive optical technique for evaluation and detection of canine skin and subcutaneous tumors ex vivo and in vivo. The optical properties of tumors and skin were calculated in a spectrally constrained manner, using a lookup table-based inverse model. The obtained optical properties were analyzed and compared among different tumor groups. The calculated parameters of the absorption and reduced scattering coefficients were subsequently used for detection of malignant skin and subcutaneous tumors. The detection sensitivity and specificity of malignant tumors ex vivo were 90.0% and 73.5%, respectively, while corresponding detection sensitivity and specificity of malignant tumors in vivo were 88.4% and 54.6%, respectively. The obtained results show that the DRS is a promising noninvasive optical technique for detection and classification of malignant and benign canine skin and subcutaneous tumors. The method should be further investigated on tumors with common origin.

Correlation between Standardized Uptake Value of 68Ga-DOTA-NOC Positron Emission Tomography/Computed Tomography and Pathological Classification of Neuroendocrine Tumors.

PubMed

Kaewput, Chalermrat; Suppiah, Subapriya; Vinjamuri, Sobhan

2018-01-01

The aim of our study was to correlate tumor uptake of 68 Ga-DOTA-NOC positron emission tomography/computed tomography (PET/CT) with the pathological grade of neuroendocrine tumors (NETs). 68 Ga-DOTA-NOC PET/CT examinations in 41 patients with histopathologically proven NETs were included in the study. Maximum standardized uptake value (SUV max ) and averaged SUV SUV mean of "main tumor lesions" were calculated for quantitative analyses after background subtraction. Uptake on main tumor lesions was compared and correlated with the tumor histological grade based on Ki-67 index and pathological differentiation. Classification was performed into three grades according to Ki-67 levels; low grade: Ki-67 <2, intermediate grade: Ki-67 3-20, and high grade: Ki-67 >20. Pathological differentiation was graded into well- and poorly differentiated groups. The values were compared and evaluated for correlation and agreement between the two parameters was performed. Our study revealed negatively fair agreement between SUV max of tumor and Ki-67 index ( r = -0.241) and negatively poor agreement between SUV mean of tumor and Ki-67 index ( r = -0.094). SUV max of low-grade, intermediate-grade, and high-grade Ki-67 index is 26.18 ± 14.56, 30.71 ± 24.44, and 6.60 ± 4.59, respectively. Meanwhile, SUV mean of low-grade, intermediate-grade, and high-grade Ki-67 is 8.92 ± 7.15, 9.09 ± 5.18, and 3.00 ± 1.38, respectively. As expected, there was statistically significant decreased SUV max and SUV mean in high-grade tumors (poorly differentiated NETs) as compared with low- and intermediate-grade tumors (well-differentiated NETs). SUV of 68 Ga-DOTA-NOC PET/CT is not correlated with histological grade of NETs. However, there was statistically significant decreased tumor uptake of 68 Ga-DOTA-NOC in poorly differentiated NETs as compared with the well-differentiated group. As a result of this pilot study, we confirm that the lower tumor uptake of 68 Ga-DOTA-NOC may be associated with aggressive behavior and may, therefore, result in poor prognosis.

Skull Base Erosion Resulting From Primary Tumors of the Temporomandibular Joint and Skull Base Region: Our Classification and Reconstruction Experience.

PubMed

Chen, Min-Jie; Yang, Chi; Zheng, Ji-Si; Bai, Guo; Han, Zi-Xiang; Wang, Yi-Wen

2018-06-01

We sought to introduce our classification and reconstruction protocol for skull base erosions in the temporomandibular joint and skull base region. Patients with neoplasms in the temporomandibular joint and skull base region treated from January 2006 to March 2017 were reviewed. Skull base erosion was classified into 3 types according to the size of the defect. We included 33 patients, of whom 5 (15.2%) had type I defects (including 3 in whom free fat grafts were placed and 2 in whom deep temporal fascial fat flaps were placed). There were 8 patients (24.2%) with type II defects, all of whom received deep temporal fascial fat flaps. A total of 20 patients (60.6%) had type III defects, including 17 in whom autogenous bone grafts were placed, 1 in whom titanium mesh was placed, and 2 who received total alloplastic joints. The mean follow-up period was 50 months. All of the patients exhibited stable occlusion and good facial symmetry. No recurrence was noted. Our classification and reconstruction principles allowed reliable morpho-functional skull base reconstruction. Copyright © 2018 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

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.

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.

Finding of IDH1 R132H mutation in histologically non-neoplastic glial tissue changes surgical strategies, a case report.

PubMed

Søndergaard, Christian Baastrup; Scheie, David; Sehested, Astrid Marie; Skjøth-Rasmussen, Jane

2017-07-01

In 2016, the WHO classification of diffuse astrocytoma began to include isocitrate dehydrogenase (IDH) mutation in addition to histology. We here demonstrate a case where a 14-year-old boy presented with a parietal tumor with no histological evidence of neoplasia but with an IDH1 mutation. Due to the IDH1 R132H mutation, the patient was diagnosed with diffuse astrocytoma WHO grade II and underwent successful gross total resection of this near-eloquently located tumor. This case exemplifies how inclusion of immunohistochemistry in tumor classification alters surgical strategy and might improve accuracy and time to diagnosis.

Positron emission tomography/computed tomography with 18F-fluorocholine improve tumor staging and treatment allocation in patients with hepatocellular carcinoma.

PubMed

Chalaye, Julia; Costentin, Charlotte E; Luciani, Alain; Amaddeo, Giuliana; Ganne-Carrié, Nathalie; Baranes, Laurence; Allaire, Manon; Calderaro, Julien; Azoulay, Daniel; Nahon, Pierre; Seror, Olivier; Mallat, Ariane; Soussan, Michael; Duvoux, Christophe; Itti, Emmanuel; Nault, Jean Charles

2018-03-06

Hepatocellular carcinoma (HCC) staging according to the Barcelona Clinical Liver Cancer (BCLC) classification is based on conventional imaging. The aim of our study was to assess the impact of dual-tracer 18F-fluorocholine and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) on tumor staging and treatment allocation. A total of 192 dual-tracer PET/CT scans (18F-fluorocholine and 18F-fluorodeoxyglucose PET/CT) were performed in 177 patients with HCC. BCLC staging and treatment proposal were retrospectively collected based on conventional imaging, along with any new lesions detected, and changes in BCLC classification or treatment allocation based on dual-tracer PET/CT. Patients were primarily men (87.5%) with cirrhosis (71%) due to alcohol ± non-alcoholic steatohepatitis (26%), viral infection (62%) or unknown causes (12%). Among 122 patients with PET/CT performed for staging, BCLC stage based on conventional imaging was 0/A in 61 patients (50%), B in 32 patients (26%) and C in 29 patients (24%). Dual-tracer PET/CT detected new lesions in 26 patients (21%), upgraded BCLC staging in 14 (11%) and modified treatment strategy in 17 (14%). In addition, dual-tracer PET/CT modified the final treatment in 4/9 (44%) patients with unexplained elevation of alpha-fetoprotein (AFP), 10/25 patients (40%) with doubtful lesions on conventional imaging and 3/36 patients (8%) waiting for liver transplantation without active HCC after tumor response following bridging therapy. When used for HCC staging, dual-tracer PET/CT enabled BCLC upgrading and treatment modification in 11% and 14% of patients, respectively. Dual-tracer PET/CT might also be useful in specific situations (an unexplained rise in AFP, doubtful lesions or pre-transplant evaluation of patients without active HCC). Using a combination of tracers 18F-fluorocholine and 18F-fluorodeoxyglucose when performing positron emission tomography/computed tomography (PET/CT), often called a PET scan, helps to identify new tumor lesions in patients with hepatocellular carcinoma. This technique enabled staging modification of patients' tumors and led to changes in treatment allocation in certain patients. Copyright © 2018. Published by Elsevier B.V.

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2014-03-01

As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.

The International Neuroblastoma Risk Group (INRG) staging system: an INRG Task Force report.

PubMed

Monclair, Tom; Brodeur, Garrett M; Ambros, Peter F; Brisse, Hervé J; Cecchetto, Giovanni; Holmes, Keith; Kaneko, Michio; London, Wendy B; Matthay, Katherine K; Nuchtern, Jed G; von Schweinitz, Dietrich; Simon, Thorsten; Cohn, Susan L; Pearson, Andrew D J

2009-01-10

The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Because the International Neuroblastoma Staging System (INSS) is a postsurgical staging system, a new clinical staging system was required for the INRG pretreatment risk classification system. To stage patients before any treatment, the INRG Task Force, consisting of neuroblastoma experts from Australia/New Zealand, China, Europe, Japan, and North America, developed a new INRG staging system (INRGSS) based on clinical criteria and image-defined risk factors (IDRFs). To investigate the impact of IDRFs on outcome, survival analyses were performed on 661 European patients with INSS stages 1, 2, or 3 disease for whom IDRFs were known. In the INGRSS, locoregional tumors are staged L1 or L2 based on the absence or presence of one or more of 20 IDRFs, respectively. Metastatic tumors are defined as stage M, except for stage MS, in which metastases are confined to the skin, liver, and/or bone marrow in children younger than 18 months of age. Within the 661-patient cohort, IDRFs were present (ie, stage L2) in 21% of patients with stage 1, 45% of patients with stage 2, and 94% of patients with stage 3 disease. Patients with INRGSS stage L2 disease had significantly lower 5-year event-free survival than those with INRGSS stage L1 disease (78% +/- 4% v 90% +/- 3%; P = .0010). Use of the new staging (INRGSS) and risk classification (INRG) of neuroblastoma will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world.

Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancer--a classification tree approach.

PubMed

Martin, Michael A; Meyricke, Ramona; O'Neill, Terry; Roberts, Steven

2006-04-20

A critical choice facing breast cancer patients is which surgical treatment--mastectomy or breast conserving surgery (BCS)--is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of "propensity" is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA) data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process. Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression. Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice. Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients.

Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

PubMed

Zhao, Liya; Jia, Kebin

2016-01-01

Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

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

Joint tumor segmentation and dense deformable registration of brain MR images.

PubMed

Parisot, Sarah; Duffau, Hugues; Chemouny, Stéphane; Paragios, Nikos

2012-01-01

In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

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.

Primary meningeal myxoid liposarcoma with aggressive behavior after recurrence: case report.

PubMed

Watanabe, Noriyuki; Ohtani, Haruo; Mori, Shuichi; Iguchi, Masahiro; Zaboronok, Alexander; Sakamoto, Noriaki; Matsuda, Masahide; Ishikawa, Eiichi; Matsumura, Akira

2018-06-19

Although liposarcomas are the most common soft tissue sarcomas, their intracranial variants are extremely rare. Here, we present a case of a primary intracranial myxoid liposarcoma in a 23-year-old Japanese man who presented with generalized seizures and a mass in the left frontal lobe. The tumor was totally removed, and histological analyses pointed to liposarcoma. Thirteen years after his initial treatment, the patient presented with right-side weakness and local recurrence of tumor was discovered. Histology from the second resection confirmed the diagnosis of myxoid liposarcoma. Shortly after the second resection, progressive, new intracranial lesions were observed and despite a third resection, extensive intracerebral invasion by the tumor proved fatal. The histological features of myxoid liposarcoma were essentially similar with each recurrence, but the aggressive tumor behavior after the second operation did not align with expectations based on histological classification.

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

PubMed

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

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.

Heterogeneous activation of the TGFβ pathway in glioblastomas identified by gene expression-based classification using TGFβ-responsive genes

PubMed Central

Xu, Xie L; Kapoun, Ann M

2009-01-01

Background TGFβ has emerged as an attractive target for the therapeutic intervention of glioblastomas. Aberrant TGFβ overproduction in glioblastoma and other high-grade gliomas has been reported, however, to date, none of these reports has systematically examined the components of TGFβ signaling to gain a comprehensive view of TGFβ activation in large cohorts of human glioma patients. Methods TGFβ activation in mammalian cells leads to a transcriptional program that typically affects 5–10% of the genes in the genome. To systematically examine the status of TGFβ activation in high-grade glial tumors, we compiled a gene set of transcriptional response to TGFβ stimulation from tissue culture and in vivo animal studies. These genes were used to examine the status of TGFβ activation in high-grade gliomas including a large cohort of glioblastomas. Unsupervised and supervised classification analysis was performed in two independent, publicly available glioma microarray datasets. Results Unsupervised and supervised classification using the TGFβ-responsive gene list in two independent glial tumor gene expression data sets revealed various levels of TGFβ activation in these tumors. Among glioblastomas, one of the most devastating human cancers, two subgroups were identified that showed distinct TGFβ activation patterns as measured from transcriptional responses. Approximately 62% of glioblastoma samples analyzed showed strong TGFβ activation, while the rest showed a weak TGFβ transcriptional response. Conclusion Our findings suggest heterogeneous TGFβ activation in glioblastomas, which may cause potential differences in responses to anti-TGFβ therapies in these two distinct subgroups of glioblastomas patients. PMID:19192267

Prognostic Relevance of Histomolecular Classification of Diffuse Adult High-Grade Gliomas with Necrosis.

PubMed

Figarella-Branger, Dominique; Mokhtari, Karima; Colin, Carole; Uro-Coste, Emmanuelle; Jouvet, Anne; Dehais, Caroline; Carpentier, Catherine; Villa, Chiara; Maurage, Claude-Alain; Eimer, Sandrine; Polivka, Marc; Vignaud, Jean-Michel; Laquerriere, Annie; Sevestre, Henri; Lechapt-Zalcman, Emmanuelle; Quintin-Roué, Isabelle; Aubriot-Lorton, Marie-Hélène; Diebold, Marie-Danièle; Viennet, Gabriel; Adam, Clovis; Loussouarn, Delphine; Michalak, Sophie; Rigau, Valérie; Heitzmann, Anne; Vandenbos, Fanny; Forest, Fabien; Chiforeanu, Danchristian; Tortel, Marie-Claire; Labrousse, François; Chenard, Marie-Pierre; Nguyen, Anh Tuan; Varlet, Pascale; Kemeny, Jean Louis; Levillain, Pierre-Marie; Cazals-Hatem, Dominique; Richard, Pomone; Delattre, Jean-Yves

2015-07-01

Diffuse adult high-grade gliomas (HGGs) with necrosis encompass anaplastic oligodendrogliomas (AOs) with necrosis (grade III), glioblastomas (GBM, grade IV) and glioblastomas with an oligodendroglial component (GBMO, grade IV). Here, we aimed to search for prognostic relevance of histological classification and molecular alterations of these tumors. About 210 patients were included (63 AO, 56 GBM and 91 GBMO). GBMO group was split into "anaplastic oligoastrocytoma (AOA) with necrosis grade IV/GBMO," restricted to tumors showing intermingled astrocytic and oligodendroglial component, and "GBM/GBMO" based on tumors presenting oligodendroglial foci and features of GBM. Genomic arrays, IDH1 R132H expression analyses and IDH direct sequencing were performed. 1p/19q co-deletion characterized AO, whereas no IDH1 R132H expression and intact 1p/19q characterized both GBM and GBM/GBMO. AOA with necrosis/GBMO mainly demonstrated IDH1 R132H expression and intact 1p/19q. Other IDH1 or IDH2 mutations were extremely rare. Both histological and molecular classifications were predictive of progression free survival (PFS) and overall survival (OS) (P < 10(-4) ). Diffuse adult HGGs with necrosis can be split into three histomolecular groups of prognostic relevance: 1p/19q co-deleted AO, IDH1 R132H-GBM and 1p/19q intact IDH1 R132H+ gliomas that might be classified as IDH1 R132H+ GBM. Because of histomolecular heterogeneity, we suggest to remove the name GBMO. © 2014 International Society of Neuropathology.

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.

A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

NASA Astrophysics Data System (ADS)

Yu, Ning; Wu, Jia; Weinstein, Susan P.; Gaonkar, Bilwaj; Keller, Brad M.; Ashraf, Ahmed B.; Jiang, YunQing; Davatzikos, Christos; Conant, Emily F.; Kontos, Despina

2015-03-01

Accurate and efficient automated tumor segmentation in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is highly desirable for computer-aided tumor diagnosis. We propose a novel automatic segmentation framework which incorporates mean-shift smoothing, superpixel-wise classification, pixel-wise graph-cuts partitioning, and morphological refinement. A set of 15 breast DCE-MR images, obtained from the American College of Radiology Imaging Network (ACRIN) 6657 I-SPY trial, were manually segmented to generate tumor masks (as ground truth) and breast masks (as regions of interest). Four state-of-the-art segmentation approaches based on diverse models were also utilized for comparison. Based on five standard evaluation metrics for segmentation, the proposed framework consistently outperformed all other approaches. The performance of the proposed framework was: 1) 0.83 for Dice similarity coefficient, 2) 0.96 for pixel-wise accuracy, 3) 0.72 for VOC score, 4) 0.79 mm for mean absolute difference, and 5) 11.71 mm for maximum Hausdorff distance, which surpassed the second best method (i.e., adaptive geodesic transformation), a semi-automatic algorithm depending on precise initialization. Our results suggest promising potential applications of our segmentation framework in assisting analysis of breast carcinomas.

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.

A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images.

PubMed

Khan, Adnan Mujahid; Sirinukunwattana, Korsuk; Rajpoot, Nasir

2015-09-01

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.

Texture-Based Analysis of 100 MR Examinations of Head and Neck Tumors - Is It Possible to Discriminate Between Benign and Malignant Masses in a Multicenter Trial?

PubMed

Fruehwald-Pallamar, J; Hesselink, J R; Mafee, M F; Holzer-Fruehwald, L; Czerny, C; Mayerhoefer, M E

2016-02-01

To evaluate whether texture-based analysis of standard MRI sequences can help in the discrimination between benign and malignant head and neck tumors. The MR images of 100 patients with a histologically clarified head or neck mass, from two different institutions, were analyzed. Texture-based analysis was performed using texture analysis software, with region of interest measurements for 2 D and 3 D evaluation independently for all axial sequences. COC, RUN, GRA, ARM, and WAV features were calculated for all ROIs. 10 texture feature subsets were used for a linear discriminant analysis, in combination with k-nearest-neighbor classification. Benign and malignant tumors were compared with regard to texture-based values. There were differences in the images from different field-strength scanners, as well as from different vendors. For the differentiation of benign and malignant tumors, we found differences on STIR and T2-weighted images for 2 D, and on contrast-enhanced T1-TSE with fat saturation for 3 D evaluation. In a separate analysis of the subgroups 1.5 and 3 Tesla, more discriminating features were found. Texture-based analysis is a useful tool in the discrimination of benign and malignant tumors when performed on one scanner with the same protocol. We cannot recommend this technique for the use of multicenter studies with clinical data. 2 D/3 D texture-based analysis can be performed in head and neck tumors. Texture-based analysis can differentiate between benign and malignant masses. Analyzed MR images should originate from one scanner with an identical protocol. © Georg Thieme Verlag KG Stuttgart · New York.

Brain Tumor Epidemiology: Consensus from the Brain Tumor Epidemiology Consortium (BTEC)

PubMed Central

Bondy, Melissa L.; Scheurer, Michael E.; Malmer, Beatrice; Barnholtz-Sloan, Jill S.; Davis, Faith G.; Il’yasova, Dora; Kruchko, Carol; McCarthy, Bridget J.; Rajaraman, Preetha; Schwartzbaum, Judith A.; Sadetzki, Siegal; Schlehofer, Brigitte; Tihan, Tarik; Wiemels, Joseph L.; Wrensch, Margaret; Buffler, Patricia A.

2010-01-01

Epidemiologists in the Brain Tumor Epidemiology Consortium (BTEC) have prioritized areas for further research. Although many risk factors have been examined over the past several decades, there are few consistent findings possibly due to small sample sizes in individual studies and differences between studies in subjects, tumor types, and methods of classification. Individual studies have generally lacked sufficient sample size to examine interactions. A major priority based on available evidence and technologies includes expanding research in genetics and molecular epidemiology of brain tumors. BTEC has taken an active role in promoting understudied groups such as pediatric brain tumors, the etiology of rare glioma subtypes, such as oligodendroglioma, and meningioma, which not uncommon, has only recently been systematically registered in the US. There is also a pressing need to bring more researchers, especially junior investigators, to study brain tumor epidemiology. However, relatively poor funding for brain tumor research has made it difficult to encourage careers in this area. We review the group’s consensus on the current state of scientific findings and present a consensus on research priorities to identify the important areas the science should move to address. PMID:18798534

Machine Learning methods for Quantitative Radiomic Biomarkers.

PubMed

Parmar, Chintan; Grossmann, Patrick; Bussink, Johan; Lambin, Philippe; Aerts, Hugo J W L

2015-08-17

Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

NASA Astrophysics Data System (ADS)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

[The pathology of salivary glands. Tumors of the salivary glands].

PubMed

Mahy, P; Reychler, H

2006-01-01

The management of benign and malignant neoplasms of the salivary glands requires precise knowledge of tumor histogenesis and classification as well as surgical skills. Pleomorphic adenoma and Whartin's tumor are the most frequent tumors in parotid glands while the probability for malignant tumors is higher in other glands, especially in sublingual and minor salivary glands. Those malignant salivary glands tumors are rare and necessitate multidisciplinar staging and management in close collaboration with the pathologist and the radiation oncologist.

Binary Classification using Decision Tree based Genetic Programming and Its Application to Analysis of Bio-mass Data

NASA Astrophysics Data System (ADS)

To, Cuong; Pham, Tuan D.

2010-01-01

In machine learning, pattern recognition may be the most popular task. "Similar" patterns identification is also very important in biology because first, it is useful for prediction of patterns associated with disease, for example cancer tissue (normal or tumor); second, similarity or dissimilarity of the kinetic patterns is used to identify coordinately controlled genes or proteins involved in the same regulatory process. Third, similar genes (proteins) share similar functions. In this paper, we present an algorithm which uses genetic programming to create decision tree for binary classification problem. The application of the algorithm was implemented on five real biological databases. Base on the results of comparisons with well-known methods, we see that the algorithm is outstanding in most of cases.

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

Ruan, D; Shao, W; Low, D

Purpose: To evaluate and test the hypothesis that plan quality may be systematically affected by treatment delivery techniques and target-tocritical structure geometric relationship in radiotherapy for brain tumor. Methods: Thirty-four consecutive brain tumor patients treated between 2011–2014 were analyzed. Among this cohort, 10 were planned with 3DCRT, 11 with RadipArc, and 13 with helical IMRT on TomoTherapy. The selected dosimetric endpoints (i.e., PTV V100, maximum brainstem/chiasm/ optic nerve doses) were considered as a vector in a highdimensional space. A Pareto analysis was performed to identify the subset of Pareto-efficient plans.The geometric relationships, specifically the overlapping volume and centroid-of-mass distance betweenmore » each critical structure to the PTV were extracted as potential geometric features. The classification-tree analyses were repeated using these geometric features with and without the treatment modality as an additional categorical predictor. In both scenarios, the dominant features to prognosticate the Pareto membership were identified and the tree structures to provide optimal inference were recorded. The classification performance was further analyzed to determine the role of treatment modality in affecting plan quality. Results: Seven Pareto-efficient plans were identified based on dosimetric endpoints (3 from 3DCRT, 3 from RapicArc, 1 from Tomo), which implies that the evaluated treatment modality may have a minor influence on plan quality. Classification trees with/without the treatment modality as a predictor both achieved accuracy of 88.2%: with 100% sensitivity and 87.1% specificity for the former, and 66.7% sensitivity and 96.0% specificity for the latter. The coincidence of accuracy from both analyses further indicates no-to-weak dependence of plan quality on treatment modality. Both analyses have identified the brainstem to PTV distance as the primary predictive feature for Pareto-efficiency. Conclusion: Pareto evaluation and classification-tree analyses have indicated that plan quality depends strongly on geometry for brain tumor, specifically PTV-tobrain-stem-distance but minimally on treatment modality.« less

Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform.

PubMed

Barbosa, Daniel J C; Ramos, Jaime; Lima, Carlos S

2008-01-01

Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.

21 CFR 886.1670 - Ophthalmic isotope uptake probe.

Code of Federal Regulations, 2010 CFR

2010-04-01

..., by a probe which is placed in close proximity to the eye, the uptake of a radioisotope (phosphorus 32) by tumors to detect tumor masses on, around, or within the eye. (b) Classification. Class II. ...

21 CFR 886.1670 - Ophthalmic isotope uptake probe.

Code of Federal Regulations, 2011 CFR

2011-04-01

..., by a probe which is placed in close proximity to the eye, the uptake of a radioisotope (phosphorus 32) by tumors to detect tumor masses on, around, or within the eye. (b) Classification. Class II. ...

A classification tree for the prediction of benign versus malignant disease in patients with small renal masses.

PubMed

Rendon, Ricardo A; Mason, Ross J; Kirkland, Susan; Lawen, Joseph G; Abdolell, Mohamed

2014-08-01

To develop a classification tree for the preoperative prediction of benign versus malignant disease in patients with small renal masses. This is a retrospective study including 395 consecutive patients who underwent surgical treatment for a renal mass < 5 cm in maximum diameter between July 1st 2001 and June 30th 2010. A classification tree to predict the risk of having a benign renal mass preoperatively was developed using recursive partitioning analysis for repeated measures outcomes. Age, sex, volume on preoperative imaging, tumor location (central/peripheral), degree of endophytic component (1%-100%), and tumor axis position were used as potential predictors to develop the model. Forty-five patients (11.4%) were found to have a benign mass postoperatively. A classification tree has been developed which can predict the risk of benign disease with an accuracy of 88.9% (95% CI: 85.3 to 91.8). The significant prognostic factors in the classification tree are tumor volume, degree of endophytic component and symptoms at diagnosis. As an example of its utilization, a renal mass with a volume of < 5.67 cm3 that is < 45% endophytic has a 52.6% chance of having benign pathology. Conversely, a renal mass with a volume ≥ 5.67 cm3 that is ≥ 35% endophytic has only a 5.3% possibility of being benign. A classification tree to predict the risk of benign disease in small renal masses has been developed to aid the clinician when deciding on treatment strategies for small renal masses.

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.

The International Neuroblastoma Risk Group (INRG) Staging System: An INRG Task Force Report

PubMed Central

Monclair, Tom; Brodeur, Garrett M.; Ambros, Peter F.; Brisse, Hervé J.; Cecchetto, Giovanni; Holmes, Keith; Kaneko, Michio; London, Wendy B.; Matthay, Katherine K.; Nuchtern, Jed G.; von Schweinitz, Dietrich; Simon, Thorsten; Cohn, Susan L.; Pearson, Andrew D.J.

2009-01-01

Purpose The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Because the International Neuroblastoma Staging System (INSS) is a postsurgical staging system, a new clinical staging system was required for the INRG pretreatment risk classification system. Methods To stage patients before any treatment, the INRG Task Force, consisting of neuroblastoma experts from Australia/New Zealand, China, Europe, Japan, and North America, developed a new INRG staging system (INRGSS) based on clinical criteria and image-defined risk factors (IDRFs). To investigate the impact of IDRFs on outcome, survival analyses were performed on 661 European patients with INSS stages 1, 2, or 3 disease for whom IDRFs were known. Results In the INGRSS, locoregional tumors are staged L1 or L2 based on the absence or presence of one or more of 20 IDRFs, respectively. Metastatic tumors are defined as stage M, except for stage MS, in which metastases are confined to the skin, liver, and/or bone marrow in children younger than 18 months of age. Within the 661-patient cohort, IDRFs were present (ie, stage L2) in 21% of patients with stage 1, 45% of patients with stage 2, and 94% of patients with stage 3 disease. Patients with INRGSS stage L2 disease had significantly lower 5-year event-free survival than those with INRGSS stage L1 disease (78% ± 4% v 90% ± 3%; P = .0010). Conclusion Use of the new staging (INRGSS) and risk classification (INRG) of neuroblastoma will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world. PMID:19047290

Evaluation of the 7(th) edition of the UICC-AJCC tumor, node, metastasis classification for esophageal cancer in a Chinese cohort.

PubMed

Huang, Yan; Guo, Weigang; Shi, Shiming; He, Jian

2016-07-01

To assess and evaluate the prognostic value of the 7(th) edition of the Union for International Cancer Control-American Joint Committee on Cancer (UICC-AJCC) tumor, node, metastasis (TNM) staging system for Chinese patients with esophageal cancer in comparison with the 6(th) edition. A retrospective review was performed on 766 consecutive esophageal cancer patients treated with esophagectomy between 2008 and 2012. Patients were staged according to the 6(th) and 7(th) editions for esophageal cancer respectively. Survival was calculated by the Kaplan-Meier method, and multivariate analysis was performed using Cox regression model. Overall 3-year survival rate was 59.5%. There were significant differences in 3-year survival rates among T stages both according to the 6(th) edition and the 7(th) edition (P<0.001). According to the 7(th) edition, the 3-year survival rates of N0 (75.4%), N1 (65.2%), N2 (39.7%) and N3 (27.3%) patients were significant differences (P<0.001). Kaplan-Meier curve revealed a good discriminatory ability from stage I to IV, except for stage IB, IIA and IIB in the 7(th) edition staging system. Based on the 7(th) edition, the degree of differentiation, tumor length and tumor location were not independent prognostic factors on multivariate analysis. The multivariate analyses suggested that pT-, pN-, pTNM-category were all the independent prognostic factors based on the 6(th) and 7(th) edition staging system. The 7(th) edition of AJCC TNM staging system of esophageal cancer should discriminate pT2-3N0M0 (stage IB, IIA and IIB) better when considering the esophageal squamous cell cancer patients. Therefore, to improve and optimize the AJCC TNM classification for Chinese patients with esophageal cancer, more considerations about the value of tumor grade and tumor location in pT2-3N0M0 esophageal squamous cell cancer should be taken in the next new TNM staging system.

