Dye-enhanced multimodal confocal imaging as a novel approach to intraoperative diagnosis of brain tumors.

PubMed

Snuderl, Matija; Wirth, Dennis; Sheth, Sameer A; Bourne, Sarah K; Kwon, Churl-Su; Ancukiewicz, Marek; Curry, William T; Frosch, Matthew P; Yaroslavsky, Anna N

2013-01-01

Intraoperative diagnosis plays an important role in accurate sampling of brain tumors, limiting the number of biopsies required and improving the distinction between brain and tumor. The goal of this study was to evaluate dye-enhanced multimodal confocal imaging for discriminating gliomas from nonglial brain tumors and from normal brain tissue for diagnostic use. We investigated a total of 37 samples including glioma (13), meningioma (7), metastatic tumors (9) and normal brain removed for nontumoral indications (8). Tissue was stained in 0.05 mg/mL aqueous solution of methylene blue (MB) for 2-5 minutes and multimodal confocal images were acquired using a custom-built microscope. After imaging, tissue was formalin fixed and paraffin embedded for standard neuropathologic evaluation. Thirteen pathologists provided diagnoses based on the multimodal confocal images. The investigated tumor types exhibited distinctive and complimentary characteristics in both the reflectance and fluorescence responses. Images showed distinct morphological features similar to standard histology. Pathologists were able to distinguish gliomas from normal brain tissue and nonglial brain tumors, and to render diagnoses from the images in a manner comparable to haematoxylin and eosin (H&E) slides. These results confirm the feasibility of multimodal confocal imaging for intravital intraoperative diagnosis. © 2012 The Authors; Brain Pathology © 2012 International Society of Neuropathology.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors.

PubMed

Guo, Lu; Wang, Gang; Feng, Yuanming; Yu, Tonggang; Guo, Yu; Bai, Xu; Ye, Zhaoxiang

2016-09-21

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

Enhancement of brain tumor MR images based on intuitionistic fuzzy sets

NASA Astrophysics Data System (ADS)

Deng, Wankai; Deng, He; Cheng, Lifang

2015-12-01

Brain tumor is one of the most fatal cancers, especially high-grade gliomas are among the most deadly. However, brain tumor MR images usually have the disadvantages of low resolution and contrast when compared with the optical images. Consequently, we present a novel adaptive intuitionistic fuzzy enhancement scheme by combining a nonlinear fuzzy filtering operation with fusion operators, for the enhancement of brain tumor MR images in this paper. The presented scheme consists of the following six steps: Firstly, the image is divided into several sub-images. Secondly, for each sub-image, object and background areas are separated by a simple threshold. Thirdly, respective intuitionistic fuzzy generators of object and background areas are constructed based on the modified restricted equivalence function. Fourthly, different suitable operations are performed on respective membership functions of object and background areas. Fifthly, the membership plane is inversely transformed into the image plane. Finally, an enhanced image is obtained through fusion operators. The comparison and evaluation of enhancement performance demonstrate that the presented scheme is helpful to determine the abnormal functional areas, guide the operation, judge the prognosis, and plan the radiotherapy by enhancing the fine detail of MR images.

In vivo multiphoton tomography and fluorescence lifetime imaging of human brain tumor tissue.

PubMed

Kantelhardt, Sven R; Kalasauskas, Darius; König, Karsten; Kim, Ella; Weinigel, Martin; Uchugonova, Aisada; Giese, Alf