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

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.

Comparison between endoscopic macroscopic classification and F-18 FDG PET findings in gastric mucosa-associated lymphoid tissue lymphoma patients.

PubMed

Hirose, Yasumitsu; Kaida, Hayato; Ishibashi, Masatoshi; Uozumi, Jun; Arikawa, Shunji; Kurata, Seiji; Hayabuchi, Naofumi; Nakahara, Keita; Ohshima, Koichi

2012-02-01

The aim of this study was to compare endoscopic macroscopic classification with fluorine-18 fluorodeoxyglucose (F-18 FDG) uptake in gastric mucosa-associated lymphoid tissue (MALT) lymphoma and to investigate the usefulness of F-18 FDG positron emission tomography (PET) for diagnosing gastric MALT lymphoma. Sixteen patients with gastric MALT lymphoma who underwent F-18 FDG PET and gastrointestinal imaging modalities were included in this study. Sixteen healthy asymptomatic participants undergoing both F-18 FDG PET and endoscopy for cancer screening were in the control group. We investigated the difference of F-18 FDG uptake between the gastric MALT lymphoma and the control group and compared the uptake pattern in gastric MALT lymphoma with our macroscopic classification. The endoscopic findings of 16 gastric MALT lymphoma patients were classified macroscopically as chronic gastritis-like tumors (n = 6), depressed tumors (n = 5), and protruding tumors (n = 5). Abnormal gastric F-18 FDG uptake was observed in 63% of tumors in the gastric MALT lymphoma group and 50% of cases in the control group. The median maximum standardized uptake values for gastric MALT lymphoma patients and control group were 4.0 and 2.6, respectively, the difference of which was statistically significant (P = 0.003). F-18 FDG uptake results were positive for all protruding tumors but only 50% for chronic gastritis-like tumors and 40% for depressed-type tumors. F-18 FDG PET may be a useful method for evaluating protrusion-type gastric MALT lymphoma. When strong focal or diffuse F-18 FDG uptake is detected in the stomach, endoscopic biopsy should be performed, even if the endoscopic finding is chronic gastritis.

Integrated genomic classification of melanocytic tumors of the central nervous system using mutation analysis, copy number alterations and DNA methylation profiling.

PubMed

Griewank, Klaus; Koelsche, Christian; van de Nes, Johannes A P; Schrimpf, Daniel; Gessi, Marco; Möller, Inga; Sucker, Antje; Scolyer, Richard A; Buckland, Michael E; Murali, Rajmohan; Pietsch, Torsten; von Deimling, Andreas; Schadendorf, Dirk

2018-06-11

In the central nervous system, distinguishing primary leptomeningeal melanocytic tumors from melanoma metastases and predicting their biological behavior solely using histopathologic criteria can be challenging. We aimed to assess the diagnostic and prognostic value of integrated molecular analysis. Targeted next-generation-sequencing, array-based genome-wide methylation analysis and BAP1 immunohistochemistry was performed on the largest cohort of central nervous system melanocytic tumors analyzed to date, incl. 47 primary tumors of the central nervous system, 16 uveal melanomas. 13 cutaneous melanoma metastasis and 2 blue nevus-like melanomas. Gene mutation, DNA-methylation and copy-number profiles were correlated with clinicopathological features. Combining mutation, copy-number and DNA-methylation profiles clearly distinguished cutaneous melanoma metastases from other melanocytic tumors. Primary leptomeningeal melanocytic tumors, uveal melanomas and blue nevus-like melanoma showed common DNA-methylation, copy-number alteration and gene mutation signatures. Notably, tumors demonstrating chromosome 3 monosomy and BAP1 alterations formed a homogeneous subset within this group. Integrated molecular profiling aids in distinguishing primary from metastatic melanocytic tumors of the central nervous system. Primary leptomeningeal melanocytic tumors, uveal melanoma and blue nevus-like melanoma share molecular similarity with chromosome 3 and BAP1 alterations markers of poor prognosis. Copyright ©2018, American Association for Cancer Research.

New pathologic entities in penile carcinomas: an update of the 2004 world health organization classification.

PubMed

Chaux, Alcides; Velazquez, Elsa F; Barreto, José E; Ayala, Enrique; Cubilla, Antonio L

2012-05-01

Most primary malignant tumors of the penis are squamous cell carcinomas (SCC) of the usual type. In recent years several variants, each with distinctive clinicopathologic features, have been described. Pseudohyperplastic carcinoma and carcinoma cuniculatum are both low-grade, extremely well-differentiated SCC variants characterized by an indolent clinical course and good prognosis. The former, which may be confused with pseudoepitheliomatous hyperplasia, preferentially affects the inner foreskin mucosa of elderly men and the latter is a verruciform tumor with an endophytic, burrow-like pattern of growth. Pseudoglandular carcinoma (featuring solid tumor nests with extensive central acantholysis simulating glandular lumina) and clear cell carcinoma (human papillomavirus [HPV]-related tumors composed of periodic acid-Schiff positive clear cells) are aggressive tumors with a high incidence of inguinal nodal metastases. Papillary carcinomas are HPV-unrelated verruciform tumors composed of complex papillae with acanthosis, hyper- and parakeratosis, absence of koilocytes, irregular fibrovascular cores, and jagged tumor base. Finally, in warty-basaloid carcinomas areas of warty (condylomatous) and basaloid carcinomas coexist in the same tumor, either separated or intermingled, giving the tumor a variegated appearance. In this review special emphasis is given to the differential diagnosis of these special variants with a discussion of the possible implications for clinical management.

Medulloblastoma: Tumor Biology and Relevance to Treatment and Prognosis Paradigm.

PubMed

Coluccia, Daniel; Figuereido, Carlyn; Isik, Semra; Smith, Christian; Rutka, James T

2016-05-01

Medulloblastoma is a malignant embryonic brain tumor arising in the posterior fossa and typically occurring in pediatric patients. Current multimodal treatment regimes have significantly improved the survival rates; however, a marked heterogeneity in therapy response is observed, and one third of all patients die within 5 years after diagnosis. Large-scale genetic and transcriptome analysis revealed four medulloblastoma subgroups (WNT, SHH, Group 3, and Group 4) associated with different demographic parameters, tumor manifestation, and clinical behavior. Future treatment protocols will integrate molecular classification schemes to evaluate subgroup-specific intensification or de-escalation of adjuvant therapies aimed to increase tumor control and reduce iatrogenic induced morbidity. Furthermore, the identification of genetic drivers allows assessing target therapies in order to increase the chemotherapeutic armamentarium. This review highlights the biology behind the current classification system and elucidates relevant aspects of the disease influencing forthcoming clinical trials.

The Tumor Suppressor Actions of the Vitamin D Receptor in Skin

DTIC Science & Technology

2014-10-01

induced tumor formation. In previous studies we determined that the hedgehog (HH) and wnt/β-catenin pathways were activated in the skin of VDR null...SUBJECT TERMS epidermal tumors, keratinocytes, vitamin D receptor, sonic hedgehog , β-catenin, UVB 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...epidermal tumor formation by blocking the β-catenin and hedgehog pathways, key pathways in keratinocyte proliferation that if left unchecked lead to

Prognostic value of DNA repair based stratification of hepatocellular carcinoma

PubMed Central

Lin, Zhuo; Xu, Shi-Hao; Wang, Hai-Qing; Cai, Yi-Jing; Ying, Li; Song, Mei; Wang, Yu-Qun; Du, Shan-Jie; Shi, Ke-Qing; Zhou, Meng-Tao

2016-01-01

Aberrant activation of DNA repair is frequently associated with tumor progression and response to therapy in hepatocellular carcinoma (HCC). Bioinformatics analyses of HCC data in the Cancer Genome Atlas (TCGA) were performed to define DNA repair based molecular classification that could predict the prognosis of patients with HCC. Furthermore, we tested its predictive performance in 120 independent cases. Four molecular subgroups were identified on the basis of coordinate DNA repair cluster (CDRC) comprising 15 genes in TCGA dataset. Increasing expression of CDRC genes were significantly associated with TP53 mutation. High CDRC was significantly correlated with advanced tumor grades, advanced pathological stage and increased vascular invasion rate. Multivariate Cox regression analysis indicated that the molecular subgrouping was an independent prognostic parameter for both overall survival (p = 0.004, hazard ratio (HR): 2.989) and tumor-free survival (p = 0.049, HR: 3.366) in TCGA dataset. Similar results were also obtained by analyzing the independent cohort. These data suggest that distinct dysregulation of DNA repair constituents based molecular classes in HCC would be useful for predicting prognosis and designing clinical trials for targeted therapy. PMID:27174663

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.

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 role of amino acid PET in the light of the new WHO classification 2016 for brain tumors.

PubMed

Suchorska, Bogdana; Albert, Nathalie L; Bauer, Elena K; Tonn, Jörg-Christian; Galldiks, Norbert

2018-04-26

Since its introduction in 2016, the revision of the World Health Organization (WHO) classification of central nervous system tumours has already changed the diagnostic and therapeutic approach in glial tumors. Blurring the lines between entities formerly labelled as "high-grade" or "low-grade", molecular markers define distinct biological subtypes with different clinical course. This new classification raises the demand for non-invasive imaging methods focussing on depicting metabolic processes. We performed a review of current literature on the use of amino acid PET (AA-PET) for obtaining diagnostic or prognostic information on glioma in the setting of the current WHO 2016 classification. So far, only a few studies have focussed on combining molecular genetic information and metabolic imaging using AA-PET. The current review summarizes the information available on "molecular grading" as well as prognostic information obtained from AA-PET and delivers an insight into a possible interrelation between metabolic imaging and glioma genetics. Within the framework of molecular characterization of gliomas, metabolic imaging using AA-PET is a promising tool for non-invasive characterisation of molecular features and to provide additional prognostic information. Further studies incorporating molecular and metabolic features are necessary to improve the explanatory power of AA-PET in glial tumors.

Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

NASA Astrophysics Data System (ADS)

Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

2017-03-01

Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics.

PubMed

van den Bent, Martin J; Weller, Michael; Wen, Patrick Y; Kros, Johan M; Aldape, Ken; Chang, Susan

2017-05-01

The 2007 World Health Organization (WHO) classification of brain tumors did not use molecular abnormalities as diagnostic criteria. Studies have shown that genotyping allows a better prognostic classification of diffuse glioma with improved treatment selection. This has resulted in a major revision of the WHO classification, which is now for adult diffuse glioma centered around isocitrate dehydrogenase (IDH) and 1p/19q diagnostics. This revised classification is reviewed with a focus on adult brain tumors, and includes a recommendation of genes of which routine testing is clinically useful. Apart from assessment of IDH mutational status including sequencing of R132H-immunohistochemistry negative cases and testing for 1p/19q, several other markers can be considered for routine testing, including assessment of copy number alterations of chromosome 7 and 10 and of TERT promoter, BRAF, and H3F3A mutations. For "glioblastoma, IDH mutated" the term "astrocytoma grade IV" could be considered. It should be considered to treat IDH wild-type grades II and III diffuse glioma with polysomy of chromosome 7 and loss of 10q as glioblastoma. New developments must be more quickly translated into further revised diagnostic categories. Quality control and rapid integration of molecular findings into the final diagnosis and the communication of the final diagnosis to clinicians require systematic attention. © The Author(s) 2017. 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.

Microthymoma in elderly-onset myasthenia gravis detected preoperatively.

PubMed

Hino, Haruaki; Nishimura, Takashi; Seki, Atsuko; Nitadori, Jun-Ichi; Arai, Tomio; Nakajima, Jun

2016-10-01

A 77-year-old woman with a 3-month history of muscle weakness was diagnosed with elderly-onset generalized myasthenia gravis (Myasthenia Gravis Foundation of America classification IIa) based on a high serum acetylcholine receptor antibody level (25.4 nmol·L -1 ) and neurological findings. Computed tomography detected a small nodule (diameter 15 mm) in the anterior mediastinum, which was suspected to be a thymoma. An extended thymectomy was performed. The pathological examination revealed a 6-mm-diameter thymoma, termed a microthymoma, accompanied with a unilocular thymic cyst without capsule formation (type B2 according to the World Health Organization classification). Some fat tissue was also found within the tumor. © The Author(s) 2016.

Confidence-based ensemble for GBM brain tumor segmentation

NASA Astrophysics Data System (ADS)

Huo, Jing; van Rikxoort, Eva M.; Okada, Kazunori; Kim, Hyun J.; Pope, Whitney; Goldin, Jonathan; Brown, Matthew

2011-03-01

It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.

[Multiparameter magnetic resonance imaging in the diagnosis of cancer of the cervix uteri].

PubMed

Tarachkova, E V; Strel'tsova, O N; Panov, V O; Bazaeva, I Ya; Tyurin, I E

2015-01-01

Cancer of the cervix uteri (CCU) ranks third in the incidence of malignancies in women. The choice of CCU treatment mainly depends on the extent of the tumor process, i.e., the stage of the disease. Determining the stage of CCU is based on the clinical classification of the International Federation of Gynecology and Obstetrics (FIGO) (2009) and has a number of substantial limitations in evaluating parametrial invasion, tumor spread to the pelvic wall, and involvement of regional lymph nodes and in determining the true tumor sizes. Magnetic resonance imaging (MRI) is now the method of choice in staging invasive CCU. Multiparameter MRI will be able to enhance the efficiency of diagnosing microinvasive CCU as well (FIGO 2009), to plan surgical and/or chemoradiation treatment, to evaluate its efficiency, and to diagnose locally recurrent CCU.

Molecular classifications of breast carcinoma with similar terminology and different definitions: are they the same?

PubMed

Tang, Ping; Wang, Jianmin; Bourne, Patria

2008-04-01

There are 4 major molecular classifications in the literature that divide breast carcinoma into basal and nonbasal subtypes, with basal subtypes associated with poor prognosis. Basal subtype is defined as positive for cytokeratin (CK) 5/6, CK14, and/or CK17 in CK classification; negative for ER, PR, and HER2 in triple negative (TN) classification; negative for ER and negative or positive for HER2 in ER/HER2 classification; and positive for CK5/6, CK14, CK17, and/or EGFR; and negative for ER, PR, and HER2 in CK/TN classification. These classifications use similar terminology but different definitions; it is critical to understand the precise relationship between them. We compared these 4 classifications in 195 breast carcinomas and found that (1) the rates of basal subtypes varied from 5% to 36% for ductal carcinoma in situ and 14% to 40% for invasive ductal carcinoma. (2) The rates of basal subtypes varied from 19% to 76% for HG carcinoma and 1% to 7% for NHG carcinoma. (3) The rates of basal subtypes were strongly associated with tumor grades (P < .001) in all classifications and associated with tumor types (in situ versus invasive ductal carcinomas) in TN (P < .001) and CK/TN classifications (P = .035). (4) These classifications were related but not interchangeable (kappa ranges from 0.140 to 0.658 for HG carcinoma and from 0.098 to 0.654 for NHG carcinoma). In conclusion, although these classifications all divide breast carcinoma into basal and nonbasal subtypes, they are not interchangeable. More studies are needed to evaluate to their values in predicting prognosis and guiding individualized therapy.

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications.

PubMed

Malta, Tathiane M; de Souza, Camila F; Sabedot, Thais S; Silva, Tiago C; Mosella, Maritza S; Kalkanis, Steven N; Snyder, James; Castro, Ana Valeria B; Noushmehr, Houtan

2018-04-09

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.

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).

Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Qin, Xulei; Chen, Zhuo Georgia; Fei, Baowei

2014-10-01

Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

Correlation between response to neoadjuvant chemotherapy and survival in locally advanced breast cancer patients.

PubMed

Romero, A; García-Sáenz, J A; Fuentes-Ferrer, M; López Garcia-Asenjo, J A; Furió, V; Román, J M; Moreno, A; de la Hoya, M; Díaz-Rubio, E; Martín, M; Caldés, T

2013-03-01

Measurement of residual disease following neoadjuvant chemotherapy that accurately predicts long-term survival in locally advanced breast cancer (LABC) is an essential requirement for clinical trials development. Several methods to assess tumor response have been described. However, the agreement between methods and correlation with survival in independent cohorts has not been reported. We report survival and tumor response according to the measurement of residual breast cancer burden (RCB), the Miller and Payne classification and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, in 151 LABC patients. Kappa Cohen's coefficient (К) was used to test the agreement between methods. We assessed the correlation between the treatment outcome and overall survival (OS) and relapse-free survival (RFS) by calculating Harrell's C-statistic (c). The agreement between Miller and Payne classification and RCB classes was very high (К = 0.82). In contrast, we found a moderate-to-fair agreement between the Miller and Payne classification and RECIST criteria (К = 0.52) and RCB classes and RECIST criteria (К = 0.38). The adjusted C-statistic to predict OS for RCB index (0.77) and RCB classes (0.75) was superior to that of RECIST criteria (0.69) (P = 0.007 and P = 0.035, respectively). Also, RCB index (c = 0.71), RCB classes (c = 0.71) and Miller and Payne classification (c = 0.67) predicted better RFS than RECIST criteria (c = 0.61) (P = 0.005, P = 0.006 and P = 0.028, respectively). The pathological assessment of tumor response might provide stronger prognostic information in LABC patients.

Impact of Strain Elastography on BI-RADS classification in small invasive lobular carcinoma.

PubMed

Chiorean, Angelica Rita; Szep, Mădălina Brîndușa; Feier, Diana Sorina; Duma, Magdalena; Chiorean, Marco Andrei; Strilciuc, Ștefan

2018-05-02

The purpose of this study was to determine the impact of strain elastography (SE) on the Breast Imaging Reporting Data System (BI-RADS) classification depending on invasive lobular carcinoma (ILC) lesion size. We performed a retrospective analysis on a sample of 152 female subjects examined between January 2010 - January 2017. SE was performed on all patients and ILC was subsequently diagnosed by surgical or ultrasound-guided biopsy. BI-RADS 1, 2, 6 and Tsukuba BGR cases were omitted. BI-RADS scores were recorded before and after the use of SE. The differences between scores were compared to the ILC tumor size using nonparametric tests and logistic binary regression. We controlled for age, focality, clinical assessment, heredo-collateral antecedents, B-mode and Doppler ultrasound examination. An ROC curve was used to identify the optimal cut-off point for size in relationship to BI-RADS classificationdifference using Youden's index. The histological subtypes of ILC lesions (n=180) included in the sample were luminal A (70%, n=126), luminal B (27.78%, n=50), triple negative (1.67%, n=3) and HER2+ (0.56%, n=1). The BI-RADS classification was higher when SE was performed (Z=- 6.629, p<0.000). The ROC curve identified a cut-off point of 13 mm for size in relationship to BI-RADS classification difference (J=0.670, p<0.000). Small ILC tumors were 17.92% more likely to influence BI-RADS classification (p<0.000). SE offers enhanced BI-RADS classification in small ILC tumors (<13 mm). Sonoelastography brings added value to B-mode breast ultrasound as an adjacent to mammography in breast cancer screening.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

PubMed

Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

2017-07-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

[Meta-analysis of risk factors of recurrence in patients with giant cell tumor on extremities].

PubMed

Li, Rongrui; Hu, Yongcheng

2014-12-23

To explore the risk factors of giant cell tumor on extremities for patients with postoperative recurrence. The literature reports published before June 2014 were searched in the electronic databases of CBM, CNKI, PUBNED, MEDLINE and EMBASE. Meta-analysis was performed by software Review Manager (Version 5.3). The odds ratios (OR) of gender, age, tumor site, Campanacci Classification, pathological fracture, selection of treatment and soft tissue invasion were analyzed with heterogeneity test. Publication bias were tested by funnel plot and fail-safe number.Sensitivity analysis was performed to assess the stability. A total of 15 case-control studies were identified. Age, location and type of surgery were associated with tumor recurrence. The combined OR (95%CI) was 1.83 (1.04-3.24) P = 0.04 for aged <20 years, 0.52(0.31-0.86) P = 0.01 for aged >40 years, 1.60 (1.06-2.42) P = 0.02 for distal radius, 0.35 (0.14-0.90) P = 0.03 for proximal humerus, 3.64 (1.88-7.04) P = 0.0001 for curettage,0.56 (0.35-0.91) P = 0.02 for curettage with PMMA, 1.79 (1.11-2.88) P = 0.02 for curettage with bone graft and adjuvant and 0.29 (0.12-0.66) P = 0.003 for resection respectively. There were not significant relationship between tumor recurrence and gender, tumor location (distal femur, proximal femur, distal tibia, proximal tibia), Jaffe staging, Campanacci classification,Enneking classification, pathological fracture, soft tissue invasion, extensive curettage, curettage with bone graft, curettage with polymethylmethacrylate and adjuvant (P > 0.05). Youth (aged <20 years), distal radius, curettage and curettage with bone graft and adjuvant are the risk factors for recurrence of giant cell tumor.However, advanced age (aged >40 years), proximal tibia, curettage with PMMA and resection appear to have lower risks for tumor recurrence.

Tumor Heterogeneity in Breast Cancer

PubMed Central

Turashvili, Gulisa; Brogi, Edi

2017-01-01

Breast cancer is a heterogeneous disease and differs greatly among different patients (intertumor heterogeneity) and even within each individual tumor (intratumor heterogeneity). Clinical and morphologic intertumor heterogeneity is reflected by staging systems and histopathologic classification of breast cancer. Heterogeneity in the expression of established prognostic and predictive biomarkers, hormone receptors, and human epidermal growth factor receptor 2 oncoprotein is the basis for targeted treatment. Molecular classifications are indicators of genetic tumor heterogeneity, which is probed with multigene assays and can lead to improved stratification into low- and high-risk groups for personalized therapy. Intratumor heterogeneity occurs at the morphologic, genomic, transcriptomic, and proteomic levels, creating diagnostic and therapeutic challenges. Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Despite the improved knowledge of the complex genetic and phenotypic features underpinning tumor heterogeneity, there has been only limited advancement in diagnostic, prognostic, or predictive strategies for breast cancer. The current guidelines for reporting of biomarkers aim to maximize patient eligibility for targeted therapy, but do not take into account intratumor heterogeneity. The molecular classification of breast cancer is not implemented in routine clinical practice. Additional studies and in-depth analysis are required to understand the clinical significance of rapidly accumulating data. This review highlights inter- and intratumor heterogeneity of breast carcinoma with special emphasis on pathologic findings, and provides insights into the clinical significance of molecular and cellular mechanisms of heterogeneity. PMID:29276709

Retrospective cohort study of prognostic factors in patients with oral cavity and oropharyngeal squamous cell carcinoma.

PubMed

Carrillo, José F; Carrillo, Liliana C; Cano, Ana; Ramirez-Ortega, Margarita C; Chanona, Jorge G; Avilés, Alejandro; Herrera-Goepfert, Roberto; Corona-Rivera, Jaime; Ochoa-Carrillo, Francisco J; Oñate-Ocaña, Luis F

2016-04-01

Prognostic factors in oral cavity and oropharyngeal squamous cell carcinoma (SCC) are debated. The purpose of this study was to investigate the association of prognostic factors with oncologic outcomes. Patients with oral cavity and oropharyngeal SCC treated from 1997 to 2012 were included in this retrospective cohort study. Associations of prognostic factors with locoregional recurrence (LRR) or overall survival (OS) were analyzed using the logistic regression and the Cox models. Six hundred thirty-four patients were included in this study; tumor size, surgical margins, and N classification were associated with LRR (p < .0001); considering histopathology: perineural invasion, lymphocytic infiltration, infiltrative borders, and N classification were significant determinants of LRR. Tumor size, N classification, alcoholism, and surgical margins were associated with OS (p < .0001); considering pathologic prognostic factors, perivascular invasion, islands borders, and surgical margins were independently associated with OS (p < .0001). Surgical margins, perineural and perivascular invasion, lymphocytic infiltration, and infiltrative patterns of tumor invasion are significant prognostic factors in oral cavity and oropharyngeal SCC. © 2015 Wiley Periodicals, Inc.

Integrated computational biology analysis to evaluate target genes for chronic myelogenous leukemia.

PubMed

Zheng, Yu; Wang, Yu-Ping; Cao, Hongbao; Chen, Qiusheng; Zhang, Xi

2018-06-05

Although hundreds of genes have been linked to chronic myelogenous leukemia (CML), many of the results lack reproducibility. In the present study, data across multiple modalities were integrated to evaluate 579 CML candidate genes, including literature‑based CML‑gene relation data, Gene Expression Omnibus RNA expression data and pathway‑based gene‑gene interaction data. The expression data included samples from 76 patients with CML and 73 healthy controls. For each target gene, four metrics were proposed and tested with case/control classification. The effectiveness of the four metrics presented was demonstrated by the high classification accuracy (94.63%; P<2x10‑4). Cross metric analysis suggested nine top candidate genes for CML: Epidermal growth factor receptor, tumor protein p53, catenin β 1, janus kinase 2, tumor necrosis factor, abelson murine leukemia viral oncogene homolog 1, vascular endothelial growth factor A, B‑cell lymphoma 2 and proto‑oncogene tyrosine‑protein kinase. In addition, 145 CML candidate pathways enriched with 485 out of 579 genes were identified (P<8.2x10‑11; q=0.005). In conclusion, weighted genetic networks generated using computational biology may be complementary to biological experiments for the evaluation of known or novel CML target genes.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Molecular Classification of Grade 3 Endometrioid Endometrial Cancers Identifies Distinct Prognostic Subgroups.

PubMed

Bosse, Tjalling; Nout, Remi A; McAlpine, Jessica N; McConechy, Melissa K; Britton, Heidi; Hussein, Yaser R; Gonzalez, Carlene; Ganesan, Raji; Steele, Jane C; Harrison, Beth T; Oliva, Esther; Vidal, August; Matias-Guiu, Xavier; Abu-Rustum, Nadeem R; Levine, Douglas A; Gilks, C Blake; Soslow, Robert A

2018-05-01

Our aim was to investigate whether molecular classification can be used to refine prognosis in grade 3 endometrial endometrioid carcinomas (EECs). Grade 3 EECs were classified into 4 subgroups: p53 abnormal, based on mutant-like immunostaining (p53abn); MMR deficient, based on loss of mismatch repair protein expression (MMRd); presence of POLE exonuclease domain hotspot mutation (POLE); no specific molecular profile (NSMP), in which none of these aberrations were present. Overall survival (OS) and recurrence-free survival (RFS) rates were compared using the Kaplan-Meier method (Log-rank test) and univariable and multivariable Cox proportional hazard models. In total, 381 patients were included. The median age was 66 years (range, 33 to 96 y). Federation Internationale de Gynecologie et d'Obstetrique stages (2009) were as follows: IA, 171 (44.9%); IB, 120 (31.5%); II, 24 (6.3%); III, 50 (13.1%); IV, 11 (2.9%). There were 49 (12.9%) POLE, 79 (20.7%) p53abn, 115 (30.2%) NSMP, and 138 (36.2%) MMRd tumors. Median follow-up of patients was 6.1 years (range, 0.2 to 17.0 y). Compared to patients with NSMP, patients with POLE mutant grade 3 EEC (OS: hazard ratio [HR], 0.36 [95% confidence interval, 0.18-0.70]; P=0.003; RFS: HR, 0.17 [0.05-0.54]; P=0.003) had a significantly better prognosis; patients with p53abn tumors had a significantly worse RFS (HR, 1.73 [1.09-2.74]; P=0.021); patients with MMRd tumors showed a trend toward better RFS. Estimated 5-year OS rates were as follows: POLE 89%, MMRd 75%, NSMP 69%, p53abn 55% (Log rank P=0.001). Five-year RFS rates were as follows: POLE 96%, MMRd 77%, NSMP 64%, p53abn 47% (P=0.000001), respectively. In a multivariable Cox model that included age and Federation Internationale de Gynecologie et d'Obstetrique stage, POLE and MMRd status remained independent prognostic factors for better RFS; p53 status was an independent prognostic factor for worse RFS. Molecular classification of grade 3 EECs reveals that these tumors are a mixture of molecular subtypes of endometrial carcinoma, rather than a homogeneous group. The addition of molecular markers identifies prognostic subgroups, with potential therapeutic implications.

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.

[Molecular and immunohistochemical diagnostics in melanoma].

PubMed

Schilling, B; Griewank, K G

2016-07-01

To provide appropriate therapy and follow-up to patients with malignant melanoma, proper diagnostics are of critical importance. Targeted therapy of advanced melanoma is based on the molecular genetic analyses of tumor tissue. In addition, sequencing of genes and other genetic approaches can provide insight into the origin of melanocytic tumors and can aid in distinguishing benign from malignant lesions. In this regard, spizoid neoplasms remain a challenging entity. Aside from genetic analyses of tumor tissue, immunohistochemistry remains an essential tool in melanoma diagnostics and TNM classification. With new immunotherapies being approved for advanced melanoma, immunohistochemistry to determine PD-L1 expression has gained clinical interest. While PD-L1 expression is associated with response to PD-1 blockade, a substantial number of patients without PD-L1 expression can still experience tumor remission upon treatment. In this review, current and future developments in melanoma diagnostics with regard to molecular genetics and immunohistochemistry are summarized. The utilization of such analyses in clinical decision making is also discussed.