2016-05-01

High resolution multiphoton tomography and fluorescence lifetime imaging differentiates glioma from adjacent brain in native tissue samples ex vivo. Presently, multiphoton tomography is applied in clinical dermatology and experimentally. We here present the first application of multiphoton and fluorescence lifetime imaging for in vivo imaging on humans during a neurosurgical procedure. We used a MPTflex™ Multiphoton Laser Tomograph (JenLab, Germany). We examined cultured glioma cells in an orthotopic mouse tumor model and native human tissue samples. Finally the multiphoton tomograph was applied to provide optical biopsies during resection of a clinical case of glioblastoma. All tissues imaged by multiphoton tomography were sampled and processed for conventional histopathology. The multiphoton tomograph allowed fluorescence intensity- and fluorescence lifetime imaging with submicron spatial resolution and 200 picosecond temporal resolution. Morphological fluorescence intensity imaging and fluorescence lifetime imaging of tumor-bearing mouse brains and native human tissue samples clearly differentiated tumor and adjacent brain tissue. Intraoperative imaging was found to be technically feasible. Intraoperative image quality was comparable to ex vivo examinations. To our knowledge we here present the first intraoperative application of high resolution multiphoton tomography and fluorescence lifetime imaging of human brain tumors in situ. It allowed in vivo identification and determination of cell density of tumor tissue on a cellular and subcellular level within seconds. The technology shows the potential of rapid intraoperative identification of native glioma tissue without need for tissue processing or staining.

Raman spectroscopic imaging as complementary tool for histopathologic assessment of brain tumors

NASA Astrophysics Data System (ADS)

Krafft, Christoph; Bergner, Norbert; Romeike, Bernd; Reichart, Rupert; Kalff, Rolf; Geiger, Kathrin; Kirsch, Matthias; Schackert, Gabriele; Popp, Jürgen

2012-02-01

Raman spectroscopy enables label-free assessment of brain tissues and tumors based on their biochemical composition. Combination of the Raman spectra with the lateral information allows grading of tumors, determining the primary tumor of brain metastases and delineating tumor margins - even during surgery after coupling with fiber optic probes. This contribution presents exemplary Raman spectra and images collected from low grade and high grade regions of astrocytic gliomas and brain metastases. A region of interest in dried tissue sections encompassed slightly increased cell density. Spectral unmixing by vertex component analysis (VCA) and N-FINDR resolved cell nuclei in score plots and revealed the spectral contributions of nucleic acids, cholesterol, cholesterol ester and proteins in endmember signatures. The results correlated with the histopathological analysis after staining the specimens by hematoxylin and eosin. For a region of interest in non-dried, buffer immersed tissue sections image processing was not affected by drying artifacts such as denaturation of biomolecules and crystallization of cholesterol. Consequently, the results correspond better to in vivo situations. Raman spectroscopic imaging of a brain metastases from renal cell carcinoma showed an endmember with spectral contributions of glycogen which can be considered as a marker for this primary tumor.

Label-free imaging of brain and brain tumor specimens with combined two-photon excited fluorescence and second harmonic generation microscopy

NASA Astrophysics Data System (ADS)

Jiang, Liwei; Wang, Xingfu; Wu, Zanyi; Du, Huiping; Wang, Shu; Li, Lianhuang; Fang, Na; Lin, Peihua; Chen, Jianxin; Kang, Dezhi; Zhuo, Shuangmu

2017-10-01

Label-free imaging techniques are gaining acceptance within the medical imaging field, including brain imaging, because they have the potential to be applied to intraoperative in situ identifications of pathological conditions. In this paper, we describe the use of two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopy in combination for the label-free detection of brain and brain tumor specimens; gliomas. Two independently detecting channels were chosen to subsequently collect TPEF/SHG signals from the specimen to increase TPEF/SHG image contrasts. Our results indicate that the combined TPEF/SHG microscopic techniques can provide similar rat brain structural information and produce a similar resolution like conventional H&E staining in neuropathology; including meninges, cerebral cortex, white-matter structure corpus callosum, choroid plexus, hippocampus, striatum, and cerebellar cortex. It can simultaneously detect infiltrating human brain tumor cells, the extracellular matrix collagen fiber of connective stroma within brain vessels and collagen depostion in tumor microenvironments. The nuclear-to-cytoplasmic ratio and collagen content can be extracted as quantitative indicators for differentiating brain gliomas from healthy brain tissues. With the development of two-photon fiberscopes and microendoscope probes and their clinical applications, the combined TPEF and SHG microcopy may become an important multimodal, nonlinear optical imaging approach for real-time intraoperative histological diagnostics of residual brain tumors. These occur in various brain regions during ongoing surgeries through the method of simultaneously identifying tumor cells, and the change of tumor microenvironments, without the need for the removal biopsies and without the need for tissue labelling or fluorescent markers.