Investigation of Antiangiogenic Mechanisms Using Novel Imaging Techniques

DTIC Science & Technology

2010-02-01

of the tumor environment can sensitize the tumor to conventional cytotoxic therapies. To this end, we employ the window chamber model to optically ...facilitate longitudinal, in vivo investigation into the parameters of interest. These include Doppler Optical Coherence Tomography for the measurement of... Optical Techniques, Tumor Pathophysiology, Treatment Response, Vascular Normalization 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18

Automated analysis and classification of melanocytic tumor on skin whole slide images.

PubMed

Xu, Hongming; Lu, Cheng; Berendt, Richard; Jha, Naresh; Mandal, Mrinal

2018-06-01

This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed. Finally, the skin melanocytic image is classified into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine (mSVM) with extracted epidermis and dermis features. Experimental results on 66 skin whole slide images indicate that the proposed technique achieves more than 95% classification accuracy, which suggests that the technique has the potential to be used for assisting pathologists on skin biopsy image analysis and classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Osteochondroma of the mandibular condyle: a classification system based on computed tomographic appearances.

PubMed

Chen, Min-jie; Yang, Chi; Qiu, Ya-ting; Zhou, Qin; Huang, Dong; Shi, Hui-min

2014-09-01

The objectives of this study were to introduce the classification of osteochondroma of the mandibular condyle based on computed tomographic images and to present our treatment experiences. From January 2002 and December 2012, a total of 61 patients with condylar osteochondroma were treated in our division. Both clinical and radiologic aspects were reviewed. The average follow-up period was 24.3 months with a range of 6 to 120 months. Two types of condylar osteochondroma were presented: type 1 (protruding expansion) in 50 patients (82.0%) and type 2 (globular expansion) in 11 patients (18.0%). Type 1 condylar osteochondroma presented 5 forms: anterior/anteromedial (58%), posterior/posteromedial (6%), medial (16%), lateral (6%), and gigantic (14%). Local resection was performed on patients with type 1 condylar osteochondroma. Subtotal condylectomy/total condylectomy using costochondral graft reconstruction with/without orthognathic surgeries was performed on patients with type 2 condylar osteochondroma. During the follow-up period, tumor reformation, condyle absorption, and new deformity were not detected. The patients almost reattained facial symmetry. Preoperative classification based on computed tomographic images will help surgeons to choose the suitable surgical procedure to treat the condylar osteochondroma.

[Cystic renal neoplasms. New entities and molecular findings].

PubMed

Moch, H

2010-10-01

Renal neoplasms with dominant cysts represent a broad spectrum of known as well as novel renal tumor entities. Established renal tumors with dominant cysts include cystic nephroma, mixed epithelial and stromal tumor, synovial sarcoma and multilocular cystic renal cancer (WHO classification 2004). Novel tumor types have recently been reported, which are also characterized by marked cyst formation. Examples are tubulocystic renal cancer and renal cancer in end-stage renal disease. These tumors are very likely to be included in a future WHO classification due to their characteristic phenotype and molecular features. Cysts and clear cell renal cell carcinoma frequently coexist in the kidneys of patients with von Hippel-Lindau disease. Cysts are also a component of many sporadic clear cell renal cell carcinomas. Multilocular cystic renal cell carcinoma is composed almost exclusively of cysts and is regarded as a specific subtype of clear cell renal cancer. Recent molecular findings suggest that clear cell renal cancer may develop via a cyst-dependent mechanism in von Hippel-Lindau syndrome as well as via cyst-independent molecular pathways in sporadic clear cell renal cancer.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

[French brain tumor database: general results on 40,000 cases, main current applications and future prospects].

PubMed

Zouaoui, S; Rigau, V; Mathieu-Daudé, H; Darlix, A; Bessaoud, F; Fabbro-Peray, P; Bauchet, F; Kerr, C; Fabbro, M; Figarella-Branger, D; Taillandier, L; Duffau, H; Trétarre, B; Bauchet, L

2012-02-01

This work aimed at prospectively record all primary central nervous system tumor (PCNST) cases in France, for which histological diagnosis was available. The objectives were to (i) create a national database and network to perform epidemiological studies, (ii) implement clinical and basic research protocols, and (iii) harmonize the health care of patients affected by PCNST. The methodology is based on a multidisciplinary national network already established by the French Brain Tumor DataBase (FBTDB) (Recensement national histologique des tumeurs primitives du système nerveux central [RnhTPSNC]), and the active participation of the Scientific Societies involved in neuro-oncology in France. From 2004 to 2009, 43,929 cases of newly diagnosed and histologically confirmed PCNST have been recorded. Histological diagnoses included gliomas (42,4%), all other neuroepithelial tumors (4,4%), tumors of the meninges (32,3%), nerve sheath tumors (9,2%), lymphomas (3,4%) and others (8,3%). Cryopreservation was reported for 9603 PCNST specimens. Tumor resections were performed in 78% cases, while biopsies accounted for 22%. Median age at diagnosis, sex, percentage of resections and number of cryopreserved tumors were detailed for each histology, according to the WHO classification. Many current applications and perspectives for the FBTDB are illustrated in the discussion. To our knowledge, this work is the first database in Europe, dedicated to PCNST, including clinical, surgical and histological data (with also cryopreservation of the specimens), and which may have major epidemiological, clinical and research implications. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Colorectal cancer: genetic abnormalities, tumor progression, tumor heterogeneity, clonal evolution and tumor-initiating cells.

PubMed

Testa, Ugo; Pelosi, Elvira; Castelli, Germana

2018-04-13

Colon cancer is the third most common cancer worldwide. Most colorectal cancer occurrences are sporadic, not related to genetic predisposition or family history; however, 20-30% of patients with colorectal cancer have a family history of colorectal cancer and 5% of these tumors arise in the setting of a Mendelian inheritance syndrome. In many patients, the development of a colorectal cancer is preceded by a benign neoplastic lesion: either an adenomatous polyp or a serrated polyp. Studies carried out in the last years have characterized the main molecular alterations occurring in colorectal cancers, showing that the tumor of each patient displays from two to eight driver mutations. The ensemble of molecular studies, including gene expression studies, has led to two proposed classifications of colorectal cancers, with the identification of four/five non-overlapping groups. The homeostasis of the rapidly renewing intestinal epithelium is ensured by few stem cells present at the level of the base of intestinal crypts. Various experimental evidence suggests that colorectal cancers may derive from the malignant transformation of intestinal stem cells or of intestinal cells that acquire stem cell properties following malignant transformation. Colon cancer stem cells seem to be involved in tumor chemoresistance, radioresistance and relapse.

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.

Clinical significance of erythropoietin receptor expression in oral squamous cell carcinoma

PubMed Central

2012-01-01

Background Hypoxic tumors are refractory to radiation and chemotherapy. High expression of biomarkers related to hypoxia in head and neck cancer is associated with a poorer prognosis. The present study aimed to evaluate the clinicopathological significance of erythropoietin receptor (EPOR) expression in oral squamous cell carcinoma (OSCC). Methods The study included 256 patients who underwent primary surgical resection between October 1996 and August 2005 for treatment of OSCC without previous radiotherapy and/or chemotherapy. Clinicopathological information including gender, age, T classification, N classification, and TNM stage was obtained from clinical records and pathology reports. The mRNA and protein expression levels of EPOR in OSCC specimens were evaluated by Q-RT-PCR, Western blotting and immunohistochemistry assays. Results We found that EPOR were overexpressed in OSCC tissues. The study included 17 women and 239 men with an average age of 50.9 years (range, 26–87 years). The mean follow-up period was 67 months (range, 2–171 months). High EPOR expression was significantly correlated with advanced T classification (p < 0.001), advanced TNM stage (p < 0.001), and positive N classification (p = 0.001). Furthermore, the univariate analysis revealed that patients with high tumor EPOR expression had a lower 5-year overall survival rate (p = 0.0011) and 5-year disease-specific survival rate (p = 0.0017) than patients who had low tumor levels of EPOR. However, the multivariate analysis using Cox’s regression model revealed that only the T and N classifications were independent prognostic factors for the 5-year overall survival and 5-year disease-specific survival rates. Conclusions High EPOR expression in OSCC is associated with an aggressive tumor behavior and poorer prognosis in the univariate analysis among patients with OSCC. Thus, EPOR expression may serve as a treatment target for OSCC in the future. PMID:22639817

Prognostic significance of KIT exon 11 deletion mutation in intermediate-risk gastrointestinal stromal tumor.

PubMed

Quek, Richard; Farid, Mohamad; Kanjanapan, Yada; Lim, Cindy; Tan, Iain Beehuat; Kesavan, Sittampalam; Lim, Tony Kiat Hon; Oon, Lynette Lin-Ean; Goh, Brian Kp; Chan, Weng Hoong; Teo, Melissa; Chung, Alexander Yf; Ong, Hock Soo; Wong, Wai Keong; Tan, Patrick; Yip, Desmond

2017-06-01

Benefit of adjuvant imatinib therapy following curative resection in patients with intermediate-risk gastrointestinal stromal tumor (GIST) is unclear. GIST-specific exon mutations, in particular exon 11 deletions, have been shown to be prognostic. We hypothesize that specific KIT mutations may improve risk stratification in patients with intermediate-risk GIST, identifying a subgroup of patients who may benefit from adjuvant therapy. In total, 142 GIST patients with complete clinicopathologic and mutational data from two sites were included. Risk classification was based on the modified National Institute of Health (NIH) criteria. In this cohort, 74% (n = 105) of patients harbored a KIT mutation; 61% (n = 86) were found in exon 11 of which nearly 70% were KIT exon 11 deletions (n = 60). A total of 18% (n = 25) of cases were classified as having intermediate-risk disease. Univariate analysis confirmed tumor size, mitotic index, nongastric origin, presence of tumor rupture and modified NIH criteria were adversely prognostic for relapse-free survival (RFS). Among KIT/PDGFRA mutants, KIT exon 11 deletions had a significantly worse prognosis (hazard ratio 2.31; 95% confidence interval, 1.30-4.10; P = 0.003). Multivariate analysis confirmed KIT exon 11 deletion (P = 0.003) and clinical risk classification (P < 0.001) as independent adverse prognostic factors for RFS. Intermediate-risk patients harboring KIT exon 11 deletions had RFS outcomes similar to high-risk patients. The presence of KIT exon 11 deletion mutation in patients with intermediate-risk GIST is associated with an inferior clinical outcome with RFS similar to high-risk patients. © 2016 John Wiley & Sons Australia, Ltd.

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

A New Paradigm for the Treatment of Ovarian Cancer: The Use of Epigenetic Therapy to Sensitize Patients to Immunotherapy and Chemotherapy

DTIC Science & Technology

2016-10-01

lymphoid and cancer cells from freshly dissociated tumors in cases where enough tumor is available, allowing analysis by flow cytometry and mRNA...agent, 5-aza-cytidine (AZA) potently stimulates tumor immune attraction of T- cells to the tumor microenvironment. This augmented by addition of a...demethylation, histone deactylases, immune checkpoint therapy, viral defense, immune cell attraction 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors

DTIC Science & Technology

2015-10-01

AWARD NUMBER: W81XWH-14-1-0414 TITLE: Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors PRINCIPAL INVESTIGATOR: Jean Mulcahy...29 Sep 2015 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-14-1-0414 Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors 5b. GRANT...ABSTRACT 200 words most significant findings 15. SUBJECT TERMS autophagy , BRAF, brain tumor. pediatric 16. SECURITY CLASSIFICATION OF: 17

HPV- and non-HPV-related subtypes of penile squamous cell carcinoma (SCC): Morphological features and differential diagnosis according to the new WHO classification (2015).

PubMed

Sanchez, Diego F; Cañete, Sofía; Fernández-Nestosa, María José; Lezcano, Cecilia; Rodríguez, Ingrid; Barreto, José; Alvarado-Cabrero, Isabel; Cubilla, Antonio L

2015-05-01

The majority of penile carcinomas are squamous cell carcinomas originating in the squamous mucosa covering the glans, coronal sulcus, or inner surface of the foreskin, the 3 latter sites comprising the penile anatomical compartments. There is a variegated spectrum of subtypes of penile squamous cell carcinomas according to recent classification schemes. Currently, because of etiological and prognostic considerations, 2 morphologically and molecularly distinctive groups of subtypes of penile SCCs based on the presence of HPV were delineated. The predominant cell composition of tumors associated with HPV is the basaloid cell, which is the hallmark and best tissue marker for the virus. Tumors negative for the virus, however, are preferentially of lower grade and keratinizing maturing neoplasms with the exception of sarcomatoid carcinoma. HPV is detected in research studies by PCR or in situ hybridization (ISH) technologies, but p16 immunohistochemical stain is an adequate and less-expensive surrogate that is useful in the routine practice of pathology. The aim of this review is to demonstrate the variable morphological phenotypic expression of penile tumors separating non-HPV- and HPV-related neoplasms and to add morphological information that will justify subclassifying squamous cell carcinomas in a number of special subtypes. A brief discussion of the differential diagnosis in each category is also provided. Copyright © 2015 Elsevier Inc. All rights reserved.

Update from the 4th Edition of the World Health Organization Classification of Head and Neck Tumours: Tumors of the Salivary Gland.

PubMed

Seethala, Raja R; Stenman, Göran

2017-03-01

The salivary gland section in the 4th edition of the World Health Organization classification of head and neck tumors features the description and inclusion of several entities, the most significant of which is represented by (mammary analogue) secretory carcinoma. This entity was extracted mainly from acinic cell carcinoma based on recapitulation of breast secretory carcinoma and a shared ETV6-NTRK3 gene fusion. Also new is the subsection of "Other epithelial lesions," for which key entities include sclerosing polycystic adenosis and intercalated duct hyperplasia. Many entities have been compressed into their broader categories given clinical and morphologic similarities, or transitioned to a different grouping as was the case with low-grade cribriform cystadenocarcinoma reclassified as intraductal carcinoma (with the applied qualifier of low-grade). Specific grade has been removed from the names of the salivary gland entities such as polymorphous adenocarcinoma, providing pathologists flexibility in assigning grade and allowing for recognition of a broader spectrum within an entity. Cribriform adenocarcinoma of (minor) salivary gland origin continues to be divisive in terms of whether it should be recognized as a distinct category. This chapter also features new key concepts such as high-grade transformation. The new paradigm of translocations and gene fusions being common in salivary gland tumors is featured heavily in this chapter.

Statistical Report of Central Nervous System Tumors Histologically Diagnosed in the Sichuan Province of China from 2008 to 2013: A West China Glioma Center Report.

PubMed

Wang, Xiang; Chen, Jin-Xiu; Zhou, Qiao; Liu, Yan-Hui; Mao, Qing; You, Chao; Chen, Ni; Xiong, Li; Duan, Jie; Liu, Liang

2016-12-01

Sichuan is a province in the west of China with a population of 81.4 million. This is the first statistical report of central nervous system (CNS) tumors surgically treated and histologically diagnosed in a large Chinese population. All the patient data were obtained from 86 medical facilities, which covered the Sichuan province population. Data from patients who underwent surgery between 2008 and 2013 and corresponding histology samples were re-reviewed in the major pathology centers. All the CNS tumors were categorized according to International Classification of Diseases (ICD)-10 and ICD-O-3 classifications and reviewed manually. The tumor distribution was analyzed and stratified by gender, age, race, and tumor sites. Tumors in some ethnic minorities, such as the Tibetan people, also were analyzed. The final analytic dataset included 35,496 records. The top four histologic tumors were meningioma (28.51 %), pituitary adenoma (15.00 %), nerve sheath (13.77 %), and glioblastoma (11.82 %). There was a dramatically high incidence of malignant tumor in males. The median age at diagnosis ranged from 13 years (pineal region tumors) to 56 years (metastatic brain tumors). Most of the tumors in the insular lobe or cerebellum were low grade, whereas those in the thalamus or basal ganglia were likely to be high grade. The incidence of malignant tumors or high-grade gliomas in the Tibetans was significantly lower than in the Chinese Han population. This report is a preliminary statistical analysis of brain and spinal tumors in a large Chinese population and may serve as a useful resource for clinicians, researchers, and patients' families.

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.

Mucinous cystic neoplasms of the mesentery: a case report and review of the literature

PubMed Central

Metaxas, Georgios; Tangalos, Athanasios; Pappa, Polyxeni; Papageorgiou, Irene

2009-01-01

Background Mucinous cystic neoplasms arise in the ovary and various extra-ovarian sites. While their pathogenesis remains conjectural, their similarities suggest a common pathway of development. There have been rare reports involving the mesentery as a primary tumour site. Case presentation A cystic mass of uncertain origin was demonstrated radiologically in a 22 year old female with chronic abdominal pain. At laparotomy, the mass was fixed within the colonic mesentery. Histology demonstrated a benign mucinous cystadenoma. Methods and results We review the literature on mucinous cystic neoplasms of the mesentery and report on the pathogenesis, biologic behavior, diagnosis and treatment of similar extra-ovarian tumors. We propose an updated classification of mesenteric cysts and cystic tumors. Conclusion Mucinous cystic neoplasms of the mesentery present almost exclusively in women and must be considered in the differential diagnosis of mesenteric tumors. Only full histological examination of a mucinous cystic neoplasm can exclude a borderline or malignant component. An updated classification of mesenteric cysts and cystic tumors is proposed. PMID:19454018

Sudden unexpected death from oligodendroglioma: a case report and review of the literature.

PubMed

Manousaki, Maria; Papadaki, Helen; Papavdi, Asteria; Kranioti, Elena F; Mylonakis, Panagiotis; Varakis, John; Michalodimitrakis, Manolis

2011-12-01

Sudden and unexpected deaths due to asymptomatic 5 primary brain tumors are extremely rare, with an incidence that ranges from 0.16 to 3.2%. Usually, such tumors are glioblastomas or, less commonly, astrocytomas. Asymptomatic oligodendrogliomas causing sudden death are hardly ever reported among medico-legal investigated cases.We report a rare case of sudden and unexpected death from a previously asymptomatic and undiagnosed, well-differentiated, grade II oligodendrogloioma (WHO classification). According to the autopsy and the microscopic findings brain edema as a result of obstruction of the cerebrospinal fluid flow due to hemorrhagic leakage of the oligodendroglioma is incriminated as the most probable physiopathological mechanism for the sudden death. Diagnosis is mainly based on the special microscopic features of the tumor cells (typical "fried-egg" appearance), interrupted by a dense network of branching capillaries. We discuss further the pathophysiological mechanisms of death and present a short review of literature.

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

[New features in the 2014 WHO classification of uterine neoplasms].

PubMed

Lax, S F

2016-11-01

The 2014 World Health Organization (WHO) classification of uterine tumors revealed simplification of the classification by fusion of several entities and the introduction of novel entities. Among the multitude of alterations, the following are named: a simplified classification for precursor lesions of endometrial carcinoma now distinguishes between hyperplasia without atypia and atypical hyperplasia, the latter also known as endometrioid intraepithelial neoplasia (EIN). For endometrial carcinoma a differentiation is made between type 1 (endometrioid carcinoma with variants and mucinous carcinoma) and type 2 (serous and clear cell carcinoma). Besides a papillary architecture serous carcinomas may show solid and glandular features and TP53 immunohistochemistry with an "all or null pattern" assists in the diagnosis of serous carcinoma with ambiguous features. Neuroendocrine neoplasms are categorized in a similar way to the gastrointestinal tract into well differentiated neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas (small cell and large cell types). Leiomyosarcomas of the uterus are typically high grade and characterized by marked nuclear atypia and lively mitotic activity. Low grade stromal neoplasms frequently show gene fusions, such as JAZF1/SUZ12. High grade endometrial stromal sarcoma is newly defined by cyclin D1 overexpression and the presence of the fusion gene YWHAE/FAM22 and must be distinguished from undifferentiated uterine sarcoma. Carcinosarcomas (malignant mixed Mullerian tumors MMMT) show biological and molecular similarities to high-grade carcinomas.

Setting the Stage for Personalized Treatment of Glioma | Center for Cancer Research

Cancer.gov

Gliomas, the most common type of primary brain tumors in adults, arise from different types of glial cells, which support and protect the neurons of the central nervous system. How a patient’s glioma is treated depends in part on the type of glial cell from which the tumor developed. Classification of gliomas has traditionally been done by microscopic analysis of tumor

Comparative Oncogenomics for Peripheral Nerve Sheath Cancer Gene Discovery

DTIC Science & Technology

2015-06-01

neurofibromas and MPNSTs, establish gene signatures defining distinct tumor subtypes and functionally test the role of selected driver mutations ...allografted tumor cells, and a variety of in vitro functional assays. We will validate the relevance of these mutated mouse genes in human neurofibromas...and MPNSTs by determining whether these same genes are mutated in human tumors. 15. SUBJECT TERMS Nothing listed 16. SECURITY CLASSIFICATION OF: 17

Identifying Breast Tumor Suppressors Using in Vitro and in Vivo RNAi Screens

DTIC Science & Technology

2011-10-01

vivo RNA interference screen, breast cancer , tumor suppressor, leukemia inhibitory factor receptor (LIFR) 16. SECURITY CLASSIFICATION OF: 17...The identification of these genes will improve the understanding of the causes of breast cancer , which may lead to therapeutic advancements for... breast cancer prevention and treatment. BODY Objective 1: Identification of breast tumor suppressors using in vitro and in vivo RNAi screens

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

PubMed Central

Sanjuán, Ana; Price, Cathy J.; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K.; Geva, Sharon; Leff, Alex P.; Yousry, Tarek A.; Seghier, Mohamed L.

2013-01-01

Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic. PMID:24381535

The dawn of a new era in onco-cardiology: The Kumamoto Classification.

PubMed

Sueta, Daisuke; Tabata, Noriaki; Akasaka, Tomonori; Yamashita, Takayoshi; Ikemoto, Tomokazu; Hokimoto, Seiji

2016-10-01

The term "onco-cardiology" has been used in reference to cardiotoxicity in the treatment of malignant disease. In actual clinical situations, however, cardiovascular disease (CVD) associated with malignant disease and the concurrence of atherosclerotic disease with malignant disease are commonly observed, complicating the course of treatment. Patients with malignant disease associated with coronary artery disease often die from the cardiovascular disease, so it is essential to classify these disease states. Additionally, the prevalence of these classifications makes it easy to manage patients with malignant disease and coronary artery disease. We divided the broad field of onco-cardiology into 4 classifications based on clinical scenarios (CSs): CS1 represents the so-called paraneoplastic syndrome. CS2 represents cardiotoxicity during treatment of malignant diseases. CS3 represents the concurrence of atherosclerotic disease with malignant disease, and CS4 represents cardiovascular disease with benign tumors. This classification facilitates the management of patients with malignant disease and coronary artery disease by promoting not only the primary but also the secondary prevention of CVD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

PubMed

Rantalainen, Mattias; Klevebring, Daniel; Lindberg, Johan; Ivansson, Emma; Rosin, Gustaf; Kis, Lorand; Celebioglu, Fuat; Fredriksson, Irma; Czene, Kamila; Frisell, Jan; Hartman, Johan; Bergh, Jonas; Grönberg, Henrik

2016-11-30

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

PubMed

Randhawa, Vinay; Kumar Singh, Anil; Acharya, Vishal

2015-12-01

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

Risk factors contributing to a poor prognosis of papillary thyroid carcinoma: validity of UICC/AJCC TNM classification and stage grouping.

PubMed

Ito, Yasuhiro; Miyauchi, Akira; Jikuzono, Tomoo; Higashiyama, Takuya; Takamura, Yuuki; Miya, Akihiro; Kobayashi, Kaoru; Matsuzuka, Fumio; Ichihara, Kiyoshi; Kuma, Kanji

2007-04-01

In 2002, the UICC/AJCC TNM classification for papillary thyroid carcinoma was revised. In this study, we examined the validity of this classification system by investigating the predictors of disease-free survival (DFS) and cause-specific survival (CSS) in patients. We examined various clinicopathological features, including the component of the TNM classification, for 1,740 patients who underwent initial and curative surgery for papillary carcinoma between 1987 and 1995. Clinical and pathological T4a, clinical N1b in the TNM classification, and patient age were recognized as independent predictors of not only DFS, but also CSS of patients. Tumor size, male gender, and central node metastasis independently affected DFS only. There were 1,005 pathological N1b patients, but pathological N1b did not independently affect either DFS or CSS. Regarding the stage grouping, clinical stage IVA including clinical N1b more clearly affected DFS and CSS than pathological stage IVA including pathological N1b. Clinical stage grouping was more useful than pathological stage grouping for predicting the prognosis of papillary carcinoma patients possibly because pathological stage overestimates the biological characteristics of many pathological N1b tumors.

The AJCC 8th Edition Staging System for Soft Tissue Sarcoma of the Extremities or Trunk: A Cohort Study of the SEER Database.

PubMed

Cates, Justin M M

2018-02-01

Background: The AJCC recently published the 8th edition of its cancer staging system. Significant changes were made to the staging algorithm for soft tissue sarcoma (STS) of the extremities or trunk, including the addition of 2 additional T (size) classifications in lieu of tumor depth and grouping lymph node metastasis (LNM) with distant metastasis as stage IV disease. Whether these changes improve staging system performance is questionable. Patients and Methods: This retrospective cohort analysis of 21,396 adult patients with STS of the extremity or trunk in the SEER database compares the AJCC 8th edition staging system with the 7th edition and a newly proposed staging algorithm using a variety of statistical techniques. The effect of tumor size on disease-specific survival was assessed by flexible, nonlinear Cox proportional hazard regression using restricted cubic splines and fractional polynomials. Results: The slope of covariate-adjusted log hazards for sarcoma-specific survival decreases for tumors >8 cm in greatest dimension, limiting prognostic information contributed by the new T4 classification in the AJCC 8th edition. Anatomic depth independently provides significant prognostic information. LNM is not equivalent to distant, non-nodal metastasis. Based on these findings, an alternative staging system is proposed and demonstrated to outperform both AJCC staging schemes. The analyses presented also disclose no evidence of improved clinical performance of the 8th edition compared with the previous edition. Conclusions: The AJCC 8th edition staging system for STS is no better than the previous 7th edition. Instead, a proposed staging system based on histologic grade, tumor size, and anatomic depth shows significantly higher predictive accuracy, with higher model concordance than either AJCC staging system. Changes to existing staging systems should improve the performance of prognostic models. Until such improvements are documented, AJCC committees should refrain from modifying established staging schemes. Copyright © 2018 by the National Comprehensive Cancer Network.

Sensitivity of Breast Tumors to Oncolytic Viruses

DTIC Science & Technology

2006-08-01

therapies for breast cancer based on the oncolytic virus, vesicular stomatitis virus (VSV). Studies have shown that matrix (M) protein mutants of VSV, such...more resistant to VSV-induced cytopathic effect than breast cancer cells. However, in syngeneic breast cancer system in vivo, rM51R-M virus is only...interleukin 12, breast cancer , interferon 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE

miR-448 is a novel prognostic factor of lung squamous cell carcinoma and regulates cells growth and metastasis by targeting DCLK1.

PubMed

Shan, Changting; Fei, Fan; Li, Fengzhu; Zhuang, Bo; Zheng, Yulong; Wan, Yufeng; Chen, Jianhui

2017-05-01

MicroRNA-448 (miR-448) has been showed to be low-expressed and function as tumor suppressor in most human cancers. However, there are limited reports on the clinical significance and biological function of miR-448 in lung squamous cell carcinoma. In this study, we observed that miR-448 expression was decreased in lung squamous cell carcinoma tissues and cell lines. Meanwhile, miR-448 expression associated with differentiated degree, T classification (tumor size), N classification (lymph node metastasis), M classification (distant metastasis), clinical stage and prognosis of lung squamous cell carcinoma patients. In survival analysis, low expression of miR-448 was a poor independent prognostic factor for lung squamous cell carcinoma patients. Moreover, gain-of-function and loss-of-function studies showed miR-448 acted as a tumor suppressor regulating lung squamous cell carcinoma cells growth and metastasis. Furthermore, DCLK1 has been identified as a potential target for miR-448 to regulate lung squamous cell carcinoma cells growth and metastasis. In conclusion, miR-448 low-expression was a poor prognostic factor for lung squamous cell carcinoma patients, and miR-448 served as a tumor suppressor in lung squamous cell carcinoma cells via targeting DCLK1. Copyright © 2017. Published by Elsevier Masson SAS.

Monoclonal antibody specific for IDH1 R132H mutation.

PubMed

Capper, David; Zentgraf, Hanswalter; Balss, Jörg; Hartmann, Christian; von Deimling, Andreas

2009-11-01

IDH1 R132H mutations occur in approximately 70% of astrocytomas and oligodendroglial tumors. We developed a mouse monoclonal antibody targeting the IDH1 R132H mutation. Here, we show the high specificity and sensitivity of this antibody on Western blots and tissue sections from formalin fixed paraffin embedded tumor specimens. This antibody is highly useful for tumor classification, in detecting single infiltrating tumor cells and for the characterization of the cellular role of mutant IDH1 protein.

Association between traditional clinical high-risk features and gene expression profile classification in uveal melanoma.

PubMed

Nguyen, Brandon T; Kim, Ryan S; Bretana, Maria E; Kegley, Eric; Schefler, Amy C

2018-02-01

To evaluate the association between traditional clinical high-risk features of uveal melanoma patients and gene expression profile (GEP). This was a retrospective, single-center, case series of patients with uveal melanoma. Eighty-three patients met inclusion criteria for the study. Patients were examined for the following clinical risk factors: drusen/retinal pigment epithelium (RPE) changes, vascularity on B-scan, internal reflectivity on A-scan, subretinal fluid (SRF), orange pigment, apical tumor height/thickness, and largest basal dimensions (LBD). A novel point system was created to grade the high-risk clinical features of each tumor. Further analyses were performed to assess the degree of association between GEP and each individual risk factor, total clinical risk score, vascularity, internal reflectivity, American Joint Committee on Cancer (AJCC) tumor stage classification, apical tumor height/thickness, and LBD. Of the 83 total patients, 41 were classified as GEP class 1A, 17 as class 1B, and 25 as class 2. The presence of orange pigment, SRF, low internal reflectivity and vascularity on ultrasound, and apical tumor height/thickness ≥ 2 mm were not statistically significantly associated with GEP class. Lack of drusen/RPE changes demonstrated a trend toward statistical association with GEP class 2 compared to class 1A/1B. LBD and advancing AJCC stage was statistically associated with higher GEP class. In this cohort, AJCC stage classification and LBD were the only clinical features statistically associated with GEP class. Clinicians should use caution when inferring the growth potential of melanocytic lesions solely from traditional funduscopic and ultrasonographic risk factors without GEP data.

Prognostic factors of non-functioning pancreatic neuroendocrine tumor revisited: The value of WHO 2010 classification.

PubMed

Bu, Jiyoung; Youn, Sangmin; Kwon, Wooil; Jang, Kee Taek; Han, Sanghyup; Han, Sunjong; You, Younghun; Heo, Jin Seok; Choi, Seong Ho; Choi, Dong Wook

2018-02-01

Various factors have been reported as prognostic factors of non-functional pancreatic neuroendocrine tumors (NF-pNETs). There remains some controversy as to the factors which might actually serve to successfully prognosticate future manifestation and diagnosis of NF-pNETs. As well, consensus regarding management strategy has never been achieved. The aim of this study is to further investigate potential prognostic factors using a large single-center cohort to help determine the management strategy of NF-pNETs. During the time period 1995 through 2013, 166 patients with NF-pNETs who underwent surgery in Samsung Medical Center were entered in a prospective database, and those factors thought to represent predictors of prognosis were tested in uni- and multivariate models. The median follow-up time was 46.5 months; there was a maximum follow-up period of 217 months. The five-year overall survival and disease-free survival rates were 88.5% and 77.0%, respectively. The 2010 WHO classification was found to be the only prognostic factor which affects overall survival and disease-free survival in multivariate analysis. Also, pathologic tumor size and preoperative image tumor size correlated strongly with the WHO grades ( p <0.001, and p <0.001). Our study demonstrates that 2010 WHO classification represents a valuable prognostic factor of NF-pNETs and tumor size on preoperative image correlated with WHO grade. In view of the foregoing, the preoperative image size is thought to represent a reasonable reference with regard to determination and development of treatment strategy of NF-pNETs.

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.

Rare malignant pediatric tumors registered in the German Childhood Cancer Registry 2001-2010.

PubMed

Brecht, Ines B; Bremensdorfer, Claudia; Schneider, Dominik T; Frühwald, Michael C; Offenmüller, Sonja; Mertens, Rolf; Vorwerk, Peter; Koscielniak, Ewa; Bielack, Stefan S; Benesch, Martin; Hero, Barbara; Graf, Norbert; von Schweinitz, Dietrich; Kaatsch, Peter

2014-07-01

The German Childhood Cancer Registry (GCCR) annually registers approximately 2,000 children diagnosed with a malignant disease (completeness of registration >95%). While most pediatric cancer patients are diagnosed and treated according to standardized cooperative protocols of the German Society for Pediatric Oncology and Hematology (GPOH), patients with rare tumors are at risk of not being integrated in the network including trials and reference centers. A retrospective analysis of all rare extracranial solid tumors reported to the GCCR 2001-2010 (age <18 years) was undertaken using a combination of the International Classification of Childhood Cancer (ICCC-3) and the International Classification of Diseases-Oncology (ICD-O-3). Tumors accounting for <0.3% of all malignancies were defined as rare (approx. 6 cases/year and registered malignancy). According to this definition 1,189 rare extracranial solid tumors (18.2% of all malignant extracranial solid tumors) were registered, among these 232 patients (19.5% of rare tumor cases), were not included in preexisting GPOH studies/registries. Within 10 years, the number of registered non-GPOH-trial patients with a rare tumor increased. Though most of the GCCR-registered patients with rare malignant tumors are treated within GPOH trials, there is a considerable number of patients that have been diagnosed and treated outside the structures of the GPOH. These patients should be reported to the recently founded German Pediatric Rare Tumor Registry (STEP). Active data accrual and the development of appropriate structures will allow for better registration and improvement of medical care in these patients. © 2014 Wiley Periodicals, Inc.

Regulation of IAP (Inhibitor of Apoptosis) Gene Expression by the p53 Tumor Suppressor Protein

DTIC Science & Technology

2005-05-01

adenovirus, gene therapy, polymorphism, 31 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20...averaged results of three inde- pendent experiments, with standard error. Right panel: Level of p53 in infected cells using the antibody Ab-6 (Calbiochem...with highly purified mitochondria as described in (2). The arrow marks oligomerized BAK. The right _ -. panel depicts the purity of BMH CrosIinked Mito

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

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.

Prognostic value of tumor size in gastric cancer: an analysis of 2,379 patients.

PubMed

Guo, Pengtao; Li, Yangming; Zhu, Zhi; Sun, Zhe; Lu, Chong; Wang, Zhenning; Xu, Huimian

2013-04-01

Tumor size has been included into the staging systems of many solid tumors, such as lung and breast. However, tumor size is not integrated in the staging of gastric cancer, and its prognostic value for gastric cancer needs to be reappraised. A total of 2,379 patients who received radical resection for histopathologically confirmed gastric adenocarcinoma were enrolled in the present study. Tumor size, originally presented as continuous variable, was categorized into small gastric cancer (SGC) group and large gastric cancer (LGC) group using an optimal cutoff point determined by Cox proportional hazards model. The associations between tumor size and other clinicopathological factors were checked using Chi-square test. Survival of gastric cancer patients was estimated by using univariate Kaplan-Meier method, and the survival difference was checked by using the log-rank test. The significant clinicopathological factors were included into the Cox proportional hazards model to determine the independent prognostic factors, and their hazard ratios were calculated. With the optimal cutoff point of 4 cm, tumor size was categorized into SGC group (≤ 4 cm) and LGC group (>4 cm). Tumor size closely correlated with age, tumor location, macroscopic type, Lauren classification, and lymphatic vessel invasion. Moreover, tumor size was also significantly associated with depth of tumor invasion and status of regional lymph nodes. The 5-year survival rate was 68.7 % for SGC group which was much higher than 40.2 % for LGC group. Univariate analysis showed that SGC had a better survival than LGC, mainly for patients with IIA, IIB, and IIIA stage. Multivariate analysis revealed that tumor size as well as age, tumor location, macroscopic type, Lauren classification, lymphatic vessel invasion, depth of tumor invasion, and status of regional lymph nodes were independent prognostic factors for gastric cancer. Tumor size is a reliable prognostic factor for patients with gastric cancer, and the measurement of tumor size would be helpful to the staging and management of gastric cancer.

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

NASA Astrophysics Data System (ADS)

Balasubramanian, A.; Shamsuddin, R.; Prabhakaran, B.; Sawant, A.

2017-03-01

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%) (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable prediction performance. The ability to predict a baseline shift with a sufficient look-ahead window will enable clinical systems or even human users to hold the treatment beam in such situations, thereby reducing the probability of serious geometric and dosimetric errors.

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

PubMed Central

Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

2017-01-01

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5–91.4%); (ii) the predictive modeling yields lowest accuracies (50–60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96–0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable prediction performance. The ability to predict a baseline shift with a sufficient lookahead window will enable clinical systems or even human users to hold the treatment beam in such situations, thereby reducing the probability of serious geometric and dosimetric errors. PMID:28075331

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts.

PubMed

Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

2017-03-07

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%); (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable prediction performance. The ability to predict a baseline shift with a sufficient look-ahead window will enable clinical systems or even human users to hold the treatment beam in such situations, thereby reducing the probability of serious geometric and dosimetric errors.

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.

[A new entity in WHO classification of tumors of the central nervous system--embryonic tumor with abundant neuropil and true rosettes: case report and review of literature].

PubMed

Ryzhova, M V; Zheludkova, O G; Ozerov, S S; Shishkina, L V; Panina, T N; Gorelyshev, S K; Novikov, A I; Melikian, A G; Kushel', Iu V; Korshunov, A E

2011-01-01

Embryonic tumor with abundant neuropil and true rosettes (ETANTR) is a very aggressive rare tumor with unique histologic and molecular features occurring in very young children. At present approximately 80 cases of ETANTR have been documented in the literature since first description in 2000. We report seven patients with ETANTR below 4 years of age who underwent surgical resection in the Burdenko Neurosurgery Institute between 2005 and 2010. Four children have received different modality chemotherapy and radiotherapy and two patients were treated by chemotherapy alone. One child did not receive any adjuvant treatment. All children had local relapses, two of them were operated twice. A 2 year old girl underwent subtotal resection thrice. Histological examination showed that all tumors were composed of true multilayered rosettes admixed with large areas of paucicellular neuropil. By analysis of recurrences we have found that large areas of neuropil and number of true rosettes were lost and tumors acquired a resemblance to central nervous system primitive neuroectodermal tumors. In four cases frozen tumor material was available for array-based comparative genomic hybridization, which discovered trisomy of chromosome 2 and amplification at the 19q13.42 chromosome locus. Fluorescence in situ hybridization revealed amplification at the 19q13.42 chromosome locus in all cases.

Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing.

PubMed

Euskirchen, Philipp; Bielle, Franck; Labreche, Karim; Kloosterman, Wigard P; Rosenberg, Shai; Daniau, Mailys; Schmitt, Charlotte; Masliah-Planchon, Julien; Bourdeaut, Franck; Dehais, Caroline; Marie, Yannick; Delattre, Jean-Yves; Idbaih, Ahmed

2017-11-01

Molecular classification of cancer has entered clinical routine to inform diagnosis, prognosis, and treatment decisions. At the same time, new tumor entities have been identified that cannot be defined histologically. For central nervous system tumors, the current World Health Organization classification explicitly demands molecular testing, e.g., for 1p/19q-codeletion or IDH mutations, to make an integrated histomolecular diagnosis. However, a plethora of sophisticated technologies is currently needed to assess different genomic and epigenomic alterations and turnaround times are in the range of weeks, which makes standardized and widespread implementation difficult and hinders timely decision making. Here, we explored the potential of a pocket-size nanopore sequencing device for multimodal and rapid molecular diagnostics of cancer. Low-pass whole genome sequencing was used to simultaneously generate copy number (CN) and methylation profiles from native tumor DNA in the same sequencing run. Single nucleotide variants in IDH1, IDH2, TP53, H3F3A, and the TERT promoter region were identified using deep amplicon sequencing. Nanopore sequencing yielded ~0.1X genome coverage within 6 h and resulting CN and epigenetic profiles correlated well with matched microarray data. Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas, and brain metastases of different primary sites. Single nucleotide variants in IDH1, IDH2, and H3F3A were identified using deep amplicon sequencing within minutes of sequencing. Detection of TP53 and TERT promoter mutations shows that sequencing of entire genes and GC-rich regions is feasible. Nanopore sequencing allows same-day detection of structural variants, point mutations, and methylation profiling using a single device with negligible capital cost. It outperforms hybridization-based and current sequencing technologies with respect to time to diagnosis and required laboratory equipment and expertise, aiming to make precision medicine possible for every cancer patient, even in resource-restricted settings.

Computer-aided diagnosis with textural features for breast lesions in sonograms.

PubMed

Chen, Dar-Ren; Huang, Yu-Len; Lin, Sheng-Hsiung

2011-04-01

Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k-fold cross-validation (k=10) to evaluate the performance with receiver operating characteristic (ROC) curve. The area (A(Z)) under the ROC curve for the proposed CAD system with the specific textural features was 0.925±0.019. The classification ability for breast tumor with textural information is satisfactory. This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion. Copyright © 2010 Elsevier Ltd. All rights reserved.

Pattern Classification of Endocervical Adenocarcinoma: Reproducibility and Review of Criteria

PubMed Central

Rutgers, Joanne K.L.; Roma, Andres; Park, Kay; Zaino, Richard J.; Johnson, Abbey; Alvarado, Isabel; Daya, Dean; Rasty, Golnar; Longacre, Teri; Ronnett, Brigitte; Silva, Elvio

2017-01-01

Previously, our international team proposed a 3-tiered pattern classification (Pattern Classification) system for endocervical adenocarcinoma of the usual type that correlates with nodal disease and recurrence. Pattern Classification- A have well demarcated glands lacking destructive stromal invasion or lymphovascular invasion (lymphovascular invasion), Pattern Classification- B show localized, limited destructive invasion arising from A-type glands, and Pattern Classification- C have diffuse destructive stromal invasion, significant (filling a 4× field) confluence, or solid architecture. 24 Pattern Classification-A, 22 Pattern Classification-B, 38 Pattern Classification-C from the tumor set used in the original description were chosen using the reference diagnosis (reference diagnosis) originally established. 1 H&E slide per case was reviewed by 7 gynecologic pathologists, 4 from the original study. Kappa statistics were prepared, and cases with discrepancies reviewed. We found a majority agreement with reference diagnosis in 81% of cases, with complete or near complete (6 of 7) agreement in 50%. Overall concordance was 74%. Overall Kappa (agreement among pathologists) was .488 (moderate agreement). Pattern Classification- B has lowest kappa, and agreement is not improved by combining B+C. 6 of 7 reviewers had substantial agreement by weighted kappas (>.6), with one reviewer accounting for the majority of cases under or overcalled by 2 tiers. Confluence filling a 4× field, labyrinthine glands, or solid architecture accounted for undercalling other reference diagnosis-C cases. Missing a few individually infiltrative cells was the most common cause of undercalling reference diagnosis- B. Small foci of inflamed, loose or desmoplastic stroma lacking infiltrative tumor cells in reference diagnosis-A appeared to account for those cases up-graded to Pattern Classification-B. In summary, an overall concordance of 74% indicates that the criteria can be reproducibly applied by gynecologic pathologists. Further refinement of criteria should allow use of this powerful classification system to delineate which cervical adenocarcinomas can be safely treated conservatively. PMID:27255163

Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies

NASA Astrophysics Data System (ADS)

Turkki, Riku; Linder, Nina; Kovanen, Panu E.; Pellinen, Teijo; Lundin, Johan

2016-03-01

The characteristics of immune cells in the tumor microenvironment of breast cancer capture clinically important information. Despite the heterogeneity of tumor-infiltrating immune cells, it has been shown that the degree of infiltration assessed by visual evaluation of hematoxylin-eosin (H and E) stained samples has prognostic and possibly predictive value. However, quantification of the infiltration in H and E-stained tissue samples is currently dependent on visual scoring by an expert. Computer vision enables automated characterization of the components of the tumor microenvironment, and texture-based methods have successfully been used to discriminate between different tissue morphologies and cell phenotypes. In this study, we evaluate whether local binary pattern texture features with superpixel segmentation and classification with support vector machine can be utilized to identify immune cell infiltration in H and E-stained breast cancer samples. Guided with the pan-leukocyte CD45 marker, we annotated training and test sets from 20 primary breast cancer samples. In the training set of arbitrary sized image regions (n=1,116) a 3-fold cross-validation resulted in 98% accuracy and an area under the receiver-operating characteristic curve (AUC) of 0.98 to discriminate between immune cell -rich and - poor areas. In the test set (n=204), we achieved an accuracy of 96% and AUC of 0.99 to label cropped tissue regions correctly into immune cell -rich and -poor categories. The obtained results demonstrate strong discrimination between immune cell -rich and -poor tissue morphologies. The proposed method can provide a quantitative measurement of the degree of immune cell infiltration and applied to digitally scanned H and E-stained breast cancer samples for diagnostic purposes.

Prognostic value of Ki-67 index in adult medulloblastoma after accounting for molecular subgroup: a retrospective clinical and molecular analysis.

PubMed

Zhao, Fu; Zhang, Jing; Li, Peng; Zhou, Qiangyi; Zhang, Shun; Zhao, Chi; Wang, Bo; Yang, Zhijun; Li, Chunde; Liu, Pinan

2018-04-23

Medulloblastoma (MB) is a rare primary brain tumor in adults. We previously evaluated that combining both clinical and molecular classification could improve current risk stratification for adult MB. In this study, we aimed to identify the prognostic value of Ki-67 index in adult MB. Ki-67 index of 51 primary adult MBs was reassessed using a computer-based image analysis (Image-Pro Plus). All patients were followed up ranging from 12 months up to 15 years. Gene expression profiling and immunochemistry were used to establish the molecular subgroups in adult MB. Combined risk stratification models were designed based on clinical characteristics, molecular classification and Ki-67 index, and identified by multivariable Cox proportional hazards analysis. In our cohort, the mean Ki-67 value was 30.0 ± 11.3% (range 6.56-63.55%). The average Ki-67 value was significantly higher in LC/AMB than in CMB and DNMB (P = .001). Among three molecular subgroups, Group 4-tumors had the highest average Ki-67 value compared with WNT- and SHH-tumors (P = .004). Patients with Ki-67 index large than 30% displayed poorer overall survival (OS) and progression free survival (PFS) than those with Ki-67 less than 30% (OS: P = .001; PFS: P = .006). Ki-67 index (i.e. > 30%, < 30%) was identified as an independent significant prognostic factor (OS: P = .017; PFS: P = .024) by using multivariate Cox proportional hazards model. In conclusion, Ki-67 index can be considered as a valuable independent prognostic biomarker for adult patients with MB.

[Inpatient Salivary Gland Surgery in Germany: A DRG-Based Nationwide Analysis, 2007-2011].

PubMed

Jensen, J E; Schlattmann, P; Guntinas-Lichius, O

2016-09-01

This is the first population-based analysis of inpatient salivary gland surgery across Germany. Nationwide Diagnosis-Related Groups (DRG) statistics for 2007 to 2011 were analyzed regarding indications for salivary gland surgery based on ICD-10 codes. Age specific surgery rates were calculated for both sexes. Inpatient salivary gland surgical rates in 2007-2011 amounted for incisions (OPS [Classification of Operations and Procedures] code 5-260) 1.43 per 100 000 population, for excisions (5-261) 2.06 per 100 000, for salivary gland resections (5-262) 2.06 per 100 000, and for external incisions (5-270) 0.43 per 100 000. Regarding the mentioned four OPS codes, the surgical rates for benign tumors accounted to 10.08 per 100 000, for sialadenitis (without sialoliths) to 4.00 per 100 000, for malignant tumors to 3.90 per 100 000, and for sialolithiasis to 2.09 per 100 000. The increase of surgical rates from 2007 to 2011 was significant for malignant and benign tumors as well as for salivary stones. The surgical rates were highest for patients>60 years. Especially surgery for malignant tumors was more frequent than expected. In spite of the introduction of minimal invasive technique the rates for salivary gland resections in case of sialadenitis or sialolithiasis still seem to be high. © Georg Thieme Verlag KG Stuttgart · New York.

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

Medulloblastoma in the Molecular Era

PubMed Central

Miranda Kuzan-Fischer, Claudia; Juraschka, Kyle; Taylor, Michael D.

2018-01-01

Medulloblastoma is the most common malignant brain tumor of childhood and remains a major cause of cancer related mortality in children. Significant scientific advancements have transformed the understanding of medulloblastoma, leading to the recognition of four distinct clinical and molecular subgroups, namely wingless (WNT), sonic hedgehog, group 3, and group 4. Subgroup classification combined with the recognition of subgroup specific molecular alterations has also led to major changes in risk stratification of medulloblastoma patients and these changes have begun to alter clinical trial design, in which the newly recognized subgroups are being incorporated as individualized treatment arms. Despite these recent advancements, identification of effective targeted therapies remains a challenge for several reasons. First, significant molecular heterogeneity exists within the four subgroups, meaning this classification system alone may not be sufficient to predict response to a particular therapy. Second, the majority of novel agents are currently tested at the time of recurrence, after which significant selective pressures have been exerted by radiation and chemotherapy. Recent studies demonstrate selection of tumor sub-clones that exhibit genetic divergence from the primary tumor, exist within metastatic and recurrent tumor populations. Therefore, tumor resampling at the time of recurrence may become necessary to accurately select patients for personalized therapy. PMID:29742881

Medulloblastoma in the Molecular Era.

PubMed

Miranda Kuzan-Fischer, Claudia; Juraschka, Kyle; Taylor, Michael D

2018-05-01

Medulloblastoma is the most common malignant brain tumor of childhood and remains a major cause of cancer related mortality in children. Significant scientific advancements have transformed the understanding of medulloblastoma, leading to the recognition of four distinct clinical and molecular subgroups, namely wingless (WNT), sonic hedgehog, group 3, and group 4. Subgroup classification combined with the recognition of subgroup specific molecular alterations has also led to major changes in risk stratification of medulloblastoma patients and these changes have begun to alter clinical trial design, in which the newly recognized subgroups are being incorporated as individualized treatment arms. Despite these recent advancements, identification of effective targeted therapies remains a challenge for several reasons. First, significant molecular heterogeneity exists within the four subgroups, meaning this classification system alone may not be sufficient to predict response to a particular therapy. Second, the majority of novel agents are currently tested at the time of recurrence, after which significant selective pressures have been exerted by radiation and chemotherapy. Recent studies demonstrate selection of tumor sub-clones that exhibit genetic divergence from the primary tumor, exist within metastatic and recurrent tumor populations. Therefore, tumor resampling at the time of recurrence may become necessary to accurately select patients for personalized therapy.

Biomolecular pathogenesis of borderline ovarian tumors: focusing target discovery through proteogenomics.

PubMed

Vergara, D; Tinelli, A; Martignago, R; Malvasi, A; Chiuri, V E; Leo, G

2010-02-01

Tumors of the epithelial surface account for about 80% of all ovarian neoplasms and exhibit a heterogeneous histological classification affecting survival. Tumors of low malignant potential, defined as borderline ovarian tumors(BOTs), have a markedly better survival and low recurrence, even if surgery still represents the common management for this type of cancer. It is still debated in the literature if BOTs can be considered as intermediate precursors in the progression to high grade ovarian tumors. Evidences now propose that high-grade serous carcinomas are not associated with a defined precursor lesion. Together with histopathological studies, mutations of KRAS, BRAF and p53 genes, microsatellite instability (MSI)and under- or over-expression of many genes and proteins have been used to address this question. Despite the large body of data summarized, a limited number of molecules proved to be useful in elucidating BOTs pathogenesis and only a few of these showed possible application in the therapy. We believe that high-throughput technologies would help to overcome these limitations offering the promise of a better understanding of BOTs classification. The aim is to guide the diagnosis and prognosis of BOTs to develop new possible therapeutic molecular targets avoiding surgery.

Breast cancer - one term, many entities?

PubMed

Bertos, Nicholas R; Park, Morag

2011-10-01

Breast cancer, rather than constituting a monolithic entity, comprises heterogeneous tumors with different clinical characteristics, disease courses, and responses to specific treatments. Tumor-intrinsic features, including classical histological and immunopathological classifications as well as more recently described molecular subtypes, separate breast tumors into multiple groups. Tumor-extrinsic features, including microenvironmental configuration, also have prognostic significance and further expand the list of tumor-defining variables. A better understanding of the features underlying heterogeneity, as well as of the mechanisms and consequences of their interactions, is essential to improve targeting of existing therapies and to develop novel agents addressing specific combinations of features.

Pathobiology of germ cell tumors - applying the gossip test!

PubMed

Looijenga, Leendert H J; Oosterhuis, J Wolter

2013-01-01

Residual mature teratoma, a frequent finding in clinical pathology since the introduction of cisplatin-based chemotherapy, put Wolter Oosterhuis on the track of germ cell tumors (GCTs). These neoplasms in the borderland between developmental biology and oncology have fascinated him ever since. He tells the story on how GCTs brought him in contact with leading investigators in the field like Ivan Damjanov, Peter Andrews, and Niels Skakkebaek. His fruitful line of research was made possible through a longstanding collaboration with Bauke de Jong and, to this day, Leendert Looijenga who joined his group as a student in 1988. Probably their most important contribution to the field of GCTs is an integrated approach to GCTs, combining epidemiology, pathology, (cyto)genetics and molecular biology, that has resulted in a pathobiology-based classification of GCTs in five types. It has clinical relevance and stimulates further research on these intriguing neoplasms and their corresponding animal models.

Metabolic impact of partial volume correction of [18F]FDG PET-CT oncological studies on the assessment of tumor response to treatment.

PubMed

Stefano, A; Gallivanone, F; Messa, C; Gilardi, M C; Gastiglioni, I

2014-12-01

The aim of this work is to evaluate the metabolic impact of Partial Volume Correction (PVC) on the measurement of the Standard Uptake Value (SUV) from [18F]FDG PET-CT oncological studies for treatment monitoring purpose. Twenty-nine breast cancer patients with bone lesions (42 lesions in total) underwent [18F]FDG PET-CT studies after surgical resection of breast cancer primitives, and before (PET-II) chemotherapy and hormone treatment. PVC of bone lesion uptake was performed on the two [18F]FDG PET-CT studies, using a method based on Recovery Coefficients (RC) and on an automatic measurement of lesion metabolic volume. Body-weight average SUV was calculated for each lesion, with and without PVC. The accuracy, reproducibility, clinical feasibility and the metabolic impact on treatment response of the considered PVC method was evaluated. The PVC method was found clinically feasible in bone lesions, with an accuracy of 93% for lesion sphere-equivalent diameter >1 cm. Applying PVC, average SUV values increased, from 7% up to 154% considering both PET-I and PET-II studies, proving the need of the correction. As main finding, PVC modified the therapy response classification in 6 cases according to EORTC 1999 classification and in 5 cases according to PERCIST 1.0 classification. PVC has an important metabolic impact on the assessment of tumor response to treatment by [18F]FDG PET-CT oncological studies.

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.

Integrated Proteomic and Transcriptomic-Based Approaches to Identifying Signature Biomarkers and Pathways for Elucidation of Daoy and UW228 Subtypes.

PubMed

Higdon, Roger; Kala, Jessie; Wilkins, Devan; Yan, Julia Fangfei; Sethi, Manveen K; Lin, Liang; Liu, Siqi; Montague, Elizabeth; Janko, Imre; Choiniere, John; Kolker, Natali; Hancock, William S; Kolker, Eugene; Fanayan, Susan

2017-02-03

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Patient survival has remained largely the same for the past 20 years, with therapies causing significant health, cognitive, behavioral and developmental complications for those who survive the tumor. In this study, we profiled the total transcriptome and proteome of two established MB cell lines, Daoy and UW228, using high-throughput RNA sequencing (RNA-Seq) and label-free nano-LC-MS/MS-based quantitative proteomics, coupled with advanced pathway analysis. While Daoy has been suggested to belong to the sonic hedgehog (SHH) subtype, the exact UW228 subtype is not yet clearly established. Thus, a goal of this study was to identify protein markers and pathways that would help elucidate their subtype classification. A number of differentially expressed genes and proteins, including a number of adhesion, cytoskeletal and signaling molecules, were observed between the two cell lines. While several cancer-associated genes/proteins exhibited similar expression across the two cell lines, upregulation of a number of signature proteins and enrichment of key components of SHH and WNT signaling pathways were uniquely observed in Daoy and UW228, respectively. The novel information on differentially expressed genes/proteins and enriched pathways provide insights into the biology of MB, which could help elucidate their subtype classification.