Image updating for brain deformation compensation in tumor resection

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Ji, Songbai; Olson, Jonathan D.; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2016-03-01

Preoperative magnetic resonance images (pMR) are typically used for intraoperative guidance in image-guided neurosurgery, the accuracy of which can be significantly compromised by brain deformation. Biomechanical finite element models (FEM) have been developed to estimate whole-brain deformation and produce model-updated MR (uMR) that compensates for brain deformation at different surgical stages. Early stages of surgery, such as after craniotomy and after dural opening, have been well studied, whereas later stages after tumor resection begins remain challenging. In this paper, we present a method to simulate tumor resection by incorporating data from intraoperative stereovision (iSV). The amount of tissue resection was estimated from iSV using a "trial-and-error" approach, and the cortical shift was measured from iSV through a surface registration method using projected images and an optical flow (OF) motion tracking algorithm. The measured displacements were employed to drive the biomechanical brain deformation model, and the estimated whole-brain deformation was subsequently used to deform pMR and produce uMR. We illustrate the method using one patient example. The results show that the uMR aligned well with iSV and the overall misfit between model estimates and measured displacements was 1.46 mm. The overall computational time was ~5 min, including iSV image acquisition after resection, surface registration, modeling, and image warping, with minimal interruption to the surgical flow. Furthermore, we compare uMR against intraoperative MR (iMR) that was acquired following iSV acquisition.

In Vivo Follow-up of Brain Tumor Growth via Bioluminescence Imaging and Fluorescence Tomography

PubMed Central

Genevois, Coralie; Loiseau, Hugues; Couillaud, Franck

2016-01-01

Reporter gene-based strategies are widely used in experimental oncology. Bioluminescence imaging (BLI) using the firefly luciferase (Fluc) as a reporter gene and d-luciferin as a substrate is currently the most widely employed technique. The present paper compares the performances of BLI imaging with fluorescence imaging using the near infrared fluorescent protein (iRFP) to monitor brain tumor growth in mice. Fluorescence imaging includes fluorescence reflectance imaging (FRI), fluorescence diffuse optical tomography (fDOT), and fluorescence molecular Imaging (FMT®). A U87 cell line was genetically modified for constitutive expression of both the encoding Fluc and iRFP reporter genes and assayed for cell, subcutaneous tumor and brain tumor imaging. On cultured cells, BLI was more sensitive than FRI; in vivo, tumors were first detected by BLI. Fluorescence of iRFP provided convenient tools such as flux cytometry, direct detection of the fluorescent protein on histological slices, and fluorescent tomography that allowed for 3D localization and absolute quantification of the fluorescent signal in brain tumors. PMID:27809256

In Vivo Follow-up of Brain Tumor Growth via Bioluminescence Imaging and Fluorescence Tomography.

PubMed

Genevois, Coralie; Loiseau, Hugues; Couillaud, Franck

2016-10-31

Reporter gene-based strategies are widely used in experimental oncology. Bioluminescence imaging (BLI) using the firefly luciferase (Fluc) as a reporter gene and d-luciferin as a substrate is currently the most widely employed technique. The present paper compares the performances of BLI imaging with fluorescence imaging using the near infrared fluorescent protein (iRFP) to monitor brain tumor growth in mice. Fluorescence imaging includes fluorescence reflectance imaging (FRI), fluorescence diffuse optical tomography (fDOT), and fluorescence molecular Imaging (FMT ® ). A U87 cell line was genetically modified for constitutive expression of both the encoding Fluc and iRFP reporter genes and assayed for cell, subcutaneous tumor and brain tumor imaging. On cultured cells, BLI was more sensitive than FRI; in vivo, tumors were first detected by BLI. Fluorescence of iRFP provided convenient tools such as flux cytometry, direct detection of the fluorescent protein on histological slices, and fluorescent tomography that allowed for 3D localization and absolute quantification of the fluorescent signal in brain tumors.