Molecular Testing of Brain Tumor

PubMed Central

Park, Sung-Hye; Won, Jaekyung; Kim, Seong-Ik; Lee, Yujin; Park, Chul-Kee; Kim, Seung-Ki; Choi, Seung-Hong

2017-01-01

The World Health Organization (WHO) classification of central nervous system (CNS) tumors was revised in 2016 with a basis on the integrated diagnosis of molecular genetics. We herein provide the guidelines for using molecular genetic tests in routine pathological practice for an accurate diagnosis and appropriate management. While astrocytomas and IDH-mutant (secondary) glioblastomas are characterized by the mutational status of IDH, TP53, and ATRX, oligodendrogliomas have a 1p/19q codeletion and mutations in IDH, CIC, FUBP1, and the promoter region of telomerase reverse transcriptase (TERTp). IDH-wildtype (primary) glioblastomas typically lack mutations in IDH, but are characterized by copy number variations of EGFR, PTEN, CDKN2A/B, PDGFRA, and NF1 as well as mutations of TERTp. High-grade pediatric gliomas differ from those of adult gliomas, consisting of mutations in H3F3A, ATRX, and DAXX, but not in IDH genes. In contrast, well-circumscribed low-grade neuroepithelial tumors in children, such as pilocytic astrocytoma, pleomorphic xanthoastrocytoma, and ganglioglioma, often have mutations or activating rearrangements in the BRAF, FGFR1, and MYB genes. Other CNS tumors, such as ependymomas, neuronal and glioneuronal tumors, embryonal tumors, meningothelial, and other mesenchymal tumors have important genetic alterations, many of which are diagnostic, prognostic, and predictive markers and therapeutic targets. Therefore, the neuropathological evaluation of brain tumors is increasingly dependent on molecular genetic tests for proper classification, prediction of biological behavior and patient management. Identifying these gene abnormalities requires cost-effective and high-throughput testing, such as next-generation sequencing. Overall, this paper reviews the global guidelines and diagnostic algorithms for molecular genetic testing of brain tumors. PMID:28535583

Neuroblastoma in children: Update on clinicopathologic and genetic prognostic factors.

PubMed

Ahmed, Atif A; Zhang, Lei; Reddivalla, Naresh; Hetherington, Maxine

2017-04-01

Neuroblastoma is the most common extracranial solid tumor in childhood accounting for 8-10% of all childhood malignancies. The tumor is characterized by a spectrum of histopathologic features and a heterogeneous clinical phenotype. Modern multimodality therapy results in variable clinical response ranging from cure in localized tumors to limited response in aggressive metastatic disease. Accurate clinical staging and risk assessment based on clinical, surgical, biologic and pathologic criteria are of pivotal importance in assigning prognosis and planning effective treatment approaches. Numerous studies have analyzed the presence of several clinicopathologic and biologic factors in association with the patient's prognosis and outcome. Although patient's age, tumor stage, histopathologic classification, and MYCN amplification are the most commonly validated prognostic markers, several new gene mutations have been identified in sporadic and familial neuroblastoma cases that show association with an adverse outcome. Novel molecular studies have also added data on chromosomal segmental aberrations in MYCN nonamplified tumors. In this review, we provide an updated summary of the clinical, serologic and genetic prognostic indicators in neuroblastoma including classic factors that have consistently played a role in risk stratification of patients as well as newly discovered biomarkers that may show a potential significance in patients' management.

Management of anorectal melanoma: report of 17 cases and literature review.

PubMed

Belbaraka, Rhizlane; Elharroudi, Tijani; Ismaili, Nabil; Fetohi, Mohammed; Tijami, Fouad; Jalil, Abdelouahed; Errihani, Hassan

2012-03-01

Primary anorectal melanoma is a rare and aggressive disease. It accounts for 0.5% of all rectal tumors. They are very agressive tumors with poor prognosis. The aim of this study is to report the clinical and evolutionary profile and therapeutical approach of these tumors. A retrospective study of 17 patients with anorectal melanoma diagnosed between January 1998 and December 2007 was performed. The signs and symptoms, diagnostic study, and surgical and medical treatments were analyzed. The average age was 58 years. Sex ratio was 12 men per five women. Patients had symptoms present for an average of 6 months. The most common symptom was rectal bleeding. According to Slingluff classification, five patients had stage I (localized tumor), four cases had stage II (regional nodes metastasis), and eight cases had stage III (distant metastasis). Seven patients have radical surgery. Only two patients received adjuvant immunotherapy. Eight patients received palliative chemotherapy based on dacarbazine or cisplatinum. The median survival was 8 months. Prognosis of anorectal melanoma is still very poor. However, some patients when treated by radical resection may experience long-term survival. The use of adjuvant immunotherapy needs large collaborative studies in view of the rarity of the tumor.

Clinical characteristics and oncological outcomes of testicular cancer patients registered in 2005 and 2008: the first large-scale study from the Cancer Registration Committee of the Japanese Urological Association.

PubMed

Miki, Tsuneharu; Kamoi, Kazumi; Fujimoto, Hiroyuki; Kanayama, Hiro-omi; Ohyama, Chikara; Suzuki, Kazuhiro; Nishiyama, Hiroyuki; Eto, Masatoshi; Naito, Seiji; Fukumori, Tomoharu; Kubota, Yoshinobu; Takahashi, Satoru; Mikami, Kazuya; Homma, Yukio

2014-08-01

To describe the clinical and pathological characteristics and oncological outcomes of testicular cancer diagnosed in Japan, we report the results of the testicular cancer registration carried out by the Japanese Urological Association. Testicular cancer survey was conducted by the Japanese Urological Association in 2011 to register newly diagnosed testicular cancers in 2005 and 2008. The survey included details such as age, presenting symptoms, physical examination findings, tumor markers, histopathology, clinical stage, initial treatment and clinical outcomes. We analyzed 1121 cases of testicular primary germ cell tumor among 1157 registered patients. The median age was 37.0 years. Seminomas and non-seminomatous germ cell tumors accounted for 61.9% and 38.1%, respectively. Measurements of tumor markers were documented in 98.6% of the patients; however, there was an unsatisfactory uniform measurement of human chorionic gonadotropin, which made it difficult to evaluate the International Germ Cell Consensus Classification in all patients. The 1- and 3-year overall survival rates from the entire cohort were 98.3% and 96.8%, respectively. According to the International Germ Cell Consensus Classification, 3-year overall survival rates in the good, intermediate, and poor prognosis group were 99.1%, 100% and 79.9%, respectively. The present report is the first large-scale study of the characteristics and survival of testicular cancer patients in Japan based on multi-institutional registry data, and showed a good prognosis even in an advanced stage. The improved survival attributed substantially to accurate diagnosis and effective multimodal treatment. © 2014 The Japanese Urological Association.

Cancer of the esophagus and esophagogastric junction: data-driven staging for the seventh edition of the American Joint Committee on Cancer/International Union Against Cancer Cancer Staging Manuals.

PubMed

Rice, Thomas W; Rusch, Valerie W; Ishwaran, Hemant; Blackstone, Eugene H

2010-08-15

Previous American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) stage groupings for esophageal cancer have not been data driven or harmonized with stomach cancer. At the request of the AJCC, worldwide data from 3 continents were assembled to develop data-driven, harmonized esophageal staging for the seventh edition of the AJCC/UICC cancer staging manuals. All-cause mortality among 4627 patients with esophageal and esophagogastric junction cancer who underwent surgery alone (no preoperative or postoperative adjuvant therapy) was analyzed by using novel random forest methodology to produce stage groups for which survival was monotonically decreasing, distinctive, and homogeneous. For lymph node-negative pN0M0 cancers, risk-adjusted 5-year survival was dominated by pathologic tumor classification (pT) but was modulated by histopathologic cell type, histologic grade, and location. For lymph node-positive, pN+M0 cancers, the number of cancer-positive lymph nodes (a new pN classification) dominated survival. Resulting stage groupings departed from a simple, logical arrangement of TNM. Stage groupings for stage I and II adenocarcinoma were based on pT, pN, and histologic grade; and groupings for squamous cell carcinoma were based on pT, pN, histologic grade, and location. Stage III was similar for histopathologic cell types and was based only on pT and pN. Stage 0 and stage IV, by definition, were categorized as tumor in situ (Tis) (high-grade dysplasia) and pM1, respectively. The prognosis for patients with esophageal and esophagogastric junction cancer depends on the complex interplay of TNM classifications as well as nonanatomic factors, including histopathologic cell type, histologic grade, and cancer location. These features were incorporated into a data-driven staging of these cancers for the seventh edition of the AJCC/UICC cancer staging manuals. Copyright (c) 2010 American Cancer Society.

Spectroscopic diagnosis of laryngeal carcinoma using near-infrared Raman spectroscopy and random recursive partitioning ensemble techniques.

PubMed

Teh, Seng Khoon; Zheng, Wei; Lau, David P; Huang, Zhiwei

2009-06-01

In this work, we evaluated the diagnostic ability of near-infrared (NIR) Raman spectroscopy associated with the ensemble recursive partitioning algorithm based on random forests for identifying cancer from normal tissue in the larynx. A rapid-acquisition NIR Raman system was utilized for tissue Raman measurements at 785 nm excitation, and 50 human laryngeal tissue specimens (20 normal; 30 malignant tumors) were used for NIR Raman studies. The random forests method was introduced to develop effective diagnostic algorithms for classification of Raman spectra of different laryngeal tissues. High-quality Raman spectra in the range of 800-1800 cm(-1) can be acquired from laryngeal tissue within 5 seconds. Raman spectra differed significantly between normal and malignant laryngeal tissues. Classification results obtained from the random forests algorithm on tissue Raman spectra yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification. The random forests technique also provided variables importance that facilitates correlation of significant Raman spectral features with cancer transformation. This study shows that NIR Raman spectroscopy in conjunction with random forests algorithm has a great potential for the rapid diagnosis and detection of malignant tumors in the larynx.

Study design requirements for RNA sequencing-based breast cancer diagnostics.

PubMed

Mer, Arvind Singh; Klevebring, Daniel; Grönberg, Henrik; Rantalainen, Mattias

2016-02-01

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.

Tumor Vaccination With Cytokine-Loaded Microspheres

DTIC Science & Technology

2005-12-01

indirect effects of IFN-gamma. J Immunol. 2003;170:400–412. 23. Yan J, Vetvicka V, Xia Y, et al. Beta - glucan , a ‘‘specific’’ biologic response...12, GM-CSF, Breast Cancer , Spontaneous tumors 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF... cancer vaccines, cytokines, adjuvants, immunotherapy, tumor models (J Immunother 2006;29:10–20) I t is now well established that numerous immune

Monitoring of Breast Tumor Response to Local Chemotherapeutic Agent Delivered by Biodegradable Fibers

DTIC Science & Technology

2005-05-01

AD_ Award Number: DAMD17-03-1-0353 TITLE: Monitoring of Breast Tumor Response to Local Chemotherapeutic Agent Delivered by Biodegradable Fibers...30 Apr 2005 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Monitoring of Breast Tumor Response to Local Chemotherapeutic Agent Delivered by Biodegradable ... biodegradable fiber 16. SECURITY CLASSIFICATION OF: 17. LIMITATION 18. NUMBER 19a. NAME OF RESPONSIBLE PERSON OF ABSTRACT OF PAGES a. REPORT b. ABSTRACT c

[A Case of Duodenal Invasive Advanced Gastric Cancer in Which the Primary Tumor Achieved pCR, but Viable Cancer Cells Remained in Lymph Node No.13 after Neoadjuvant Chemotherapy].

PubMed

Kubota, Tetsushi; Choda, Yasuhiro; Morito, Toshiaki; Miyake, Soichiro; Ishida, Michihiro; Sato, Daisuke; Sumitani, Daisuke; Nakano, Kanyu; Harano, Masao; Matsukawa, Hiroyoshi; Ojima, Yasutomo; Idani, Hitoshi; Shiozaki, Shigehiro; Okajima, Masazumi

2017-11-01

A woman approximately 70-years-old with duodenal invasive advanced gastric cancer was referred to our hospital. Meta- stasis to lymph node(LN)No.13 was suspected based on FDG/PET-CT. For better curability, we selected neoadjuvant chemotherapy( NAC)with S-1 plus oxaliplatin(SOX therapy). After 3 courses of SOX, distal gastrectomy with D2(+No.13) lymphadenectomy was performed. Upon pathological evaluation, no viable cancer cells were found in the primary tumor, but viable cancer cells were identified in LN No.6 and 13. LN No.13 was defined as M1 according to the current Japanese classification of gastric carcinoma. On the other hand, the 2014 Japanese gastric cancer treatment guidelines(ver. 4)mentioned that D2(+No.13)lymphadenectomy may be an option in potentially curative gastrectomy for tumors invading the duodenum. This case suggests that No.13 lymphadenectomy is necessary as a curative operation for duodenal invasive advanced gastric cancer, even if the primary tumor has achieved pCR after NAC.

Surgical Strategies in Childhood Craniopharyngioma

PubMed Central

Flitsch, Jörg; Müller, Hermann Lothar; Burkhardt, Till

2011-01-01

Craniopharyngiomas are biologically benign lesions (WHO Grade 1) of the sellar and suprasellar region, associated with a serious morbidity. About 50% of these tumors become clinically apparent during childhood. Clinical symptoms include headaches, chiasm syndrome, hydrocephalus, pituitary insufficiencies, and obesity. Growth arrest is a typical symptom in children. The treatment of craniopharyngiomas includes surgery as well as radiotherapy. The goal of surgery varies according to the tumor location and extension and may range from complete resection to biopsy. Surgical complications are well known and cause constant evaluation of surgical strategies. Diencephalic obesity is related to surgical manipulation of hypothalamic tissue. Therefore, a classification system for craniopharyngiomas based on preoperative MRI is suggested by the authors. Recurrences are frequent in craniopharyngiomas, even after complete or gross-total resection. Radiotherapy is therefore recommended to patients with incomplete resections. However, the ideal time for radiotherapy after surgery is under discussion. The treatment of craniopharyngiomas requires an interdisciplinary and multimodal approach. Each patient should receive an individually tailored treatment. Surgically, different approaches as well as different degrees of resection can be considered, depending on tumor location and tumor extension. PMID:22645514

Spindle cell oncocytomas and granular cell tumors of the pituitary are variants of pituicytoma.

PubMed

Mete, Ozgur; Lopes, Maria Beatriz; Asa, Sylvia L

2013-11-01

Pituicytomas are neoplasms that arise from pituicytes, which are specialized glia of the posterior pituitary. Pituicytes have 5 ultrastructural variants: light, dark, granular, ependymal, and oncocytic. Granular cell tumors of the pituitary gland are thought to arise from granular pituicytes. Spindle cell oncocytomas are considered to arise from folliculostellate cells, which are sustentacular cells of the adenohypophysis. Recent data suggest that, whereas pituicytes and all 3 tumor types are positive for TTF-1, folliculostellate cells are negative for TTF-1. We investigated 7 spindle cell oncocytomas, 4 pituicytomas, and 3 granular cell tumors for their genetic (BRAF(V600E) mutation and BRAF-KIAA fusion), immunohistochemical (GFAP, vimentin, S100 protein, olig2, IDH1-R132H, NF, galectin-3, chromogranin-A, CD56, EMA, CAM5.2, CD68, TTF-1, and bcl-2), and ultrastructural features to refine their classification. All tumors had nuclear positivity for TTF-1 and were negative for CAM5.2, chromogranin-A, and NF. GFAP, vimentin, S100, galectin-3, EMA, and CD68 were variably positive in the majority of the 3 tumor groups. Olig2 was only positive in 1 pituicytoma. Whereas granular cell tumors were negative for bcl-2 and CD56, pituicytomas and spindle cell oncocytomas showed variable positivity. All tumors were negative with the IDH1-R132H mutation-specific antibody, and none had evidence of BRAF alterations (BRAF(V600E) mutation and BRAF-KIAA fusion). Diffuse TTF-1 expression in nontumorous pituicytes, pituicytomas, spindle cell oncocytomas, and granular cell tumors indicates a common pituicyte lineage. The ultrastructural variants of pituicytes are reflected in these 3 morphologic variants of tumors arising from these cells. We propose the terminology "oncocytic pituicytomas" and "granular cell pituicytomas" to refine the classification of these lesions.

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.

Identification of a subgroup with worse prognosis among patients with poor-risk testicular germ cell tumor.

PubMed

Kojima, Takahiro; Kawai, Koji; Tsuchiya, Kunihiko; Abe, Takashige; Shinohara, Nobuo; Tanaka, Toshiaki; Masumori, Naoya; Yamada, Shigeyuki; Arai, Yoichi; Narita, Shintaro; Tsuchiya, Norihiko; Habuchi, Tomonori; Nishiyama, Hiroyuki

2015-10-01

To clarify the significance of the International Germ Cell Cancer Collaborative Group classification in the 2000s, especially in intermediate- and poor-prognosis testicular germ cell tumor in Japan. We retrospectively analyzed 117 patients with intermediate- and poor-prognosis testicular non-seminomatous germ cell tumor treated at five university hospitals in Japan between 2000 and 2010. Data collected included age, levels of tumor markers, spread to non-pulmonary visceral metastases, treatment details and survival. The median follow-up period of all patients was 57 months. A total of 50 patients (43%) were classified as having intermediate prognosis, and 67 patients (57%) as poor prognosis according to the International Germ Cell Cancer Collaborative Group classification. As first-line chemotherapy, 92 patients (79%) received bleomycin, etoposide and cisplatin. Of all patients, 74 patients (63%) received second-line chemotherapy. The most commonly used second-line chemotherapy regimens were a combination of taxanes, ifosfamide and platinum in 49 cases (66%). Overall, 33 patients (28%) received third-line chemotherapy. A total of 88 patients (75%) underwent post-chemotherapy surgery. The 5-year overall survival for intermediate (n = 50) and poor prognosis (n = 67) was 89% and 83% (P = 0.21), respectively. In poor prognosis patients, patients with two or more risk factors (any of high lactic dehydrogenase, alpha-fetoprotein and human chorionic gonadotropin levels, and presence of non-pulmonary visceral metastases) had significantly worse survival than those with only one risk factor (71% and 91%, respectively, P = 0.01). The 5-year overall survivals of poor-prognosis testicular non-seminomatous germ cell tumor patients reached 83%. Further stratification of poor-prognosis patients based on a number of risk factors has the potential to further identify those with poorer prognosis. © 2015 The Japanese Urological Association.

Results of Fertility-Sparing Surgery for Expansile and Infiltrative Mucinous Ovarian Cancers.

PubMed

Gouy, Sebastien; Saidani, Marine; Maulard, Amandine; Bach-Hamba, Slim; Bentivegna, Enrica; Leary, Alexandra; Pautier, Patricia; Devouassoux-Shisheboran, Mojgan; Genestie, Catherine; Morice, Philippe

2018-03-01

No series had been reported focusing on the results of fertility-sparing surgery in stage I mucinous ovarian cancers according to histotype (infiltrative vs. expansile). Investigating such outcomes was the aim of the present study. The present study was a retrospective analysis of patients treated conservatively with preservation of the uterus and contralateral ovary from 1976 to 2016. The pathology of the tumors was reviewed by two expert pathologists according to the 2014 World Health Organization (WHO) classification criteria. Oncologic and fertility results were analyzed. Twenty-one patients fulfilled the inclusion criteria, twelve with expansile and nine with infiltrative cancer. All patients had a unilateral tumor and underwent unilateral salpingo-oophorectomy in one-step ( n  = 6) or two-step ( n  = 15) surgeries. All but one had complete peritoneal staging surgery based on cytology, omentectomy, and random peritoneal biopsies. Ten had nodal staging surgery. The International Federation of Gynecology and Obstetrics stages were IA ( n  = 9), IC1 ( n  = 6), and IC2 ( n  = 6); the nuclear grades were grade 1 ( n  = 9), grade 2 ( n  = 5), and grade 3 ( n  = 1). Two patients recurred (one expansile and one infiltrative type) 19 and 160 months after surgery, respectively. One stage IA, nuclear grade 2 expansile tumor recurred on the spared ovary; the patient remains alive. The other stage IA infiltrative tumor recurred as peritoneal spread; the patient is alive with disease. Six patients became pregnant; four with expansile tumors and two with infiltrative tumors. The type of mucinous cancer has no impact on the oncologic outcome in this series of patients treated conservatively. Fertility-sparing surgery should be considered for early-stage infiltrative-type tumors. According to the most recently updated World Health Organization classification guidelines, mucinous cancers should be classified as either expansile or infiltrative. The infiltrative type has a poorer prognosis, but there are no data about the safety of fertility-sparing surgery (FSS) in this context. A collection of 21 cases reviewed by two expert pathologists this study is the first devoted to the conservative treatment of mucinous tumors according to both subtypes. The key result was that the type of mucinous cancer has no impact on the oncologic outcome; thus, FSS may be considered in both subtypes. © AlphaMed Press 2017.

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

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

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

PubMed

Turkki, Riku; Linder, Nina; Kovanen, Panu E; Pellinen, Teijo; Lundin, Johan

2016-01-01

Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Molecular classification of gastric adenocarcinoma: translating new insights from the cancer genome atlas research network.

PubMed

Sunakawa, Yu; Lenz, Heinz-Josef

2015-04-01

Gastric cancer is a heterogenous cancer, which may be classified into several distinct subtypes based on pathology and epidemiology, each with different initiating pathological processes and each possibly having different tumor biology. A classification of gastric cancer should be important to select patients who can benefit from the targeted therapies or to precisely predict prognosis. The Cancer Genome Atlas (TCGA) study collaborated with previous reports regarding subtyping gastric cancer but also proposed a refined classification based on molecular characteristics. The addition of the new molecular classification strategy to a current classical subtyping may be a promising option, particularly stratification by Epstein-Barr virus (EBV) and microsatellite instability (MSI) statuses. According to TCGA study, EBV gastric cancer patients may benefit the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibodies or phosphoinositide 3-kinase (PI3K) inhibitors which are now being developed. The discoveries of predictive biomarkers should improve patient care and individualized medicine in the management since the targeted therapies may have the potential to change the landscape of gastric cancer treatment, moreover leading to both better understanding of the heterogeneity and better outcomes. Patient enrichment by predictive biomarkers for new treatment strategies will be critical to improve clinical outcomes. Additionally, liquid biopsies will be able to enable us to monitor in real-time molecular escape mechanism, resulting in better treatment strategies.

Pancreatic neuroendocrine tumour: Correlation of apparent diffusion coefficient or WHO classification with recurrence-free survival.

PubMed

Kim, Mimi; Kang, Tae Wook; Kim, Young Kon; Kim, Seong Hyun; Kwon, Wooil; Ha, Sang Yun; Ji, Sang A

2016-03-01

To evaluate the correlation between grade of pancreatic neuroendocrine tumours (pNETs) based on the 2010 World Health Organization (WHO) classification and the apparent diffusion coefficient (ADC), and to assess whether the ADC value and WHO classification can predict recurrence-free survival (RFS) after surgery for pNETs. This retrospective study was approved by the Institutional Review Board. The requirement for informed consent was waived. Between March 2009 and November 2014, forty-nine patients who underwent magnetic resonance (MR) imaging with diffusion-weighted image and subsequent surgery for single pNETs were included. Correlations among qualitative MR imaging findings, quantitative ADC values, and WHO classifications were assessed. An ordered logistic regression test was used to control for tumour size as a confounding factor. The association between ADC value (or WHO classification) and RFS was analysed. All tumors (n=49) were classified as low- (n=29, grade 1), intermediate- (n=17, grade 2), and high-grade (n=3, grade 3), respectively. The mean ADC of pNETs was moderately negatively correlated with WHO classification before and after adjustment for tumour size (ρ=-0.64, p<0.001 and ρ=-0.55, p=0.001 respectively). RFS was significantly associated with WHO classification (p=0.007), but not with the ADC value (p=0.569). The ADC value of pNETs is moderately correlated with WHO tumour grade, regardless of tumour size. However, the WHO tumour classification of pNET may be more suitable for predicting RFS than the ADC value. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

MRI textures as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma

NASA Astrophysics Data System (ADS)

Langenhuizen, P. P. J. H.; Legters, M. J. W.; Zinger, S.; Verheul, H. B.; Leenstra, S.; de With, P. H. N.

2018-02-01

Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co- occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85%, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.

Assessment of the correlation between serum prolidase and alpha-fetoprotein levels in patients with hepatocellular carcinoma

PubMed Central

Uygun Ilikhan, Sevil; Bilici, Muammer; Sahin, Hatice; Demir Akca, Ayşe Semra; Can, Murat; Oz, Ibrahim Ilker; Guven, Berrak; Buyukuysal, M Cagatay; Ustundag, Yucel

2015-01-01

AIM: To determine the predictive value of increased prolidase activity that reflects increased collagen turnover in patients with hepatocellular carcinoma (HCC). METHODS: Sixty-eight patients with HCC (mean age of 69.1 ± 10.1), 31 cirrhosis patients (mean age of 59.3 ± 6.3) and 33 healthy volunteers (mean age of 51.4 ± 12.6) were enrolled in this study. Univariate and multivariate analysis were used to evaluate the association of serum α-fetoprotein (AFP) values with HCC clinicopathological features, such as tumor size, number and presence of vascular and macrovascular invasion. The patients with HCC were divided into groups according to tumor size, number and presence of vascular invasion (diameters; ≤ 3 cm, 3-5 cm and ≥ 5 cm, number; 1, 2 and ≥ 3, macrovascular invasion; yes/no). Barcelona-clinic liver cancer (BCLC) criteria were used to stage HCC patients. Serum samples for measurement of prolidase and alpha-fetoprotein levels were kept at -80 °C until use. Prolidase levels were measured spectrophotometrically and AFP concentrations were determined by a chemiluminescence immunometric commercial diagnostic assay. RESULTS: In patients with HCC, prolidase and AFP values were evaluated according to tumor size, number, presence of macrovascular invasion and BCLC staging classification. Prolidase values were significantly higher in patients with HCC compared with controls (P < 0.001). Prolidase levels were significantly associated with tumor size and number (P < 0.001, P = 0.002, respectively). Prolidase levels also differed in patients in terms of BCLC staging classification (P < 0.001). Furthermore the prolidase levels in HCC patients showed a significant difference compared with patients with cirrhosis (P < 0.001). In HCC patients grouped according to tumor size, number and BCLC staging classification, AFP values differed separately (P = 0.032, P = 0.038, P = 0.015, respectively). In patients with HCC, there was a significant correlation (r = 0.616; P < 0.001) between prolidase and AFP values in terms of tumor size, number and BCLC staging classification, whereas the presence of macrovascular invasion did not show a positive association with serum prolidase and AFP levels. CONCLUSION: Considering the levels of both serum prolidase and AFP could contribute to the early diagnosing of hepatocellular carcinoma. PMID:26078578

Assessment of the correlation between serum prolidase and alpha-fetoprotein levels in patients with hepatocellular carcinoma.

PubMed

Ilikhan, Sevil Uygun; Bilici, Muammer; Sahin, Hatice; Akca, Ayşe Semra Demir; Can, Murat; Oz, Ibrahim Ilker; Guven, Berrak; Buyukuysal, M Cagatay; Ustundag, Yucel

2015-06-14

To determine the predictive value of increased prolidase activity that reflects increased collagen turnover in patients with hepatocellular carcinoma (HCC). Sixty-eight patients with HCC (mean age of 69.1 ± 10.1), 31 cirrhosis patients (mean age of 59.3 ± 6.3) and 33 healthy volunteers (mean age of 51.4 ± 12.6) were enrolled in this study. Univariate and multivariate analysis were used to evaluate the association of serum α-fetoprotein (AFP) values with HCC clinicopathological features, such as tumor size, number and presence of vascular and macrovascular invasion. The patients with HCC were divided into groups according to tumor size, number and presence of vascular invasion (diameters; ≤ 3 cm, 3-5 cm and ≥ 5 cm, number; 1, 2 and ≥ 3, macrovascular invasion; yes/no). Barcelona-clinic liver cancer (BCLC) criteria were used to stage HCC patients. Serum samples for measurement of prolidase and alpha-fetoprotein levels were kept at -80 °C until use. Prolidase levels were measured spectrophotometrically and AFP concentrations were determined by a chemiluminescence immunometric commercial diagnostic assay. In patients with HCC, prolidase and AFP values were evaluated according to tumor size, number, presence of macrovascular invasion and BCLC staging classification. Prolidase values were significantly higher in patients with HCC compared with controls (P < 0.001). Prolidase levels were significantly associated with tumor size and number (P < 0.001, P = 0.002, respectively). Prolidase levels also differed in patients in terms of BCLC staging classification (P < 0.001). Furthermore the prolidase levels in HCC patients showed a significant difference compared with patients with cirrhosis (P < 0.001). In HCC patients grouped according to tumor size, number and BCLC staging classification, AFP values differed separately (P = 0.032, P = 0.038, P = 0.015, respectively). In patients with HCC, there was a significant correlation (r = 0.616; P < 0.001) between prolidase and AFP values in terms of tumor size, number and BCLC staging classification, whereas the presence of macrovascular invasion did not show a positive association with serum prolidase and AFP levels. Considering the levels of both serum prolidase and AFP could contribute to the early diagnosing of hepatocellular carcinoma.

Contour classification in thermographic images for detection of breast cancer

NASA Astrophysics Data System (ADS)

Okuniewski, Rafał; Nowak, Robert M.; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Oleszkiewicz, Witold

2016-09-01

Thermographic images of breast taken by the Braster device are uploaded into web application which uses different classification algorithms to automatically decide whether a patient should be more thoroughly examined. This article presents the approach to the task of classifying contours visible on thermographic images of breast taken by the Braster device in order to make the decision about the existence of cancerous tumors in breast. It presents the results of the researches conducted on the different classification algorithms.

SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

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

Bredfeldt, J; Liu, Y; Conklin, M

Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). Thismore » group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.« less

Twenty-four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability

PubMed Central

Gao, Jianyong; Tian, Gang; Han, Xu; Zhu, Qiang

2018-01-01

Oral squamous cell carcinoma (OSCC) is the sixth most common type cancer worldwide, with poor prognosis. The present study aimed to identify gene signatures that could classify OSCC and predict prognosis in different stages. A training data set (GSE41613) and two validation data sets (GSE42743 and GSE26549) were acquired from the online Gene Expression Omnibus database. In the training data set, patients were classified based on the tumor-node-metastasis staging system, and subsequently grouped into low stage (L) or high stage (H). Signature genes between L and H stages were selected by disparity index analysis, and classification was performed by the expression of these signature genes. The established classification was compared with the L and H classification, and fivefold cross validation was used to evaluate the stability. Enrichment analysis for the signature genes was implemented by the Database for Annotation, Visualization and Integration Discovery. Two validation data sets were used to determine the precise of classification. Survival analysis was conducted followed each classification using the package ‘survival’ in R software. A set of 24 signature genes was identified based on the classification model with the Fi value of 0.47, which was used to distinguish OSCC samples in two different stages. Overall survival of patients in the H stage was higher than those in the L stage. Signature genes were primarily enriched in ‘ether lipid metabolism’ pathway and biological processes such as ‘positive regulation of adaptive immune response’ and ‘apoptotic cell clearance’. The results provided a novel 24-gene set that may be used as biomarkers to predict OSCC prognosis with high accuracy, which may be used to determine an appropriate treatment program for patients with OSCC in addition to the traditional evaluation index. PMID:29257303

Do we need a new classification of parotid gland surgery?

PubMed

Wierzbicka, Małgorzata; Piwowarczyk, Krzysztof; Nogala, Hanna; Błaszczyńska, Marzena; Kosiedrowski, Michał; Mazurek, Cezary

2016-06-30

In February 2016 the European Salivary Gland Society (ESGS) presented and recommended classification of parotidectomies based on the anatomical I-V level division of parotid gland. The main goal of this paper is to present the new classification, and to answer the question if it is more precise compared to classic one. 607 patients (315 man, 292 women) operated on for parotid tumours in a tertiary referral centre, Department of Otolaryngology, Head and Neck Surgery, Medical University of Poznań (502 benign and 105 malignant tumours). Parotid surgery descriptions provided by retrospective analysis of all operating protocols covering the years 2006-2015 were "translated" into the new classification proposed by the ESGS. Analysis of operating protocols and fitting them into the new classification proposed by the ESGS show some discrepancies, in both benign and malignant tumours. Based on the re-evaluation of 607 cases, in 94 procedures for benign tumors the only information available was that "surgery was performed within the superficial lobe". Thus, the new classification forces the surgeon to be much more precise than previously. In 3 cases the whole superficial lobe was removed, together with the upper part of the deep lobe. Because the classification lacked parotidectomy I-II-IV, it indicated that the new classification was insufficient in the aforementioned three cases. In 6 cases of ECD more than one parotid gland tumour was removed. Among malignant tumours, total parotidectomy was the predominant procedure. In 3/13 cases of expanded parotidectomy the temporomandibular joint (TMJ) was additionally removed and it seems that the acronym TMJ should be included among the additional resected structures. It is also necessary to supplement the description of the treatment with casuistically resected anatomical structures for oncological purposes (RT planning) and follow-up imaging. Currently, since 2015 in Poland there has been the National Cancer Registry of benign salivary gland tumours (https://guzyslinianek.pcss.pl). New surgical anatomy and classification based on it will be very helpful in unequivocal, albeit brief and not laborious, reporting of procedures. To summarize, the classification is: easy to use, precise, and forced the surgeon to make a detailed description saving time at the same time. Although it is broad and accurate, it did not cover all clinically rare cases, multiple foci and it does not contain key information about the rupture of the tumour's capsule, so it is necessary to complement the type of surgery by this annotations. The simple, clear and comprehensive classification is especially valuable for centres that lead registration. Thus, we are personally grateful for this new classification, which facilitates multicentre communication.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

PubMed

Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan

2018-04-01

We investigated the association between the textural features obtained from 18 F-FDG images, metabolic parameters (SUVmax , SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

[Gastrointestinal stromal tumors: clinical considerations].

PubMed

Castronovo, G; Ciulla, A; Tomasello, G; Urso, G; Damiani, S

2003-01-01

Gastrointestinal stromal tumors (61ST) are an heterogeneous group of non epithelial tumors of the gastrointestinal tract. They are peculiar to extreme cellular variability and uncertain malignancy. Gist are rare tumors that arise from primitive mesenchymal cells located in all gastrointestinal tract. Till now they are object of discussion about their origin, diagnostic standards, prognostic factors, histopathological classification. They are more frequently in over 40 years old people without difference in two sex, but they can appear in the child too and in the young man suffering from HIV. The authors relate two cases of recent observation, and discuss on the biological behaviour of these rare tumors.

Targeting Tumor Oct4 to Deplete Prostate Tumor- and Metastasis-Initiating Cells

DTIC Science & Technology

2015-10-01

and stem cell To investigate whether POU5F1B overrxpression can induce cancer stem cell -related genes expression, we did cancer stem cell ...future 15. SUBJECT TERMS OCT4, cancer stem cells , prostate cancer, metastasis, tumor formation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT...described in last report. Here we describe some findings previously not reported. 1.1 POU5F1B expression in prostatic tissue As cancer stem cell marker

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering.

PubMed

Yang, Guang; Raschke, Felix; Barrick, Thomas R; Howe, Franklyn A

2015-09-01

To investigate whether nonlinear dimensionality reduction improves unsupervised classification of (1) H MRS brain tumor data compared with a linear method. In vivo single-voxel (1) H magnetic resonance spectroscopy (55 patients) and (1) H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With (1) H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of (1) H MRSI data after cluster analysis. © 2014 Wiley Periodicals, Inc.

Relationship between clinical characteristics and survival of gastroenteropancreatic neuroendocrine neoplasms: A single-institution analysis (1995-2012) in South China.

PubMed

Wang, Yu-Hong; Lin, Yuan; Xue, Ling; Wang, Jin-Hui; Chen, Min-Hu; Chen, Jie

2012-11-29

Gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) is the most common type of neuroendocrine tumors accounting for 65-75% of neuroendocrine neoplasms (NENs). Given the fact that there are few studies on GEP-NENs among Chinese patients, we performed a retrospective study in South China. Totally 178 patients with GEP-NENs treated at the First Affiliated Hospital of Sun Yat-sen University between January 1995 and May 2012 were analyzed retrospectively. Pancreas was found the most common site of involvement (34.8%). 149 patients (83.7%) presented as non-functional tumors with non-specific symptoms such as abdominal pain (33.7%); carcinoid syndrome was not found in this study. Several methods are useful for localization of GEP-NENs, yielding varied detection rates from 77.8% to 98.7%. Positive rates of chromogranin A (CgA) and synaptophysin (Syn) immunhistochemically were 69.1% and 90.2%, respectively. 87 patients (51.5%) had G1 tumors, 31(18.3%) G2 tumors and 51 (30.2%) G3 tumors. Neuroendocrine tumor (NET), neuroendocrine carcinoma (NEC) and mixed adenoendocrine carcinoma (MANEC) were 69.8%, 27.2% and 3.0%, respectively. 28.1% of patients presented with distant disease. Surgery was performed in 152 (85.4%) patients, and overall 5-year survival rate was 54.5%. Functionality, G1 grading and NET classification were associated with favorable prognosis in univariate analysis. Distant metastasis contributed to unfavorable prognosis of these tumors. Nonfunctional tumors with non-specific symptoms account for the majority of GEP-NENs. Diagnosis depends on pathological classification. Multidisciplinary treatments could help improve the outcome.

Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification in stage IB lung adenocarcinoma.

PubMed

Xu, C-h; Wang, W; Wei, Y; Hu, H-d; Zou, J; Yan, J; Yu, L-k; Yang, R-s; Wang, Y

2015-10-01

Patients with pathological stage IB lung adenocarcinoma have a variable prognosis, even if received the same treatment. This study investigated the prognostic value of the new International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) lung adenocarcinoma classification in resected stage IB lung adenocarcinoma. We identified 276 patients with pathological stage IB adenocarcinoma who had undergone surgical resection at the Nanjing Chest Hospital between 2005 and 2010. The histological subtypes of all patients were classified according to the 2011 IASLC/ATS/ERS international multidisciplinary lung adenocarcinoma classification. Kaplan-Meier and Cox regression analyses were used to analyze the correlation between the IASLC/ATS/ERS classification and patients' prognosis. Two hundred and seventy-six patients with pathological stage IB adenocarcinoma had an 86.2% 5-year overall survival (OS) and 80.4% 5-year disease-free survival (DFS). Patients with micropapillary and solid predominant tumors had a significantly worse OS and DFS as compared to those with other subtypes predominant tumors (p = 0.003 and 0.001). Multivariate analysis revealed that the new classification was an independent prognostic factor for both OS and DFS of pathological stage IB adenocarcinoma (p = 0.009 and 0.003). Our study revealed that the new IASLC/ATS/ERS classification was an independent prognostic factor of pathological stage IB adenocarcinoma. This new classification is valuable of screening out high risk patients to receive postoperative adjuvant therapy. Copyright © 2015. Published by Elsevier Ltd.

CMS-dependent prognostic impact of KRAS and BRAFV600E mutations in primary colorectal cancer.

PubMed

Smeby, J; Sveen, A; Merok, M A; Danielsen, S A; Eilertsen, I A; Guren, M G; Dienstmann, R; Nesbakken, A; Lothe, R A

2018-05-01

The prognostic impact of KRAS and BRAFV600E mutations in primary colorectal cancer (CRC) varies with microsatellite instability (MSI) status. The gene expression-based consensus molecular subtypes (CMSs) of CRC define molecularly and clinically distinct subgroups, and represent a novel stratification framework in biomarker analysis. We investigated the prognostic value of these mutations within the CMS groups. Totally 1197 primary tumors from a Norwegian series of CRC stage I-IV were analyzed for MSI and mutation status in hotspots in KRAS (codons 12, 13 and 61) and BRAF (codon 600). A subset was analyzed for gene expression and confident CMS classification was obtained for 317 samples. This cohort was expanded with clinical and molecular data, including CMS classification, from 514 patients in the publically available dataset GSE39582. Gene expression signatures associated with KRAS and BRAFV600E mutations were used to evaluate differential impact of mutations on gene expression among the CMS groups. BRAFV600E and KRAS mutations were both associated with inferior 5-year overall survival (OS) exclusively in MSS tumors (BRAFV600E mutation versus KRAS/BRAF wild-type: Hazard ratio (HR) 2.85, P < 0.001; KRAS mutation versus KRAS/BRAF wild-type: HR 1.30, P = 0.013). BRAFV600E-mutated MSS tumors were strongly enriched and associated with metastatic disease in CMS1, leading to negative prognostic impact in this subtype (OS: BRAFV600E mutation versus wild-type: HR 7.73, P = 0.001). In contrast, the poor prognosis of KRAS mutations was limited to MSS tumors with CMS2/CMS3 epithelial-like gene expression profiles (OS: KRAS mutation versus wild-type: HR 1.51, P = 0.011). The subtype-specific prognostic associations were substantiated by differential effects of BRAFV600E and KRAS mutations on gene expression signatures according to the MSI status and CMS group. BRAFV600E mutations are enriched and associated with metastatic disease in CMS1 MSS tumors, leading to poor prognosis in this subtype. KRAS mutations are associated with adverse outcome in epithelial (CMS2/CMS3) MSS tumors.

Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Clark, Kal; Czarnek, Nicholas M.; Shamsesfandabadi, Parisa; Peters, Katherine B.; Saha, Ashirbani

2017-03-01

Recent studies showed that genomic analysis of lower grade gliomas can be very effective for stratification of patients into groups with different prognosis and proposed specific genomic classifications. In this study, we explore the association of one of those genomic classifications with imaging parameters to determine whether imaging could serve a similar role to genomics in cancer patient treatment. Specifically, we analyzed imaging and genomics data for 110 patients from 5 institutions from The Cancer Genome Atlas and The Cancer Imaging Archive datasets. The analyzed imaging data contained preoperative FLAIR sequence for each patient. The images were analyzed using the in-house algorithms which quantify 2D and 3D aspects of the tumor shape. Genomic data consisted of a cluster of clusters classification proposed in a very recent and leading publication in the field of lower grade glioma genomics. Our statistical analysis showed that there is a strong association between the tumor cluster-of-clusters subtype and two imaging features: bounding ellipsoid volume ratio and angular standard deviation. This result shows high promise for the potential use of imaging as a surrogate measure for genomics in the decision process regarding treatment of lower grade glioma patients.

Identification of somatic mutations in cancer through Bayesian-based analysis of sequenced genome pairs

PubMed Central

2013-01-01

Background The field of cancer genomics has rapidly adopted next-generation sequencing (NGS) in order to study and characterize malignant tumors with unprecedented resolution. In particular for cancer, one is often trying to identify somatic mutations – changes specific to a tumor and not within an individual’s germline. However, false positive and false negative detections often result from lack of sufficient variant evidence, contamination of the biopsy by stromal tissue, sequencing errors, and the erroneous classification of germline variation as tumor-specific. Results We have developed a generalized Bayesian analysis framework for matched tumor/normal samples with the purpose of identifying tumor-specific alterations such as single nucleotide mutations, small insertions/deletions, and structural variation. We describe our methodology, and discuss its application to other types of paired-tissue analysis such as the detection of loss of heterozygosity as well as allelic imbalance. We also demonstrate the high level of sensitivity and specificity in discovering simulated somatic mutations, for various combinations of a) genomic coverage and b) emulated heterogeneity. Conclusion We present a Java-based implementation of our methods named Seurat, which is made available for free academic use. We have demonstrated and reported on the discovery of different types of somatic change by applying Seurat to an experimentally-derived cancer dataset using our methods; and have discussed considerations and practices regarding the accurate detection of somatic events in cancer genomes. Seurat is available at https://sites.google.com/site/seuratsomatic. PMID:23642077

Identification of somatic mutations in cancer through Bayesian-based analysis of sequenced genome pairs.

PubMed

Christoforides, Alexis; Carpten, John D; Weiss, Glen J; Demeure, Michael J; Von Hoff, Daniel D; Craig, David W

2013-05-04

The field of cancer genomics has rapidly adopted next-generation sequencing (NGS) in order to study and characterize malignant tumors with unprecedented resolution. In particular for cancer, one is often trying to identify somatic mutations--changes specific to a tumor and not within an individual's germline. However, false positive and false negative detections often result from lack of sufficient variant evidence, contamination of the biopsy by stromal tissue, sequencing errors, and the erroneous classification of germline variation as tumor-specific. We have developed a generalized Bayesian analysis framework for matched tumor/normal samples with the purpose of identifying tumor-specific alterations such as single nucleotide mutations, small insertions/deletions, and structural variation. We describe our methodology, and discuss its application to other types of paired-tissue analysis such as the detection of loss of heterozygosity as well as allelic imbalance. We also demonstrate the high level of sensitivity and specificity in discovering simulated somatic mutations, for various combinations of a) genomic coverage and b) emulated heterogeneity. We present a Java-based implementation of our methods named Seurat, which is made available for free academic use. We have demonstrated and reported on the discovery of different types of somatic change by applying Seurat to an experimentally-derived cancer dataset using our methods; and have discussed considerations and practices regarding the accurate detection of somatic events in cancer genomes. Seurat is available at https://sites.google.com/site/seuratsomatic.

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.

The incidence of satellite cysts in keratocystic odontogenic tumors.

PubMed

Pavelić, Boiidar; Katunarić, Marina; Segović, Sanja; Karadole, Maja Cimas; Katanec, Davor; Saban, Aida; Puhar, Ivan

2014-03-01

Renaming of the Odontogenic Keratocyst as the Keratocystic Odontogenic Tumor by the World Health Organization (WHO) is based on the aggressive nature of this lesion. Satellite cysts founded in the walls of the original cysts may give rise to a new lesion formation. The aim of this retrospecitve study was to identify the existence of specific features according incidence of satellite cysts and the pallisading of the basal layer of the epithelium and to establish their mutual correlation. The histopathologic data of Keratocystic Odontogenic Tumor on the basis of new WHO's classification (2005) were analized. Prominent palisade basal cell layer was found in 415 (94.75%) and partially absent palisade basal cell layer in 23 (5.25%) cases. Satellite cysts were presented in prominent palisade basal cell layer in 85 specimens (20.5%) and in cases with partial absent of the palisade basal layer in 3 spicemens (13%). The higher the frequency of pallisading was the higher the frequency of satellite cysts was (p > 0.05).

Suppression of NFkB by Tetrathiomolybdate Inhibits Tumor Angiogenesis and Enhances Apoptosis in Human Breast Cancers

DTIC Science & Technology

2005-05-01

to treat breast cancer. 15. SUBJECT TERMS NFkappaB , tetrathiomolybdate, breast cancer 16. SECURITY CLASSIFICATION OF: 17. LIMITATION 18. NUMBER 19a...Sonenshein, G. E. Aberrant nuclear factor-icB/Rel expression and the pathogen- HER-2/neu blocks tumor necrosis factor-induced apoptosis via the Akt /NF

Tumor classification in well-differentiated thyroid carcinoma and sentinel lymph node biopsy outcomes: a direct correlation.

PubMed

Maniakas, Anastasios; Forest, Veronique-Isabelle; Jozaghi, Yelda; Saliba, Joe; Hier, Michael P; Mlynarek, Alex; Tamilia, Michael; Payne, Richard J

2014-04-01

Predicting locoregional metastasis in well-differentiated thyroid carcinoma (WDTC) is a challenge for thyroid cancer surgeons. Sentinel lymph node biopsy (SLNB) has been shown to be an effective predictive tool. To our knowledge, primary tumor (T) classification has yet to be studied with regard to SLNB. We hypothesized that larger primary tumors would correlate with the rate of malignancy in SLNBs. A retrospective chart review was conducted on patients operated for WDTC at the McGill Thyroid Cancer Center over a 36-month period. Patients who underwent a total thyroidectomy and SLNB for WDTC were included in this study. A total of 311 patients were included and separated into two groups (236 negative and 75 positive SLNBs). Among patients with negative SLNBs, 65% had T1 primary tumors, 17% T2, 16% T3, and 2% T4, whereas 18% of patients with positive SLNBs had T1 primary tumors, 5% T2, 45% T3, and 32% T4 (p<0.001). Patients under the age of 45 years had a higher rate of positive SLNs (36% in those <45 years vs. 17% in those ≥ 45 years; p<0.001). Age (<45 years) and higher T category were found to be associated with a higher rate of positive SLNBs.

Segmentation of breast cancer cells positive 1+ and 3+ immunohistochemistry

NASA Astrophysics Data System (ADS)

Labellapansa, Ause; Muhimmah, Izzati; Indrayanti

2016-03-01

Breast cancer is a disease occurs as a result of uncontrolled cells growth. One examination method of breast cancer cells is using Immunohistochemistry (IHC) to determine status of Human Epidermal Growth Factor Receptor2 (HER2) protein. This study helps anatomic pathologist to determine HER2 scores using image processing techniques to obtain HER2 overexpression positive area percentages of 1+ and 3+ scores. This is done because the score of 0 is HER2 negative cells and 2+ scores have equivocal results, which means it could not be determined whether it is necessary to give targeted therapy or not. HER2 overexpression positive area percentage is done by dividing the area with a HER2 positive tumor area. To obtain better tumor area, repair is done by eliminating lymphocytes area which is not tumor area using morphological opening. Results of 10 images IHC scores of 1+ and 3+ and 10 IHC images testing without losing lymphocytes area in tumor area, has proven that the system has been able to provide an overall correct classification in accordance with the experts analysis. However by doing operation to remove non-tumor areas, classification can be done correctly 100% for scores of 3+ and 65% for scores of 1+.

Early tumor shrinkage served as a prognostic factor for patients with stage III non-small cell lung cancer treated with concurrent chemoradiotherapy.

PubMed

Wei, Min; Ye, Qingqing; Wang, Xuan; Wang, Men; Hu, Yan; Yang, Yonghua; Yang, Jiyuan; Cai, Jun

2018-05-01

Lung cancer is the most common cause of cancer death. About 80% of patients are diagnosed at stage III in the non-small cell lung cancer (NSCLC). It is extremely important to understand the progression of this disease which has low survival times despite the advancing treatment modalities. We aimed to investigate the relationship between early tumor shrinkage (ETS) after initial concurrent chemoradiotherapy (C-CRT) and survival outcome in patients with stage III (NSCLC). A retrospective review of 103 patients with stage III NSCLC who had received C-CRT from January 2006 to October 2011 was performed. Patients were treated with systemic chemotherapy regimen of Cisplatin/Vp-16 and concurrent thoracic radiotherapy at a median dose of 66 Gy (range 60-70 Gy). All patients received a computed tomography (CT) examination before treatment. Also subsequently, chest CT scans were performed with the same imaging parameters at approximately 5 weeks after the initiation of treatment. ETS is here stratified by a decrease in tumor size ≥30% and <30% in the longest dimension of the target lesion within 5 weeks. Of the 103 patients, 59 ones showed a 30% decrease in tumor size, and the rest displayed a decrease of <30%. ETS showed no significant correlation with age, T classification, N classification, histological classification, smoking status, G classification, EGFR status, or acute pulmonary toxicity. In the current retrospective clinical study, Kaplan-Meier curves showed that patients with ETS ≥ 30% had a better progression-free survival and overall survival. The univariate and multivariate Cox regression analyses indicated that ETS < 30% was associated with a significantly increased risk of cancer-related death (P < .05) in stage IIINSCLC. ETS may be served as a useful prognostic factor to predict the outcome of stage III NSCLC patients treated with CCRT.

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

Molecular Pathology: Predictive, Prognostic, and Diagnostic Markers in Uterine Tumors.

PubMed

Ritterhouse, Lauren L; Howitt, Brooke E

2016-09-01

This article focuses on the diagnostic, prognostic, and predictive molecular biomarkers in uterine malignancies, in the context of morphologic diagnoses. The histologic classification of endometrial carcinomas is reviewed first, followed by the description and molecular classification of endometrial epithelial malignancies in the context of histologic classification. Taken together, the molecular and histologic classifications help clinicians to approach troublesome areas encountered in clinical practice and evaluate the utility of molecular alterations in the diagnosis and subclassification of endometrial carcinomas. Putative prognostic markers are reviewed. The use of molecular alterations and surrogate immunohistochemistry as prognostic and predictive markers is also discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

A Distinct DNA Methylation Shift in a Subset of Glioma CpG Island Methylator Phenotypes during Tumor Recurrence.

PubMed

de Souza, Camila Ferreira; Sabedot, Thais S; Malta, Tathiane M; Stetson, Lindsay; Morozova, Olena; Sokolov, Artem; Laird, Peter W; Wiznerowicz, Maciej; Iavarone, Antonio; Snyder, James; deCarvalho, Ana; Sanborn, Zachary; McDonald, Kerrie L; Friedman, William A; Tirapelli, Daniela; Poisson, Laila; Mikkelsen, Tom; Carlotti, Carlos G; Kalkanis, Steven; Zenklusen, Jean; Salama, Sofie R; Barnholtz-Sloan, Jill S; Noushmehr, Houtan

2018-04-10

Glioma diagnosis is based on histomorphology and grading; however, such classification does not have predictive clinical outcome after glioblastomas have developed. To date, no bona fide biomarkers that significantly translate into a survival benefit to glioblastoma patients have been identified. We previously reported that the IDH mutant G-CIMP-high subtype would be a predecessor to the G-CIMP-low subtype. Here, we performed a comprehensive DNA methylation longitudinal analysis of diffuse gliomas from 77 patients (200 tumors) to enlighten the epigenome-based malignant transformation of initially lower-grade gliomas. Intra-subtype heterogeneity among G-CIMP-high primary tumors allowed us to identify predictive biomarkers for assessing the risk of malignant recurrence at early stages of disease. G-CIMP-low recurrence appeared in 9.5% of all gliomas, and these resembled IDH-wild-type primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, mesenchymal stem cell-like epigenomic phenotype, and genomic instability. Molecular abnormalities of longitudinal G-CIMP offer possibilities to defy glioblastoma progression. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Analysis of failure in patients with adenoid cystic carcinoma of the head and neck. An international collaborative study.

PubMed

Amit, Moran; Binenbaum, Yoav; Sharma, Kanika; Ramer, Naomi; Ramer, Ilana; Agbetoba, Abib; Miles, Brett; Yang, Xinjie; Lei, Delin; Bjøerndal, Kristine; Godballe, Christian; Mücke, Thomas; Wolff, Klaus-Dietrich; Fliss, Dan; Eckardt, André M; Copelli, Chiara; Sesenna, Enrico; Palmer, Frank; Patel, Snehal; Gil, Ziv

2014-07-01

Adenoid cystic carcinoma (ACC) is a locally aggressive tumor with a high prevalence of distant metastases. The purpose of this study was to identify independent predictors of outcome and to characterize the patterns of failure. An international retrospective review was conducted of 489 patients with ACC treated between 1985 and 2011 in 9 cancer centers worldwide. Five-year overall-survival (OS), disease-specific survival (DSS), and disease-free survival (DFS) were 76%, 80%, and 68%, respectively. Independent predictors of OS and DSS were: age, site, N classification, and presence of distant metastases. N classification, age, and bone invasion were associated with DFS on multivariate analysis. Age, tumor site, orbital invasion, and N classification were independent predictors of distant metastases. The clinical course of ACC is slow but persistent. Paranasal sinus origin is associated with the lowest distant metastases rate but with the poorest outcome. These prognostic estimates should be considered when tailoring treatment for patients with ACC. Copyright © 2013 Wiley Periodicals, Inc.

MUCI Facilitation of Growth in Chemically Induced Mammary Gland Tumors in Muc-1 Mutant and MUCI Transgenic Mice.

DTIC Science & Technology

1998-08-01

present grant proposed to initiate tumor development using chemical carcinogenesis. Pazos et al. (1991) demonstrated chemical induction of murine...latency of 154 ±19 days. Tumors were mammary adenocarcinomas of the B type of Dunn’s classification ( Pazos , 1991). My hypothesis for these studies was...in rats. Murine response to NMU is only briefly documented in the literature ( Pazos et al., 1991). Following the protocol for NMU induction of mammary

Targeting Therapy Resistant Tumor Vessels

DTIC Science & Technology

2008-08-01

No 6 C8161 s.c. xenografts No 5 K14-HPV16 skin cancer No 4 MDA-MB-435 orthotopic xenografts No 4 AGR TRAMP PIN lesions TRAMP PIN lesions Yes 18 TRAMP...CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18 . NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a. REPORT U b. ABSTRACT U c...Summary We developed three tumor models under this project: 4T1 mouse breast cancer and MDA-MB-435 human cancer xenograft tumors treated with anti

Optical Strategies for Studying Metastatic Mechanisms, Tumor Cell Detection and Treatment of Prostate Cancer

DTIC Science & Technology

2006-10-01

Treatment, Photodynamic Therapy, Biological Response 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18 . NUMBER OF PAGES 19a. NAME OF...photodynamic therapy in a rat prostate tumor model. Clin Cancer Res 2005;11:720-7. 18 . Fukumura D, Yuan F, Monsky WL, Chen Y, Jain RK. Effect of host...angiogenesis and microvascular functions in human breast cancer xenografts : Mammary fat pad versus cranial tumors. Clin Cancer Res 2002;8:1008-13. 20. Hasan T

Prognostication in eye cancer: the latest tumor, node, metastasis classification and beyond

PubMed Central

Kivelä, T; Kujala, E

2013-01-01

The tumour, node, metastasis (TNM) classification is a universal cancer staging system, which has been used for five decades. The current seventh edition became effective in 2010 and covers six ophthalmic sites: eyelids, conjunctiva, uvea, retina, orbit, and lacrimal gland; and five cancer types: carcinoma, sarcoma, melanoma, retinoblastoma, and lymphoma. The TNM categories are based on the anatomic extent of the primary tumour (T), regional lymph node metastases (N), and systemic metastases (M). The T categories of ophthalmic cancers are based on the size of the primary tumour and any invasion of periocular structures. The anatomic category is used to determine the TNM stage that correlates with survival. Such staging is currently implemented only for carcinoma of the eyelid and melanoma of the uvea. The classification of ciliary body and choroidal melanoma is the only one based on clinical evidence so far: a database of 7369 patients analysed by the European Ophthalmic Oncology Group. It spans a prognosis from 96% 5-year survival for stage I to 97% 5-year mortality for stage IV. The most accurate criterion for prognostication in uveal melanoma is, however, analysis of chromosomal alterations and gene expression. When such data are available, the TNM stage may be used for further stratification. Prognosis in retinoblastoma is frequently assigned by using an international classification, which predicts conservation of the eye and vision, and an international staging separate from the TNM system, which predicts survival. The TNM cancer staging manual is a useful tool for all ophthalmologists managing eye cancer. PMID:23258307

Malignant pineal germ-cell tumors: an analysis of cases from three tumor registries.