Improved tumor identification using dual tracer molecular imaging in fluorescence guided brain surgery

NASA Astrophysics Data System (ADS)

Xu, Xiaochun; Torres, Veronica; Straus, David; Brey, Eric M.; Byrne, Richard W.; Tichauer, Kenneth M.

2015-03-01

Brain tumors represent a leading cause of cancer death for people under the age of 40 and the probability complete surgical resection of brain tumors remains low owing to the invasive nature of these tumors and the consequences of damaging healthy brain tissue. Molecular imaging is an emerging approach that has the potential to improve the ability for surgeons to correctly discriminate between healthy and cancerous tissue; however, conventional molecular imaging approaches in brain suffer from significant background signal in healthy tissue or an inability target more invasive sections of the tumor. This work presents initial studies investigating the ability of novel dual-tracer molecular imaging strategies to be used to overcome the major limitations of conventional "single-tracer" molecular imaging. The approach is evaluated in simulations and in an in vivo mice study with animals inoculated orthotopically using fluorescent human glioma cells. An epidermal growth factor receptor (EGFR) targeted Affibody-fluorescent marker was employed as a targeted imaging agent, and the suitability of various FDA approved untargeted fluorescent tracers (e.g. fluorescein & indocyanine green) were evaluated in terms of their ability to account for nonspecific uptake and retention of the targeted imaging agent. Signal-to-background ratio was used to measure and compare the amount of reporter in the tissue between targeted and untargeted tracer. The initial findings suggest that FDA-approved fluorescent imaging agents are ill-suited to act as untargeted imaging agents for dual-tracer fluorescent guided brain surgery as they suffer from poor delivery to the healthy brain tissue and therefore cannot be used to identify nonspecific vs. specific uptake of the targeted imaging agent where current surgery is most limited.

A validation framework for brain tumor segmentation.

PubMed

Archip, Neculai; Jolesz, Ferenc A; Warfield, Simon K

2007-10-01

We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented. The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms. We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/. We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

PubMed

Menze, Bjoern H; Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B; Ayache, Nicholas; Buendia, Patricia; Collins, D Louis; Cordier, Nicolas; Corso, Jason J; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M; Jena, Raj; John, Nigel M; Konukoglu, Ender; Lashkari, Danial; Mariz, José Antonió; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J; Raviv, Tammy Riklin; Reza, Syed M S; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A; Sousa, Nuno; Subbanna, Nagesh K; Szekely, Gabor; Taylor, Thomas J; Thomas, Owen M; Tustison, Nicholas J; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

2015-10-01

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Optical Imaging of Targeted β-Galactosidase in Brain Tumors to Detect EGFR Levels

PubMed Central

Broome, Ann-Marie; Ramamurthy, Gopal; Lavik, Kari; Liggett, Alexander; Kinstlinger, Ian; Basilion, James

2015-01-01

A current limitation in molecular imaging is that it often requires genetic manipulation of cancer cells for noninvasive imaging. Other methods to detect tumor cells in vivo using exogenously delivered and functionally active reporters, such as β-gal, are required. We report the development of a platform system for linking β-gal to any number of different ligands or antibodies for in vivo targeting to tissue or cells, without the requirement for genetic engineering of the target cells prior to imaging. Our studies demonstrate significant uptake in vitro and in vivo of an EGFR-targeted β-gal complex. We were then able to image orthotopic brain tumor accumulation and localization of the targeted enzyme when a fluorophore was added to the complex, as well as validate the internalization of the intravenously administered β-gal reporter complex ex vivo. After fluorescence imaging localized the β-gal complexes to the brain tumor, we topically applied a bioluminescent β-gal substrate to serial sections of the brain to evaluate the delivery and integrity of the enzyme. Finally, robust bioluminescence of the EGFR-targeted β-gal complex was captured within the tumor during noninvasive in vivo imaging. PMID:25775241

Optical imaging of targeted β-galactosidase in brain tumors to detect EGFR levels.

PubMed

Broome, Ann-Marie; Ramamurthy, Gopal; Lavik, Kari; Liggett, Alexander; Kinstlinger, Ian; Basilion, James

2015-04-15

A current limitation in molecular imaging is that it often requires genetic manipulation of cancer cells for noninvasive imaging. Other methods to detect tumor cells in vivo using exogenously delivered and functionally active reporters, such as β-gal, are required. We report the development of a platform system for linking β-gal to any number of different ligands or antibodies for in vivo targeting to tissue or cells, without the requirement for genetic engineering of the target cells prior to imaging. Our studies demonstrate significant uptake in vitro and in vivo of an EGFR-targeted β-gal complex. We were then able to image orthotopic brain tumor accumulation and localization of the targeted enzyme when a fluorophore was added to the complex, as well as validate the internalization of the intravenously administered β-gal reporter complex ex vivo. After fluorescence imaging localized the β-gal complexes to the brain tumor, we topically applied a bioluminescent β-gal substrate to serial sections of the brain to evaluate the delivery and integrity of the enzyme. Finally, robust bioluminescence of the EGFR-targeted β-gal complex was captured within the tumor during noninvasive in vivo imaging.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

PubMed

Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

2016-05-01

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors.

PubMed

Pak, Rebecca W; Hadjiabadi, Darian H; Senarathna, Janaka; Agarwal, Shruti; Thakor, Nitish V; Pillai, Jay J; Pathak, Arvind P

2017-11-01

Functional magnetic resonance imaging (fMRI) serves as a critical tool for presurgical mapping of eloquent cortex and changes in neurological function in patients diagnosed with brain tumors. However, the blood-oxygen-level-dependent (BOLD) contrast mechanism underlying fMRI assumes that neurovascular coupling remains intact during brain tumor progression, and that measured changes in cerebral blood flow (CBF) are correlated with neuronal function. Recent preclinical and clinical studies have demonstrated that even low-grade brain tumors can exhibit neurovascular uncoupling (NVU), which can confound interpretation of fMRI data. Therefore, to avoid neurosurgical complications, it is crucial to understand the biophysical basis of NVU and its impact on fMRI. Here we review the physiology of the neurovascular unit, how it is remodeled, and functionally altered by brain cancer cells. We first discuss the latest findings about the components of the neurovascular unit. Next, we synthesize results from preclinical and clinical studies to illustrate how brain tumor induced NVU affects fMRI data interpretation. We examine advances in functional imaging methods that permit the clinical evaluation of brain tumors with NVU. Finally, we discuss how the suppression of anomalous tumor blood vessel formation with antiangiogenic therapies can "normalize" the brain tumor vasculature, and potentially restore neurovascular coupling.

Evaluation of image quality of MRI data for brain tumor surgery

NASA Astrophysics Data System (ADS)

Heckel, Frank; Arlt, Felix; Geisler, Benjamin; Zidowitz, Stephan; Neumuth, Thomas

2016-03-01

3D medical images are important components of modern medicine. Their usefulness for the physician depends on their quality, though. Only high-quality images allow accurate and reproducible diagnosis and appropriate support during treatment. We have analyzed 202 MRI images for brain tumor surgery in a retrospective study. Both an experienced neurosurgeon and an experienced neuroradiologist rated each available image with respect to its role in the clinical workflow, its suitability for this specific role, various image quality characteristics, and imaging artifacts. Our results show that MRI data acquired for brain tumor surgery does not always fulfill the required quality standards and that there is a significant disagreement between the surgeon and the radiologist, with the surgeon being more critical. Noise, resolution, as well as the coverage of anatomical structures were the most important criteria for the surgeon, while the radiologist was mainly disturbed by motion artifacts.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