PubMed

Villano, J Lee; Propp, Jennifer M; Porter, Kimberly R; Stewart, Andrew K; Valyi-Nagy, Tibor; Li, Xinyu; Engelhard, Herbert H; McCarthy, Bridget J

2008-04-01

The exact incidence of pineal germ-cell tumors is largely unknown. The tumors are rare, and the number of patients with these tumors, as reported in clinical series, has been limited. The goal of this study was to describe pineal germ-cell tumors in a large number of patients, using data from available brain tumor databases. Three different databases were used: Surveillance, Epidemiology, and End Results (SEER) database (1973-2001); Central Brain Tumor Registry of the United States (CBTRUS; 1997-2001); and National Cancer Data Base (NCDB; 1985-2003). Tumors were identified using the International Classification of Diseases for Oncology, third edition (ICD-O-3), site code C75.3, and categorized according to histology codes 9060-9085. Data were analyzed using SAS/STAT release 8.2, SEER*Stat version 5.2, and SPSS version 13.0 software. A total of 1,467 cases of malignant pineal germ-cell tumors were identified: 1,159 from NCDB, 196 from SEER, and 112 from CBTRUS. All three databases showed a male predominance for pineal germ-cell tumors (>90%), and >72% of patients were Caucasian. The peak number of cases occurred in the 10- to 14-year age group in the CBTRUS data and in the 15- to 19-year age group in the SEER and NCDB data, and declined significantly thereafter. The majority of tumors (73%-86%) were germinomas, and patients with germinomas had the highest survival rate (>79% at 5 years). Most patients were treated with surgical resection and radiation therapy or with radiation therapy alone. The number of patients included in this study exceeds that of any study published to date. The proportions of malignant pineal germ-cell tumors and intracranial germ-cell tumors are in range with previous studies. Survival rates for malignant pineal germ-cell tumors are lower than results from recent treatment trials for intracranial germ-cell tumors, and patients that received radiation therapy in the treatment plan either with surgery or alone survived the longest.

An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.

PubMed

Nogueira, Mariana A; Abreu, Pedro H; Martins, Pedro; Machado, Penousal; Duarte, Hugo; Santos, João

2017-02-13

Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.

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.

Microsurgical Resection of Glomus Jugulare Tumors With Facial Nerve Reconstruction: 3-Dimensional Operative Video.

PubMed

Cândido, Duarte N C; de Oliveira, Jean Gonçalves; Borba, Luis A B

2018-05-08

Paragangliomas are tumors originating from the paraganglionic system (autonomic nervous system), mostly found at the region around the jugular bulb, for which reason they are also termed glomus jugulare tumors (GJT). Although these lesions appear to be histologically benign, clinically they present with great morbidity, especially due to invasion of nearby structures such as the lower cranial nerves. These are challenging tumors, as they need complex approaches and great knowledge of the skull base. We present the case of a 31-year-old woman, operated by the senior author, with a 1-year history of tinnitus, vertigo, and progressive hearing loss, that evolved with facial nerve palsy (House-Brackmann IV) 2 months before surgery. Magnetic resonance imaging and computed tomography scans demonstrated a typical lesion with intense flow voids at the jugular foramen region with invasion of the petrous and tympanic bone, carotid canal, and middle ear, and extending to the infratemporal fossa (type C2 of Fisch's classification for GJT). During the procedure the mastoid part of the facial nerve was identified involved by tumor and needed to be resected. We also describe the technique for nerve reconstruction, using an interposition graft from the great auricular nerve, harvested at the beginning of the surgery. We achieved total tumor resection with a remarkable postoperative course. The patient also presented with facial function after 6 months. The patient consented with publication of her images.

Assessing local stromal alterations in human ovarian cancer subtypes via second harmonic generation microscopy and analysis

NASA Astrophysics Data System (ADS)

Campbell, Kirby R.; Campagnola, Paul J.

2017-11-01

The collagen architecture in all human ovarian cancers is substantially remodeled, where these alterations are manifested in different fiber widths, fiber patterns, and fibril size and packing. Second harmonic generation (SHG) microscopy has differentiated normal tissues from high-grade serous (HGS) tumors with high accuracy; however, the classification between low-grade serous, endometrioid, and benign tumors was less successful. We postulate this is due to known higher genetic variation in these tissues relative to HGS tumors, which are genetically similar, and this results in more heterogeneous collagen remodeling in the respective matrix. Here, we examine fiber widths and SHG emission intensity and directionality locally within images (e.g., 10×10 microns) and show that normal tissues and HGS tumors are more uniform in fiber properties as well as in fibril size and packing than the other tissues. Moreover, these distributions are in good agreement with phase matching considerations relating SHG emission directionality and intensity. The findings show that in addition to average collagen assembly properties the intrinsic heterogeneity must also be considered as another aspect of characterization. These local analyses showed differences not shown in pure intensity-based image analyses and may provide further insight into disease etiology of the different tumor subtypes.

A diffuse mixed histiocytic-lymphocytic lymphoma associated with immunological abnormalities.

PubMed

Syrjänen, K J

1979-01-01

A diffuse generalized lymphoma histologically classified as mixed histiocytic-lymphocytic type and associated with profound immunologie abnormalities is reported. The patient had an autoimmune hemolytic anemia, an autoimmune thrombocytopenia, polyclonally increased IgG and IgM, polyclonal secretion of kappa and lamda chains into urine, very low serum complement C3 and antibodies against glomerulus and smooth muscle. When studied with the modern surface-marker techniques, the lesion was found to be composed of entirely lymphoid cells of the B-lymphocyte series. The proper classification of this tumor could be a primitive immunoblastic sarcoma. The relationship of the present tumor to the non-neoplastic angioimmunoblastic lymphadenopathia is discussed. The necessity of applying the surface-marker techniques in the classification of malignant lymphomas is emphasized.

Classification of Astrocytomas and Oligodendrogliomas from Mass Spectrometry Data Using Sparse Kernel Machines

PubMed Central

Huang, Jacob; Gholami, Behnood; Agar, Nathalie Y. R.; Norton, Isaiah; Haddad, Wassim M.; Tannenbaum, Allen R.

2013-01-01

Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample’s histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry. PMID:22256188

Central granular cell odontogenic tumor: Report of an unusual case.

PubMed

Madan, Mani; Chandra, Shaleen; Raj, Vineet; Madan, Rohit

2016-01-01

Central granular cell odontogenic tumor (CGCOT) is an unusual benign odontogenic neoplasm characterized by the presence of granular cells associated with apparently inactive odontogenic epithelium. These tumors tend to occur in the posterior mandible and usually present as well-defined unilocular or multilocular radiolucent lesions. So far, only <40 cases of CGCOT have been described in the literature under various terminologies. Though these tumors were not considered as distinct entity in the recent WHO classification of odontogenic tumors, long-term follow-up is recommended as malignant counterpart of CGCOT has already been reported. The main aim of this article is to report an additional case of CGCOT to the literature, occurring in a 73-year-old male.

Diffuse low-grade glioma: a review on the new molecular classification, natural history and current management strategies.

PubMed

Delgado-López, P D; Corrales-García, E M; Martino, J; Lastra-Aras, E; Dueñas-Polo, M T

2017-08-01

The management of diffuse supratentorial WHO grade II glioma remains a challenge because of the infiltrative nature of the tumor, which precludes curative therapy after total or even supratotal resection. When possible, functional-guided resection is the preferred initial treatment. Total and subtotal resections correlate with increased overall survival. High-risk patients (age >40, partial resection), especially IDH-mutated and 1p19q-codeleted oligodendroglial lesions, benefit from surgery plus adjuvant chemoradiation. Under the new 2016 WHO brain tumor classification, which now incorporates molecular parameters, all diffusely infiltrating gliomas are grouped together since they share specific genetic mutations and prognostic factors. Although low-grade gliomas cannot be regarded as benign tumors, large observational studies have shown that median survival can actually be doubled if an early, aggressive, multi-stage and personalized therapy is applied, as compared to prior wait-and-see policy series. Patients need an honest long-term therapeutic strategy that should ideally anticipate neurological, cognitive and histopathologic worsening.

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.

Clinical significance of tumor cavitation in surgically resected early-stage primary lung cancer.

PubMed

Tomizawa, Kenji; Shimizu, Shigeki; Ohara, Shuta; Fujino, Toshio; Nishino, Masaya; Sesumi, Yuichi; Kobayashi, Yoshihisa; Sato, Katsuaki; Chiba, Masato; Shimoji, Masaki; Suda, Kenichi; Takemoto, Toshiki; Mitsudomi, Tetsuya

2017-10-01

The prognostic impact of tumor cavitation is unclear in patients with early-stage primary lung cancer. The aim of the present study was to examine the clinicopathological features and prognoses of patients with pathological stage I-IIA (p-stage I-IIA) primary lung cancers harboring tumor cavitation. This study was conducted according to the eighth edition of the TNM classification for lung cancer. We examined 602 patients with p-stage I-IIA primary lung cancer out of 890 patients who underwent pulmonary resection from January 2007 through March 2014 and searched for the presence of tumor cavitation, which is defined as the presence of air space within the primary tumor. A total of 59 out of the 602 patients had tumor cavitation (10%). Compared with patients without tumor cavitation, those with tumor cavitation had a significantly higher frequency of the following characteristics: high serum carcinoembryonic antigen (CEA) level (≥5ng/ml, p=0.027), interstitial pneumonia (p=0.0001), high SUVmax value on FDG-PET scan (≥4.2, p=0.023), tumors located in the lower lobe (p=0.024), large tumor size (>3cm, p=0.002), vascular invasion (66% vs 17%, p<0.0001) and non-adenocarcinoma histology (p=0.025). The overall survival period of patients with tumor cavitation was significantly shorter than that of patients without tumor cavitation (log-rank test: p<0.0001, 5-year OS rate: 56% vs 81%). Tumor cavitation was found to be an independent and significant factor associated with poor prognosis in the multivariate analysis (hazard ratio: 1.76, 95% confidence interval: 1.02-3.10, p=0.042). Tumor cavitation is an independent factor for poor prognosis in patients with resected p-stage I-IIA primary lung cancer. Based on our analyses, patients with tumor cavitation should be regarded as a separate cohort that requires more intensive follow-up. Copyright © 2017 Elsevier B.V. All rights reserved.

[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.

Serial MR diffusion to predict treatment response in high-grade pediatric brain tumors: a comparison of regional and voxel-based diffusion change metrics

PubMed Central

Rodriguez Gutierrez, Daniel; Manita, Muftah; Jaspan, Tim; Dineen, Robert A.; Grundy, Richard G.; Auer, Dorothee P.

2013-01-01

Background Assessment of treatment response by measuring tumor size is known to be a late and potentially confounded response index. Serial diffusion MRI has shown potential for allowing earlier and possibly more reliable response assessment in adult patients, with limited experience in clinical settings and in pediatric brain cancer. We present a retrospective study of clinical MRI data in children with high-grade brain tumors to assess and compare the values of several diffusion change metrics to predict treatment response. Methods Eighteen patients (age range, 1.9–20.6 years) with high-grade brain tumors and serial diffusion MRI (pre- and posttreatment interval range, 1–16 weeks posttreatment) were identified after obtaining parental consent. The following diffusion change metrics were compared with the clinical response status assessed at 6 months: (1) regional change in absolute and normalized apparent diffusivity coefficient (ADC), (2) voxel-based fractional volume of increased (fiADC) and decreased ADC (fdADC), and (3) a new metric based on the slope of the first principal component of functional diffusion maps (fDM). Results Responders (n = 12) differed significantly from nonresponders (n = 6) in all 3 diffusional change metrics demonstrating higher regional ADC increase, larger fiADC, and steeper slopes (P < .05). The slope method allowed the best response prediction (P < .01, η2 = 0.78) with a classification accuracy of 83% for a slope of 58° using receiver operating characteristic (ROC) analysis. Conclusions We demonstrate that diffusion change metrics are suitable response predictors for high-grade pediatric tumors, even in the presence of variable clinical diffusion imaging protocols. PMID:23585630

A fuzzy neural network for intelligent data processing

NASA Astrophysics Data System (ADS)

Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam

2005-03-01

In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.

T-RMSD: a fine-grained, structure-based classification method and its application to the functional characterization of TNF receptors.

PubMed

Magis, Cedrik; Stricher, François; van der Sloot, Almer M; Serrano, Luis; Notredame, Cedric

2010-07-16

This study addresses the relation between structural and functional similarity in proteins. We introduce a novel method named tree based on root mean square deviation (T-RMSD), which uses distance RMSD (dRMSD) variations to build fine-grained structure-based classifications of proteins. The main improvement of the T-RMSD over similar methods, such as Dali, is its capacity to produce the equivalent of a bootstrap value for each cluster node. We validated our approach on two domain families studied extensively for their role in many biological and pathological pathways: the small GTPase RAS superfamily and the cysteine-rich domains (CRDs) associated with the tumor necrosis factor receptors (TNFRs) family. Our analysis showed that T-RMSD is able to automatically recover and refine existing classifications. In the case of the small GTPase ARF subfamily, T-RMSD can distinguish GTP- from GDP-bound states, while in the case of CRDs it can identify two new subgroups associated with well defined functional features (ligand binding and formation of ligand pre-assembly complex). We show how hidden Markov models (HMMs) can be built on these new groups and propose a methodology to use these models simultaneously in order to do fine-grained functional genomic annotation without known 3D structures. T-RMSD, an open source freeware incorporated in the T-Coffee package, is available online. 2010 Elsevier Ltd. All rights reserved.

Tumor Slice Culture: A New Avatar in Personalized Oncology

DTIC Science & Technology

2017-08-01

official Department of the Army position, policy or decision unless so designated by other documentation. REPORT DOCUMENTATION PAGE Form Approved OMB No...sensitivity and to correlate the results with clinical and molecular data. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT...differences in pre-operative treatments. Indeed, the viability scores significantly correlated with pathologic assessment of tumor viability/necrosis

Radiation-Induced Immune Modulation in Prostate Cancer

DTIC Science & Technology

2008-01-01

cancers. 15. SUBJECT TERMS Radiation, Dendritic Cells , Cytokines, PSA 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18...radiation is more than a cytotoxic agent. Our recent study has shown that radiation modulates the immune system by affecting dendritic cell (DC...translate radiation-induced tumor cell death into generation of tumor immunity in the hope of optimizing therapy for localized and disseminated prostate

75 FR 9767 - Classification of Benzoyl Peroxide as Safe and Effective and Revision of Labeling to Drug Facts...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-04

... photocarcinogenicity studies at that time. We also proposed labeling to communicate the results of the animal studies... promoter, benzoyl peroxide, at multiple times throughout the remainder of the study. Because tumor... to the backs 5 times per week for 50 weeks. In this study, benzoyl peroxide was not a tumor promoter...

Margins in extra-abdominal desmoid tumors: a comparative analysis.

PubMed

Leithner, Andreas; Gapp, Markus; Leithner, Katharina; Radl, Roman; Krippl, Peter; Beham, Alfred; Windhager, Reinhard

2004-06-01

The main treatment of extra-abdominal desmoid tumors remains surgery, but recurrence rates up to 80% are reported. The impact of microscopic surgical margin status according to the Enneking classification system is discussed controversially. Therefore, the authors screened the published literature for reliable data on the importance of a wide or radical excision of extra-abdominal desmoid tumors. All studies with more than ten patients, a surgical treatment only, and margin status stated were included. Only 12 out of 49 identified studies fulfilled the inclusion criteria. One hundred fifty-two primary tumors were excised with wide or radical microscopic surgical margins, while in 260 cases a marginal or intralesional excision was performed. In the first group 41 patients (27%) and in the second one 187 patients (72%) developed a recurrence. Therefore, microscopic surgical margin status according to the Enneking classification system is a significant prognostic factor (P < 0.001). The data of this review underline the strategy of a wide or radical local excision as the treatment of choice. Furthermore, as a large number of studies had to be excluded from this analysis, exact microscopic surgical margin status should be provided in future studies in order to allow comparability. . Copyright 2004 Wiley-Liss, Inc.

An active learning approach for rapid characterization of endothelial cells in human tumors.

PubMed

Padmanabhan, Raghav K; Somasundar, Vinay H; Griffith, Sandra D; Zhu, Jianliang; Samoyedny, Drew; Tan, Kay See; Hu, Jiahao; Liao, Xuejun; Carin, Lawrence; Yoon, Sam S; Flaherty, Keith T; Dipaola, Robert S; Heitjan, Daniel F; Lal, Priti; Feldman, Michael D; Roysam, Badrinath; Lee, William M F

2014-01-01

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

Computer-assisted surgery planning in children with complex liver tumors identifies variability of the classical Couinaud classification.

PubMed

Warmann, Steven W; Schenk, Andrea; Schaefer, Juergen F; Ebinger, Martin; Blumenstock, Gunnar; Tsiflikas, Ilias; Fuchs, Joerg

2016-11-01

In complex malignant pediatric liver tumors there is an ongoing discussion regarding surgical strategy; for example, primary organ transplantation versus extended resection in hepatoblastoma involving 3 or 4 sectors of the liver. We evaluated the possible role of computer-assisted surgery planning in children with complex hepatic tumors. Between May 2004 and March 2016, 24 Children with complex liver tumors underwent standard multislice helical CT scan or MRI scan at our institution. Imaging data were processed using the software assistant LiverAnalyzer (Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany). Results were provided as Portable Document Format (PDF) with embedded interactive 3-dimensional surface mesh models. Median age of patients was 33months. Diagnoses were hepatoblastoma (n=14), sarcoma (n=3), benign parenchyma alteration (n=2), as well as hepatocellular carcinoma, rhabdoid tumor, focal nodular hyperplasia, hemangioendothelioma, or multiple hepatic metastases of a pancreas carcinoma (each n=1). Volumetry of liver segments identified remarkable variations and substantial aberrances from the Couinaud classification. Computer-assisted surgery planning was used to determine surgical strategies in 20/24 children; this was especially relevant in tumors affecting 3 or 4 liver sectors. Primary liver transplantation could be avoided in 12 of 14 hepaoblastoma patients who theoretically were candidates for this approach. Computer-assisted surgery planning substantially contributed to the decision for surgical strategies in children with complex hepatic tumors. This tool possibly allows determination of specific surgical procedures such as extended surgical resection instead of primary transplantation in certain conditions. Copyright © 2016. Published by Elsevier Inc.

Clinical presentation and epidemiology of brain tumors firstly diagnosed in adults in the Emergency Department: a 10-year, single center retrospective study.

PubMed

Comelli, Ivan; Lippi, Giuseppe; Campana, Valentina; Servadei, Franco; Cervellin, Gianfranco

2017-07-01

Several patients with new onset brain tumors present to the Emergency Department (ED) complaining for new symptoms. Although information exists on symptom prevalence in the entire population of patients with brain tumors, little is known about the clinical presentation in ED. This retrospective study was planned to investigate clinical presentation and epidemiology of brain tumors firstly diagnosed in a large urban ED throughout a 10-year period. All medical records of patients aged ≥18 years, discharged from our ED with a diagnosis of brain tumor were retrieved from the electronic hospital database during a 10-year period (2006 to 2015). The records were reassessed for selecting only brain tumors firstly diagnosed in the ED. The symptoms at presentation were divided in six categories: (I) headache; (II) seizures; (III) focal signs; (IV) altered mental status; (V) nausea/vomiting/dizziness; (VI) trauma. For all cases, the hospital record was retrieved, to obtain histologic classification of tumors. Patients with inflammatory neoformations were excluded from the study. Overall, 205 patients with firstly diagnosed brain tumor were identified among 870,135 ED visits (i.e., <1%). Glial tumors were the most frequent (50% of the entire sample). No significant differences were found between mean age of patients in the different histologically based groups (meningiomas 66±14; glioblastomas 65±16 years; metastases 66±13 years; other miscellaneous 66±19 years). Focal signs accounted for more than 50% of all presentation signs/symptoms. First presentation of brain tumor in the ED is not a rare occurrence, so that the emergency physicians should be aware of this possibility.

The cost-saving effect of centralized histological reviews with soft tissue and visceral sarcomas, GIST, and desmoid tumors: The experiences of the pathologists of the French Sarcoma Group

PubMed Central

Rascle, Pauline; Morelle, Magali; Toulmonde, Maud; Ranchere Vince, Dominique; Le Cesne, Axel; Terrier, Philippe; Neuville, Agnès; Meeus, Pierre; Farsi, Fadila; Ducimetière, Françoise; Blay, Jean-Yves; Ray Coquard, Isabelle; Coindre, Jean-Michel

2018-01-01

Objective This study examined the types of discordance occurring in the diagnosis of soft tissue and visceral sarcomas, gastrointestinal stromal tumors (GIST), and desmoid tumors, as well as the economic impact of diagnostic discrepancies. Methods We carried out a retrospective, multicenter analysis using prospectively implemented databases performed on a cohort of patients within the French RRePS network in 2010. Diagnoses were deemed to be discordant based on the 2013 World Health Organization (WHO) classification. Predictive factors of discordant diagnoses were explored. A decision tree was used to assess the expected costs of two strategies of disease management: one based on revised diagnoses after centralized histological review (option 1), the other on diagnoses without centralized review (option 2). Both were defined based on the patient and the disease characteristics, according to national or international guidelines. The time horizon was 12 months and the perspective of the French National Health Insurance (NHI) was retained. Costs were expressed in Euros for 2013. Sensitivity analyses were performed using low and high scenarios that included ± 20% estimates for cost. Results A total of 2,425 patients were included. Three hundred forty-one patients (14%) had received discordant diagnoses. These discordances were determined to mainly be benign tumors diagnosed as sarcomas (n = 124), or non-sarcoma malignant tumors diagnosed as sarcomas (n = 77). The probability of discordance was higher for a final diagnosis of desmoid tumors when compared to liposarcomas (odds ratio = 5.1; 95%CI [2.6–10.4]). The expected costs per patient for the base-case analysis (low- and high-case scenarios) amounted to €8,791 (€7,033 and €10,549, respectively) for option 1 and €8,904 (€7,057 and €10,750, respectively) for option 2. Conclusions Our findings highlight misdiagnoses of sarcomas, which were found to most often be confused with benign tumors. Centralized histological reviews are likely to provide cost-savings for the French NHI. PMID:29621244

The cost-saving effect of centralized histological reviews with soft tissue and visceral sarcomas, GIST, and desmoid tumors: The experiences of the pathologists of the French Sarcoma Group.

PubMed

Perrier, Lionel; Rascle, Pauline; Morelle, Magali; Toulmonde, Maud; Ranchere Vince, Dominique; Le Cesne, Axel; Terrier, Philippe; Neuville, Agnès; Meeus, Pierre; Farsi, Fadila; Ducimetière, Françoise; Blay, Jean-Yves; Ray Coquard, Isabelle; Coindre, Jean-Michel

2018-01-01

This study examined the types of discordance occurring in the diagnosis of soft tissue and visceral sarcomas, gastrointestinal stromal tumors (GIST), and desmoid tumors, as well as the economic impact of diagnostic discrepancies. We carried out a retrospective, multicenter analysis using prospectively implemented databases performed on a cohort of patients within the French RRePS network in 2010. Diagnoses were deemed to be discordant based on the 2013 World Health Organization (WHO) classification. Predictive factors of discordant diagnoses were explored. A decision tree was used to assess the expected costs of two strategies of disease management: one based on revised diagnoses after centralized histological review (option 1), the other on diagnoses without centralized review (option 2). Both were defined based on the patient and the disease characteristics, according to national or international guidelines. The time horizon was 12 months and the perspective of the French National Health Insurance (NHI) was retained. Costs were expressed in Euros for 2013. Sensitivity analyses were performed using low and high scenarios that included ± 20% estimates for cost. A total of 2,425 patients were included. Three hundred forty-one patients (14%) had received discordant diagnoses. These discordances were determined to mainly be benign tumors diagnosed as sarcomas (n = 124), or non-sarcoma malignant tumors diagnosed as sarcomas (n = 77). The probability of discordance was higher for a final diagnosis of desmoid tumors when compared to liposarcomas (odds ratio = 5.1; 95%CI [2.6-10.4]). The expected costs per patient for the base-case analysis (low- and high-case scenarios) amounted to €8,791 (€7,033 and €10,549, respectively) for option 1 and €8,904 (€7,057 and €10,750, respectively) for option 2. Our findings highlight misdiagnoses of sarcomas, which were found to most often be confused with benign tumors. Centralized histological reviews are likely to provide cost-savings for the French NHI.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

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

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiersmore » were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.« less

Photonic Breast Tomography and Tumor Aggressiveness Assessment

DTIC Science & Technology

2011-07-01

incorporates, in optical domain, the vector subspace classification method, Multiple Signal Classification ( MUSIC ). MUSIC was developed by Devaney...and co-workers for finding the location of scattering targets whose size is smaller than the wavelength of acoustic waves or electromagnetic waves...general area of array processing for acoustic and radar time-reversal imaging [12]. The eigenvalue equation of TR matrix is solved, and the signal and

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2-Uncommon Sarcomas.

PubMed

Levy, Angela D; Manning, Maria A; Miettinen, Markku M

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis.

Soft-Tissue Sarcomas of the Abdomen and Pelvis: Radiologic-Pathologic Features, Part 2—Uncommon Sarcomas

PubMed Central

Manning, Maria A.; Miettinen, Markku M.

2017-01-01

Soft-tissue sarcomas occurring in the abdomen and pelvis are an uncommon but important group of malignancies. Recent changes to the World Health Organization classification of soft-tissue tumors include the movement of gastrointestinal stromal tumors (GISTs) into the soft-tissue tumor classification. GIST is the most common intraperitoneal sarcoma. Liposarcoma is the most common retroperitoneal sarcoma, and leiomyosarcoma is the second most common. GIST, liposarcoma, and leiomyosarcoma account for the majority of sarcomas encountered in the abdomen and pelvis and are discussed in part 1 of this article. Undifferentiated pleomorphic sarcoma (previously called malignant fibrous histiocytoma), dermatofibrosarcoma protuberans, solitary fibrous tumor, malignant peripheral nerve sheath tumor, rhabdomyosarcoma, extraskeletal chondro-osseous sarcomas, vascular sarcomas, and sarcomas of uncertain differentiation uncommonly arise in the abdomen and pelvis and the abdominal wall. Although these lesions are rare sarcomas and their imaging features overlap, familiarity with the locations where they occur and their imaging features is important so they can be diagnosed accurately. The anatomic location and clinical history are important factors in the differential diagnosis of these lesions because metastasis, more-common sarcomas, borderline fibroblastic proliferations (such as desmoid tumors), and endometriosis have imaging findings that overlap with those of these uncommon sarcomas. In this article, the clinical, pathologic, and imaging findings of uncommon soft-tissue sarcomas of the abdomen and pelvis and the abdominal wall are reviewed, with an emphasis on their differential diagnosis. PMID:28493803

Classification of cancer cell lines using matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry and statistical analysis.

PubMed

Serafim, Vlad; Shah, Ajit; Puiu, Maria; Andreescu, Nicoleta; Coricovac, Dorina; Nosyrev, Alexander; Spandidos, Demetrios A; Tsatsakis, Aristides M; Dehelean, Cristina; Pinzaru, Iulia

2017-10-01

Over the past decade, matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF MS) has been established as a valuable platform for microbial identification, and it is also frequently applied in biology and clinical studies to identify new markers expressed in pathological conditions. The aim of the present study was to assess the potential of using this approach for the classification of cancer cell lines as a quantifiable method for the proteomic profiling of cellular organelles. Intact protein extracts isolated from different tumor cell lines (human and murine) were analyzed using MALDI‑TOF MS and the obtained mass lists were processed using principle component analysis (PCA) within Bruker Biotyper® software. Furthermore, reference spectra were created for each cell line and were used for classification. Based on the intact protein profiles, we were able to differentiate and classify six cancer cell lines: two murine melanoma (B16‑F0 and B164A5), one human melanoma (A375), two human breast carcinoma (MCF7 and MDA‑MB‑231) and one human liver carcinoma (HepG2). The cell lines were classified according to cancer type and the species they originated from, as well as by their metastatic potential, offering the possibility to differentiate non‑invasive from invasive cells. The obtained results pave the way for developing a broad‑based strategy for the identification and classification of cancer cells.

The functional cancer map: a systems-level synopsis of genetic deregulation in cancer.

PubMed

Krupp, Markus; Maass, Thorsten; Marquardt, Jens U; Staib, Frank; Bauer, Tobias; König, Rainer; Biesterfeld, Stefan; Galle, Peter R; Tresch, Achim; Teufel, Andreas

2011-06-30

Cancer cells are characterized by massive dysegulation of physiological cell functions with considerable disruption of transcriptional regulation. Genome-wide transcriptome profiling can be utilized for early detection and molecular classification of cancers. Accurate discrimination of functionally different tumor types may help to guide selection of targeted therapy in translational research. Concise grouping of tumor types in cancer maps according to their molecular profile may further be helpful for the development of new therapeutic modalities or open new avenues for already established therapies. Complete available human tumor data of the Stanford Microarray Database was downloaded and filtered for relevance, adequacy and reliability. A total of 649 tumor samples from more than 1400 experiments and 58 different tissues were analyzed. Next, a method to score deregulation of KEGG pathway maps in different tumor entities was established, which was then used to convert hundreds of gene expression profiles into corresponding tumor-specific pathway activity profiles. Based on the latter, we defined a measure for functional similarity between tumor entities, which yielded to phylogeny of tumors. We provide a comprehensive, easy-to-interpret functional cancer map that characterizes tumor types with respect to their biological and functional behavior. Consistently, multiple pathways commonly associated with tumor progression were revealed as common features in the majority of the tumors. However, several pathways previously not linked to carcinogenesis were identified in multiple cancers suggesting an essential role of these pathways in cancer biology. Among these pathways were 'ECM-receptor interaction', 'Complement and Coagulation cascades', and 'PPAR signaling pathway'. The functional cancer map provides a systematic view on molecular similarities across different cancers by comparing tumors on the level of pathway activity. This work resulted in identification of novel superimposed functional pathways potentially linked to cancer biology. Therefore, our work may serve as a starting point for rationalizing combination of tumor therapeutics as well as for expanding the application of well-established targeted tumor therapies.