PubMed Central

Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B.; Ayache, Nicholas; Buendia, Patricia; Collins, D. Louis; Cordier, Nicolas; Corso, Jason J.; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R.; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M.; Jena, Raj; John, Nigel M.; Konukoglu, Ender; Lashkari, Danial; Mariz, José António; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J.; Raviv, Tammy Riklin; Reza, Syed M. S.; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A.; Sousa, Nuno; Subbanna, Nagesh K.; Szekely, Gabor; Taylor, Thomas J.; Thomas, Owen M.; Tustison, Nicholas J.; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

2016-01-01

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource. PMID:25494501

Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

PubMed

Thapaliya, Kiran; Pyun, Jae-Young; Park, Chun-Su; Kwon, Goo-Rak

2013-01-01

The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images.

PubMed

Harati, Vida; Khayati, Rasoul; Farzan, Abdolreza

2011-07-01

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary. A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures. Copyright © 2011 Elsevier Ltd. All rights reserved.

Brain tumor imaging of rat fresh tissue using terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Yamaguchi, Sayuri; Fukushi, Yasuko; Kubota, Oichi; Itsuji, Takeaki; Ouchi, Toshihiko; Yamamoto, Seiji

2016-07-01

Tumor imaging by terahertz spectroscopy of fresh tissue without dye is demonstrated using samples from a rat glioma model. The complex refractive index spectrum obtained by a reflection terahertz time-domain spectroscopy system can discriminate between normal and tumor tissues. Both the refractive index and absorption coefficient of tumor tissues are higher than those of normal tissues and can be attributed to the higher cell density and water content of the tumor region. The results of this study indicate that terahertz technology is useful for detecting brain tumor tissue.

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

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

2015-08-01

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

Fast and robust brain tumor segmentation using level set method with multiple image information.

PubMed

Lok, Ka Hei; Shi, Lin; Zhu, Xianlun; Wang, Defeng

2017-01-01

Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

NASA Astrophysics Data System (ADS)

Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

2015-04-01

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

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.

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

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

Abdulbaqi, Hayder Saad; Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya; Jafri, Mohd Zubir Mat

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introducemore » a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.« less

Magnetic resonance imaging spectroscopy in pediatric atypical teratoid rhabdoid tumors of the brain.

PubMed

Bruggers, Carol S; Moore, Kevin

2014-08-01

Pediatric central nervous system (CNS) atypical teratoid rhabdoid tumors (ATRT) are highly malignant tumors characterized by SMARCB1 gene abnormalities. Despite chemoradiation responsiveness, most children die of disease. No imaging findings distinguish ATRT from other malignant brain tumors. This study sought to describe magnetic resonance spectroscopy (MRS) of childhood CNS ATRT and identify metabolite patterns for diagnosis and disease status monitoring. Data from 7 children diagnosed with CNS ATRT from 2007 to 2010, whose imaging included MRS, were retrospectively reviewed. Age at diagnosis ranged from 2.5 to 54 months. Tumors were large with calcium and cysts and avid gadolinium enhancement. All were isointense on T1-weighted imaging and mildly hyperintense on T2-weighted imaging. Short-TE MRS showed prominent lactate+lipid and choline, minimal N-acetyl acetate (NAA), and rarely minimal myoinositol and low creatine peaks. Long TE showed prominent choline, minimal NAA, and rarely low lactate peaks. The combination of prominent choline and lactate+lipids peaks, and generally absent NAA and myoinositol peaks by MRS in this panel of ATRT expands existing information and provides a potentially distinct metabolite profile from other malignant pediatric brain tumors, including medulloblastoma. Prospective, comparative quantitative MRS of ATRT with other pediatric CNS tumors is warranted.

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel

2014-03-01

Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

Multifunctional Nanoparticles for Brain Tumor Diagnosis and Therapy

PubMed Central

Cheng, Yu; Morshed, Ramin; Auffinger, Brenda; Tobias, Alex L.; Lesniak, Maciej S.

2013-01-01

Brain tumors are a diverse group of neoplasms that often carry a poor prognosis for patients. Despite tremendous efforts to develop diagnostic tools and therapeutic avenues, the treatment of brain tumors remains a formidable challenge in the field of neuro-oncology. Physiological barriers including the blood-brain barrier result in insufficient accumulation of therapeutic agents at the site of a tumor, preventing adequate destruction of malignant cells. Furthermore, there is a need for improvements in brain tumor imaging to allow for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional nanoparticles offer the potential to improve upon many of these issues and may lead to breakthroughs in brain tumor management. In this review, we discuss the diagnostic and therapeutic applications of nanoparticles for brain tumors with an emphasis on innovative approaches in tumor targeting, tumor imaging, and therapeutic agent delivery. Clinically feasible nanoparticle administration strategies for brain tumor patients are also examined. Furthermore, we address the barriers towards clinical implementation of multifunctional nanoparticles in the context of brain tumor management. PMID:24060923

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.

Targeted delivery of antibody-based therapeutic and imaging agents to CNS tumors: Crossing the blood-brain-barrier divide

PubMed Central

Chacko, Ann-Marie; Li, Chunsheng; Pryma, Daniel A.; Brem, Steven; Coukos, George; Muzykantov, Vladimir R.

2014-01-01

Introduction Brain tumors are inherently difficult to treat in large part due to the cellular blood-brain barriers (BBB) that limit the delivery of therapeutics to the tumor tissue from the systemic circulation. Virtually no large-molecules, including antibody-based proteins, can penetrate the BBB. With antibodies fast becoming attractive ligands for highly specific molecular targeting to tumor antigens, a variety of methods are being investigated to enhance the access of these agents to intracranial tumors for imaging or therapeutic applications. Areas covered This review describes the characteristics of the BBB and the vasculature in brain tumors, described as the blood-brain tumor barrier (BBTB). Antibodies targeted to molecular markers of CNS tumors will be highlighted, and current strategies for enhancing the delivery of antibodies across these cellular barriers into the brain parenchyma to the tumor will be discussed. Non-invasive imaging approaches to assess BBB/BBTB permeability and/or antibody targeting will be presented as a means of guiding the optimal delivery of targeted agents to brain tumors. Expert Opinion Pre-clinical and clinical studies highlight the potential of several approaches in increasing brain tumor delivery across the blood-brain barrier divide. However, each carries its own risks and challenges. There is tremendous potential in using neuroimaging strategies to assist in understanding and defining the challenges to translating and optimizing molecularly-targeted antibody delivery to CNS tumors to improve clinical outcomes. PMID:23751126

Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Lindner, Dirk; Arlt, Felix; Ituna-Yudonago, Jean Fulbert; Chalopin, Claire

2018-03-01

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS. A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented. Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods. The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors.

PubMed

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A

2015-11-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

PubMed Central

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A.

2015-01-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional. PMID:26644943

Covalent nano delivery systems for selective imaging and treatment of brain tumors.