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.

Hot-spot selection and evaluation methods for whole slice images of meningiomas and oligodendrogliomas.

PubMed

Swiderska, Zaneta; Markiewicz, Tomasz; Grala, Bartlomiej; Slodkowska, Janina

2015-01-01

The paper presents a combined method for an automatic hot-spot areas selection based on penalty factor in the whole slide images to support the pathomorphological diagnostic procedure. The studied slides represent the meningiomas and oligodendrogliomas tumor on the basis of the Ki-67/MIB-1 immunohistochemical reaction. It allows determining the tumor proliferation index as well as gives an indication to the medical treatment and prognosis. The combined method based on mathematical morphology, thresholding, texture analysis and classification is proposed and verified. The presented algorithm includes building a specimen map, elimination of hemorrhages from them, two methods for detection of hot-spot fields with respect to an introduced penalty factor. Furthermore, we propose localization concordance measure to evaluation localization of hot spot selection by the algorithms in respect to the expert's results. Thus, the results of the influence of the penalty factor are presented and discussed. It was found that the best results are obtained for 0.2 value of them. They confirm effectiveness of applied approach.

Functional proteomics outlines the complexity of breast cancer molecular subtypes.

PubMed

Gámez-Pozo, Angelo; Trilla-Fuertes, Lucía; Berges-Soria, Julia; Selevsek, Nathalie; López-Vacas, Rocío; Díaz-Almirón, Mariana; Nanni, Paolo; Arevalillo, Jorge M; Navarro, Hilario; Grossmann, Jonas; Gayá Moreno, Francisco; Gómez Rioja, Rubén; Prado-Vázquez, Guillermo; Zapater-Moros, Andrea; Main, Paloma; Feliú, Jaime; Martínez Del Prado, Purificación; Zamora, Pilar; Ciruelos, Eva; Espinosa, Enrique; Fresno Vara, Juan Ángel

2017-08-30

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

Clinical application of a microfluidic chip for immunocapture and quantification of circulating exosomes to assist breast cancer diagnosis and molecular classification.

PubMed

Fang, Shimeng; Tian, Hongzhu; Li, Xiancheng; Jin, Dong; Li, Xiaojie; Kong, Jing; Yang, Chun; Yang, Xuesong; Lu, Yao; Luo, Yong; Lin, Bingcheng; Niu, Weidong; Liu, Tingjiao

2017-01-01

Increasing attention has been attracted by exosomes in blood-based diagnosis because cancer cells release more exosomes in serum than normal cells and these exosomes overexpress a certain number of cancer-related biomarkers. However, capture and biomarker analysis of exosomes for clinical application are technically challenging. In this study, we developed a microfluidic chip for immunocapture and quantification of circulating exosomes from small sample volume and applied this device in clinical study. Circulating EpCAM-positive exosomes were measured in 6 cases breast cancer patients and 3 healthy controls to assist diagnosis. A significant increase in the EpCAM-positive exosome level in these patients was detected, compared to healthy controls. Furthermore, we quantified circulating HER2-positive exosomes in 19 cases of breast cancer patients for molecular classification. We demonstrated that the exosomal HER2 expression levels were almost consistent with that in tumor tissues assessed by immunohistochemical staining. The microfluidic chip might provide a new platform to assist breast cancer diagnosis and molecular classification.

Transcriptional Analysis of Aggressiveness and Heterogeneity across Grades of Astrocytomas

PubMed Central

Wang, Chunjing; Funk, Cory C.; Eddy, James A.; Price, Nathan D.

2013-01-01

Astrocytoma is the most common glioma, accounting for half of all primary brain and spinal cord tumors. Late detection and the aggressive nature of high-grade astrocytomas contribute to high mortality rates. Though many studies identify candidate biomarkers using high-throughput transcriptomic profiling to stratify grades and subtypes, few have resulted in clinically actionable results. This shortcoming can be attributed, in part, to pronounced lab effects that reduce signature robustness and varied individual gene expression among patients with the same tumor. We addressed these issues by uniformly preprocessing publicly available transcriptomic data, comprising 306 tumor samples from three astrocytoma grades (Grade 2, 3, and 4) and 30 non-tumor samples (normal brain as control tissues). Utilizing Differential Rank Conservation (DIRAC), a network-based classification approach, we examined the global and individual patterns of network regulation across tumor grades. Additionally, we applied gene-based approaches to identify genes whose expression changed consistently with increasing tumor grade and evaluated their robustness across multiple studies using statistical sampling. Applying DIRAC, we observed a global trend of greater network dysregulation with increasing tumor aggressiveness. Individual networks displaying greater differences in regulation between adjacent grades play well-known roles in calcium/PKC, EGF, and transcription signaling. Interestingly, many of the 90 individual genes found to monotonically increase or decrease with astrocytoma grade are implicated in cancer-affected processes such as calcium signaling, mitochondrial metabolism, and apoptosis. The fact that specific genes monotonically increase or decrease with increasing astrocytoma grade may reflect shared oncogenic mechanisms among phenotypically similar tumors. This work presents statistically significant results that enable better characterization of different human astrocytoma grades and hopefully can contribute towards improvements in diagnosis and therapy choices. Our results also identify a number of testable hypotheses relating to astrocytoma etiology that may prove helpful in developing much-needed biomarkers for earlier disease detection. PMID:24146911

Transcriptional analysis of aggressiveness and heterogeneity across grades of astrocytomas.

PubMed

Wang, Chunjing; Funk, Cory C; Eddy, James A; Price, Nathan D

2013-01-01

Astrocytoma is the most common glioma, accounting for half of all primary brain and spinal cord tumors. Late detection and the aggressive nature of high-grade astrocytomas contribute to high mortality rates. Though many studies identify candidate biomarkers using high-throughput transcriptomic profiling to stratify grades and subtypes, few have resulted in clinically actionable results. This shortcoming can be attributed, in part, to pronounced lab effects that reduce signature robustness and varied individual gene expression among patients with the same tumor. We addressed these issues by uniformly preprocessing publicly available transcriptomic data, comprising 306 tumor samples from three astrocytoma grades (Grade 2, 3, and 4) and 30 non-tumor samples (normal brain as control tissues). Utilizing Differential Rank Conservation (DIRAC), a network-based classification approach, we examined the global and individual patterns of network regulation across tumor grades. Additionally, we applied gene-based approaches to identify genes whose expression changed consistently with increasing tumor grade and evaluated their robustness across multiple studies using statistical sampling. Applying DIRAC, we observed a global trend of greater network dysregulation with increasing tumor aggressiveness. Individual networks displaying greater differences in regulation between adjacent grades play well-known roles in calcium/PKC, EGF, and transcription signaling. Interestingly, many of the 90 individual genes found to monotonically increase or decrease with astrocytoma grade are implicated in cancer-affected processes such as calcium signaling, mitochondrial metabolism, and apoptosis. The fact that specific genes monotonically increase or decrease with increasing astrocytoma grade may reflect shared oncogenic mechanisms among phenotypically similar tumors. This work presents statistically significant results that enable better characterization of different human astrocytoma grades and hopefully can contribute towards improvements in diagnosis and therapy choices. Our results also identify a number of testable hypotheses relating to astrocytoma etiology that may prove helpful in developing much-needed biomarkers for earlier disease detection.

Methylation-based classification of benign and malignant peripheral nerve sheath tumors.

PubMed

Röhrich, Manuel; Koelsche, Christian; Schrimpf, Daniel; Capper, David; Sahm, Felix; Kratz, Annekathrin; Reuss, Jana; Hovestadt, Volker; Jones, David T W; Bewerunge-Hudler, Melanie; Becker, Albert; Weis, Joachim; Mawrin, Christian; Mittelbronn, Michel; Perry, Arie; Mautner, Victor-Felix; Mechtersheimer, Gunhild; Hartmann, Christian; Okuducu, Ali Fuat; Arp, Mirko; Seiz-Rosenhagen, Marcel; Hänggi, Daniel; Heim, Stefanie; Paulus, Werner; Schittenhelm, Jens; Ahmadi, Rezvan; Herold-Mende, Christel; Unterberg, Andreas; Pfister, Stefan M; von Deimling, Andreas; Reuss, David E

2016-06-01

The vast majority of peripheral nerve sheath tumors derive from the Schwann cell lineage and comprise diverse histological entities ranging from benign schwannomas and neurofibromas to high-grade malignant peripheral nerve sheath tumors (MPNST), each with several variants. There is increasing evidence for methylation profiling being able to delineate biologically relevant tumor groups even within the same cellular lineage. Therefore, we used DNA methylation arrays for methylome- and chromosomal profile-based characterization of 171 peripheral nerve sheath tumors. We analyzed 28 conventional high-grade MPNST, three malignant Triton tumors, six low-grade MPNST, four epithelioid MPNST, 33 neurofibromas (15 dermal, 8 intraneural, 10 plexiform), six atypical neurofibromas, 43 schwannomas (including 5 NF2 and 5 schwannomatosis associated cases), 11 cellular schwannomas, 10 melanotic schwannomas, 7 neurofibroma/schwannoma hybrid tumors, 10 nerve sheath myxomas and 10 ganglioneuromas. Schwannomas formed different epigenomic subgroups including a vestibular schwannoma subgroup. Cellular schwannomas were not distinct from conventional schwannomas. Nerve sheath myxomas and neurofibroma/schwannoma hybrid tumors were most similar to schwannomas. Dermal, intraneural and plexiform neurofibromas as well as ganglioneuromas all showed distinct methylation profiles. Atypical neurofibromas and low-grade MPNST were indistinguishable with a common methylation profile and frequent losses of CDKN2A. Epigenomic analysis finds two groups of conventional high-grade MPNST sharing a frequent loss of neurofibromin. The larger of the two groups shows an additional loss of trimethylation of histone H3 at lysine 27 (H3K27me3). The smaller one retains H3K27me3 and is found in spinal locations. Sporadic MPNST with retained neurofibromin expression did not form an epigenetic group and most cases could be reclassified as cellular schwannomas or soft tissue sarcomas. Widespread immunohistochemical loss of H3K27me3 was exclusively seen in MPNST of the main methylation cluster, which defines it as an additional useful marker for the differentiation of cellular schwannoma and MPNST.

Abridged republication of FIGO's staging classification for cancer of the ovary, fallopian tube, and peritoneum.

PubMed

Prat, Jaime

2015-10-01

Ovarian, fallopian tube, and peritoneal cancers have a similar clinical presentation and are treated similarly, and current evidence supports staging all 3 cancers in a single system. The primary site (i.e. ovary, fallopian tube, or peritoneum) should be designated where possible. The histologic type should be recorded. Intraoperative rupture ("surgical spill") is IC1; capsule ruptured before surgery or tumor on ovarian or fallopian tube surface is IC2; and positive peritoneal cytology with or without rupture is IC3. The new staging includes a revision of stage III patients; assignment to stage IIIA1 is based on spread to the retroperitoneal lymph nodes without intraperitoneal dissemination. Extension of tumor from omentum to spleen or liver (stage IIIC) should be differentiated from isolated parenchymal metastases (stage IVB). © 2015 American Cancer Society.

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

NASA Astrophysics Data System (ADS)

Fei, Baowei; Lu, Guolan; Wang, Xu; Zhang, Hongzheng; Little, James V.; Magliocca, Kelly R.; Chen, Amy Y.

2017-02-01

We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90+/-8%, sensitivity of 89+/-9%, and specificity of 91+/-6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94+/-6%, sensitivity of 94+/-6%, and specificity of 95+/-6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.

New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.

PubMed

Sturm, Dominik; Orr, Brent A; Toprak, Umut H; Hovestadt, Volker; Jones, David T W; Capper, David; Sill, Martin; Buchhalter, Ivo; Northcott, Paul A; Leis, Irina; Ryzhova, Marina; Koelsche, Christian; Pfaff, Elke; Allen, Sariah J; Balasubramanian, Gnanaprakash; Worst, Barbara C; Pajtler, Kristian W; Brabetz, Sebastian; Johann, Pascal D; Sahm, Felix; Reimand, Jüri; Mackay, Alan; Carvalho, Diana M; Remke, Marc; Phillips, Joanna J; Perry, Arie; Cowdrey, Cynthia; Drissi, Rachid; Fouladi, Maryam; Giangaspero, Felice; Łastowska, Maria; Grajkowska, Wiesława; Scheurlen, Wolfram; Pietsch, Torsten; Hagel, Christian; Gojo, Johannes; Lötsch, Daniela; Berger, Walter; Slavc, Irene; Haberler, Christine; Jouvet, Anne; Holm, Stefan; Hofer, Silvia; Prinz, Marco; Keohane, Catherine; Fried, Iris; Mawrin, Christian; Scheie, David; Mobley, Bret C; Schniederjan, Matthew J; Santi, Mariarita; Buccoliero, Anna M; Dahiya, Sonika; Kramm, Christof M; von Bueren, André O; von Hoff, Katja; Rutkowski, Stefan; Herold-Mende, Christel; Frühwald, Michael C; Milde, Till; Hasselblatt, Martin; Wesseling, Pieter; Rößler, Jochen; Schüller, Ulrich; Ebinger, Martin; Schittenhelm, Jens; Frank, Stephan; Grobholz, Rainer; Vajtai, Istvan; Hans, Volkmar; Schneppenheim, Reinhard; Zitterbart, Karel; Collins, V Peter; Aronica, Eleonora; Varlet, Pascale; Puget, Stephanie; Dufour, Christelle; Grill, Jacques; Figarella-Branger, Dominique; Wolter, Marietta; Schuhmann, Martin U; Shalaby, Tarek; Grotzer, Michael; van Meter, Timothy; Monoranu, Camelia-Maria; Felsberg, Jörg; Reifenberger, Guido; Snuderl, Matija; Forrester, Lynn Ann; Koster, Jan; Versteeg, Rogier; Volckmann, Richard; van Sluis, Peter; Wolf, Stephan; Mikkelsen, Tom; Gajjar, Amar; Aldape, Kenneth; Moore, Andrew S; Taylor, Michael D; Jones, Chris; Jabado, Nada; Karajannis, Matthias A; Eils, Roland; Schlesner, Matthias; Lichter, Peter; von Deimling, Andreas; Pfister, Stefan M; Ellison, David W; Korshunov, Andrey; Kool, Marcel

2016-02-25

Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)," and "CNS high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)," will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors. Copyright © 2016 Elsevier Inc. All rights reserved.

Clinical outcome and long-term survival of 150 consecutive patients with pancreatic neuroendocrine tumors: A comprehensive analysis by the World Health Organization 2010 grading classification.

PubMed

Deng, Ben-Yuan; Liu, Fei; Yin, Si-Neng; Chen, An-Ping; Xu, Lin; Li, Bo

2018-06-01

The World Health Organization (WHO) has revised its grading system for pancreatic neuroendocrine tumors (PNETs) in 2010 into three main group, which has not been widely and comprehensively evaluated. We aimed to validate the clinical valve of this system associated with the clinical outcome and long-term survival when applied to PNETs, which were rare and heterogeneous. We retrospectively collected and analyzed the data of 150 consecutive patients with PNETs who underwent a resection. Sixty-four males and 86 females with PNETs were enrolled in our study. The clinical stage from I to IV by European Neuroendocrine Tumor Society were respectively defined in 53, 60, 19 and 18 patients. Seventy-two patients were pathologically diagnosed as neuroendocrine tumor G1 (NET G1), 48 as neuroendocrine tumor G2 (NET G2) and 30 as neuroendocrine carcinoma G3 (NEC G3). Patients with a radical resection obtained a notably higher overall survival (OS) than that of patients who underwent a palliative surgery (P=0.001). The 5-year OS of patients with NET G1 was significantly higher than that of patients with NET G2 (P=0.015) and NEC G3 (P<0.001); the comparison of OS for patients with NET G2 and NEC G3 was also statistically significant (P=0.005). In both univariate and multivariate analysis, clinical staging by ENETS (stage I and II vs. stage III and IV), resection (radical vs. palliative) and WHO 2010 grading classification (NET G1 and G2 vs. NEC G3) were validated to be independent predictors for the survivals of PNETs. The newly-updated WHO 2010 grading classification was prognostic for the OS of PNETs and could be widely adopted in clinical practice. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Usefulness of High-Frequency Ultrasound in the Classification of Histologic Subtypes of Primary Basal Cell Carcinoma.

PubMed

Hernández-Ibáñez, C; Blazquez-Sánchez, N; Aguilar-Bernier, M; Fúnez-Liébana, R; Rivas-Ruiz, F; de Troya-Martín, M

Incisional biopsy may not always provide a correct classification of histologic subtypes of basal cell carcinoma (BCC). High-frequency ultrasound (HFUS) imaging of the skin is useful for the diagnosis and management of this tumor. The main aim of this study was to compare the diagnostic value of HFUS compared with punch biopsy for the correct classification of histologic subtypes of primary BCC. We also analyzed the influence of tumor size and histologic subtype (single subtype vs. mixed) on the diagnostic yield of HFUS and punch biopsy. Retrospective observational study of primary BCCs treated by the Dermatology Department of Hospital Costa del Sol in Marbella, Spain, between october 2013 and may 2014. Surgical excision was preceded by HFUS imaging (Dermascan C © , 20-MHz linear probe) and a punch biopsy in all cases. We compared the overall diagnostic yield and accuracy (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of HFUS and punch biopsy against the gold standard (excisional biopsy with serial sections) for overall and subgroup results. We studied 156 cases. The overall diagnostic yield was 73.7% for HFUS (sensitivity, 74.5%; specificity, 73%) and 79.9% for punch biopsy (sensitivity, 76%; specificity, 82%). In the subgroup analyses, HFUS had a PPV of 93.3% for superficial BCC (vs. 92% for punch biopsy). In the analysis by tumor size, HFUS achieved an overall diagnostic yield of 70.4% for tumors measuring 40mm 2 or less and 77.3% for larger tumors; the NPV was 82% in both size groups. Punch biopsy performed better in the diagnosis of small lesions (overall diagnostic yield of 86.4% for lesions ≤40mm 2 vs. 72.6% for lesions >40mm 2 ). HFUS imaging was particularly useful for ruling out infiltrating BCCs, diagnosing simple, superficial BCCs, and correctly classifying BCCs larger than 40mm 2 . Copyright © 2016 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.

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

Recursive partitioning analysis (RPA) classification predicts survival in patients with brain metastases from sarcoma.

PubMed

Grossman, Rachel; Ram, Zvi

2014-12-01

Sarcoma rarely metastasizes to the brain, and there are no specific treatment guidelines for these tumors. The recursive partitioning analysis (RPA) classification is a well-established prognostic scale used in many malignancies. In this study we assessed the clinical characteristics of metastatic sarcoma to the brain and the validity of the RPA classification system in a subset of 21 patients who underwent surgical resection of metastatic sarcoma to the brain We retrospectively analyzed the medical, radiological, surgical, pathological, and follow-up clinical records of 21 patients who were operated for metastatic sarcoma to the brain between 1996 and 2012. Gliosarcomas, sarcomas of the head and neck with local extension into the brain, and metastatic sarcomas to the spine were excluded from this reported series. The patients' mean age was 49.6 ± 14.2 years (range, 25-75 years) at the time of diagnosis. Sixteen patients had a known history of systemic sarcoma, mostly in the extremities, and had previously received systemic chemotherapy and radiation therapy for their primary tumor. The mean maximal tumor diameter in the brain was 4.9 ± 1.7 cm (range 1.7-7.2 cm). The group's median preoperative Karnofsky Performance Scale was 80, with 14 patients presenting with Karnofsky Performance Scale of 70 or greater. The median overall survival was 7 months (range 0.2-204 months). The median survival time stratified by the Radiation Therapy Oncology Group RPA classes were 31, 7, and 2 months for RPA class I, II, and III, respectively (P = 0.0001). This analysis is the first to support the prognostic utility of the Radiation Therapy Oncology Group RPA classification for sarcoma brain metastases and may be used as a treatment guideline tool in this rare disease. Copyright © 2014 Elsevier Inc. All rights reserved.

Setting the Stage for Personalized Treatment of Glioma | Center for Cancer Research

Cancer.gov

Gliomas, the most common type of primary brain tumors in adults, arise from different types of glial cells, which support and protect the neurons of the central nervous system. How a patient’s glioma is treated depends in part on the type of glial cell from which the tumor developed. Classification of gliomas has traditionally been done by microscopic analysis of tumor sections. This process is subjective and prone to inconsistencies, which may explain in part the wide-ranging and often suboptimal responses of gliomas to treatment.  

[Outstanding problems of normal and pathological morphology of the diffuse endocrine system].

PubMed

Iaglov, V V; Iaglova, N V

2011-01-01

The diffuse endocrine system (DES)--a mosaic-cellular endoepithelial gland--is the biggest part of the human endocrine system. Scientists used to consider cells of DES as neuroectodermal. According to modem data cells of DES are different cytogenetic types because they develop from the different embryonic blastophyllum. So that any hormone-active tumors originated from DES of the digestive, respiratory and urogenital system shouldn't be considered as neuroendocrinal tumors. The basic problems of DES morphology and pathology are the creation of scientifically substantiated histogenetic classification of DES tumors.

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

PubMed Central

Zarinabad, Niloufar; Meeus, Emma M; Manias, Karen; Foster, Katharine

2018-01-01

Background Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care. Objective The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data. Methods The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors. Results Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters. Conclusions MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians’ skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments. PMID:29720361

21 CFR 862.1320 - Gastric acidity test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Chemistry Test Systems § 862...-secreting tumor of the pancreas), and related gastric disorders. (b) Classification. Class I (general...

Noise-band factor analysis of cancer Fourier transform infrared evanescent-wave fiber optical (FTIR-FEW) spectra

NASA Astrophysics Data System (ADS)

Sukuta, Sydney; Bruch, Reinhard F.

2002-05-01

The goal of this study is to test the feasibility of using noise factor/eigenvector bands as general clinical analytical tools for diagnoses. We developed a new technique, Noise Band Factor Cluster Analysis (NBFCA), to diagnose benign tumors via their Fourier transform IR fiber optic evanescent wave spectral data for the first time. The middle IR region of human normal skin tissue and benign and melanoma tumors, were analyzed using this new diagnostic technique. Our results are not in full-agreement with pathological classifications hence there is a possibility that our approaches could complement or improve these traditional classification schemes. Moreover, the use of NBFCA make it much easier to delineate class boundaries hence this method provides results with much higher certainty.

Intratumor heterogeneity of DCE-MRI reveals Ki-67 proliferation status in breast cancer

NASA Astrophysics Data System (ADS)

Cheng, Hu; Fan, Ming; Zhang, Peng; Liu, Bin; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer is a highly heterogeneous disease both biologically and clinically, and certain pathologic parameters, i.e., Ki67 expression, are useful in predicting the prognosis of patients. The aim of the study is to identify intratumor heterogeneity of breast cancer for predicting Ki-67 proliferation status in estrogen receptor (ER)-positive breast cancer patients. A dataset of 77 patients was collected who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Of these patients, 51 were high-Ki-67 expression and 26 were low-Ki-67 expression. We partitioned the breast tumor into subregions using two methods based on the values of time to peak (TTP) and peak enhancement rate (PER). Within each tumor subregion, image features were extracted including statistical and morphological features from DCE-MRI. The classification models were applied on each region separately to assess whether the classifiers based on features extracted from various subregions features could have different performance for prediction. An area under a receiver operating characteristic curve (AUC) was computed using leave-one-out cross-validation (LOOCV) method. The classifier using features related with moderate time to peak achieved best performance with AUC of 0.826 than that based on the other regions. While using multi-classifier fusion method, the AUC value was significantly (P=0.03) increased to 0.858+/-0.032 compare to classifier with AUC of 0.778 using features from the entire tumor. The results demonstrated that features reflect heterogeneity in intratumoral subregions can improve the classifier performance to predict the Ki-67 proliferation status than the classifier using features from entire tumor alone.

Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

PubMed

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

PubMed

Zhou, Qian-Jun; Zheng, Zhi-Chun; Zhu, Yong-Qiao; Lu, Pei-Ji; Huang, Jia; Ye, Jian-Ding; Zhang, Jie; Lu, Shun; Luo, Qing-Quan

2017-05-01

To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

Functional Assessment of Synoptic Pathology Reporting for Ovarian Cancer.

PubMed

Słodkowska, Janina; Cierniak, Szczepan; Patera, Janusz; Kopik, Jarosław; Baranowski, Włodzimierz; Markiewicz, Tomasz; Murawski, Piotr; Buda, Irmina; Kozłowski, Wojciech

2016-01-01

Ovarian cancer has one of the highest death/incidence rates and is commonly diagnosed at an advanced stage. In the recent WHO classification, new histotypes were classified which respond differently to chemotherapy. The e-standardized synoptic cancer pathology reports offer the clinicians essential and reliable information. The aim of our project was to develop an e-template for the standardized synoptic pathology reporting of ovarian carcinoma [based on the checklist of the College of American Pathologists (CAP) and the recent WHO/FIGO classification] to introduce a uniform and improved quality of cancer pathology reports. A functional and qualitative evaluation of the synoptic reporting was performed. An indispensable module for e-synoptic reporting was developed and integrated into the Hospital Information System (HIS). The electronic pathology system used a standardized structure with drop-down lists of defined elements to ensure completeness and consistency of reporting practices with the required guidelines. All ovarian cancer pathology reports (partial and final) with the corresponding glass slides selected from a 1-year current workflow were revised for the standard structured reports, and 42 tumors [13 borderline tumors and 29 carcinomas (mainly serous)] were included in the study. Analysis of the reports for completeness against the CAP checklist standard showed a lack of pTNM staging in 80% of the partial or final unstructured reports; ICD-O coding was missing in 83%. Much less frequently missed or unstated data were: ovarian capsule infiltration, angioinvasion and implant evaluation. The e-records of ovarian tumors were supplemented with digital macro- and micro-images and whole-slide images. The e-module developed for synoptic ovarian cancer pathology reporting was easily incorporated into HIS.CGM CliniNet and facilitated comprehensive reporting; it also provided open access to the database for concerned recipients. The e-synoptic pathology reports appeared more accurate, clear and conclusive than traditional narrative reports. Standardizing structured reporting and electronic tools allows open access and downstream utilization of pathology data for clinicians and tumor registries. © 2016 S. Karger AG, Basel.

Juvenile nasopharyngeal angiofibroma staging: An overview.

PubMed

Alshaikh, Nada Ali; Eleftheriadou, Anna

2015-06-01

Staging of tumors is very important in treatment and surgical decision making, as well as in predicting disease recurrence and prognosis. This review focuses on the different available classifications of juvenile nasopharyngeal angiofibroma (JNA) and their impact on the evaluation, management, and prognosis of JNA. The literature was reviewed, and publications on JNA staging were examined. Our MEDLINE search of the entire English-language literature found no review article on the current available staging systems for JNA. In this article, we review the common JNA classification systems that have been published, and we discuss some of their advantages and disadvantages. The most commonly used staging systems for JNA are the Radkowski and the Andrews-Fisch staging systems. However, some newer staging systems that are based on advances in technology and surgical approaches-the Onerci, INCan, and UPMC systems-have shown promising utility, and they will probably gain popularity in the future.

Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions

NASA Astrophysics Data System (ADS)

Bratchenko, Ivan A.; Artemyev, Dmitry N.; Myakinin, Oleg O.; Khristoforova, Yulia A.; Moryatov, Alexander A.; Kozlov, Sergey V.; Zakharov, Valery P.

2017-02-01

The differentiation of skin melanomas and basal cell carcinomas (BCCs) was demonstrated based on combined analysis of Raman and autofluorescence spectra stimulated by visible and NIR lasers. It was ex vivo tested on 39 melanomas and 40 BCCs. Six spectroscopic criteria utilizing information about alteration of melanin, porphyrins, flavins, lipids, and collagen content in tumor with a comparison to healthy skin were proposed. The measured correlation between the proposed criteria makes it possible to define weakly correlated criteria groups for discriminant analysis and principal components analysis application. It was shown that the accuracy of cancerous tissues classification reaches 97.3% for a combined 6-criteria multimodal algorithm, while the accuracy determined separately for each modality does not exceed 79%. The combined 6-D method is a rapid and reliable tool for malignant skin detection and classification.

21 CFR 862.1325 - Gastrin test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Chemistry Test Systems § 862...-secreting tumor of the pancreas). (b) Classification. Class I (general controls). The device is exempt from...