PubMed

Ljubimova, Julia Y; Sun, Tao; Mashouf, Leila; Ljubimov, Alexander V; Israel, Liron L; Ljubimov, Vladimir A; Falahatian, Vida; Holler, Eggehard

2017-04-01

Nanomedicine is a rapidly evolving form of therapy that holds a great promise for superior drug delivery efficiency and therapeutic efficacy than conventional cancer treatment. In this review, we attempt to cover the benefits and the limitations of current nanomedicines with special attention to covalent nano conjugates for imaging and drug delivery in the brain. The improvement in brain tumor treatment remains dismal despite decades of efforts in drug development and patient care. One of the major obstacles in brain cancer treatment is the poor drug delivery efficiency owing to the unique blood-brain barrier (BBB) in the CNS. Although various anti-cancer agents are available to treat tumors outside of the CNS, the majority fails to cross the BBB. In this regard, nanomedicines have increasingly drawn attention due to their multi-functionality and versatility. Nano drugs can penetrate BBB and other biological barriers, and selectively accumulate in tumor cells, while concurrently decreasing systemic toxicity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

An automatic brain tumor segmentation tool.

PubMed

Diaz, Idanis; Boulanger, Pierre; Greiner, Russell; Hoehn, Bret; Rowe, Lindsay; Murtha, Albert

2013-01-01

This paper introduces an automatic brain tumor segmentation method (ABTS) for segmenting multiple components of brain tumor using four magnetic resonance image modalities. ABTS's four stages involve automatic histogram multi-thresholding and morphological operations including geodesic dilation. Our empirical results, on 16 real tumors, show that ABTS works very effectively, achieving a Dice accuracy compared to expert segmentation of 81% in segmenting edema and 85% in segmenting gross tumor volume (GTV).

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

NASA Astrophysics Data System (ADS)

Pradhan, Nandita; Sinha, A. K.

2008-03-01

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

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images.

PubMed

Hamoud Al-Tamimi, Mohammed Sabbih; Sulong, Ghazali; Shuaib, Ibrahim Lutfi

2015-07-01

Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

[Application of diffusion tensor imaging fractography in minimally invasive surgery of brain tumors].

PubMed

Yang, Lei; Zhang, Mao-zhi; Zhang, Wei; Zhao, Yuan-li; Zhao, Ji-zong

2006-05-23

To investigate the effects and prospect of application of diffusion tensor imaging (DTI) fractography in minimally invasive surgery of brain tumors. DTI fractography was performed in 52 patients with malignant brain tumors. Based on the DTI fractography results, 34 of the 52 patients underwent operation under neuro-navigation, and 18 of the 52 patients underwent operation routine minimally invasive craniotomy and tumor resection without neuro-navigation. The rate of total tumor resection was 86.5% (45/52). The mortality was 1.9% (1/52). The disability rate was 11.5% (6/52). No case needed the second operation. DTI fractography has raised the minimally invasive neurosurgery to the level of protecting the nuclei and nerve tracts and guiding intra-operative management of infiltration of deep-seated tumors, especially when combined with neuro-navigation and interventional MRI.

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).

A tri-modality image fusion method for target delineation of brain tumors in radiotherapy.

PubMed

Guo, Lu; Shen, Shuming; Harris, Eleanor; Wang, Zheng; Jiang, Wei; Guo, Yu; Feng, Yuanming

2014-01-01

To develop a tri-modality image fusion method for better target delineation in image-guided radiotherapy for patients with brain tumors. A new method of tri-modality image fusion was developed, which can fuse and display all image sets in one panel and one operation. And a feasibility study in gross tumor volume (GTV) delineation using data from three patients with brain tumors was conducted, which included images of simulation CT, MRI, and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) examinations before radiotherapy. Tri-modality image fusion was implemented after image registrations of CT+PET and CT+MRI, and the transparency weight of each modality could be adjusted and set by users. Three radiation oncologists delineated GTVs for all patients using dual-modality (MRI/CT) and tri-modality (MRI/CT/PET) image fusion respectively. Inter-observer variation was assessed by the coefficient of variation (COV), the average distance between surface and centroid (ADSC), and the local standard deviation (SDlocal). Analysis of COV was also performed to evaluate intra-observer volume variation. The inter-observer variation analysis showed that, the mean COV was 0.14(± 0.09) and 0.07(± 0.01) for dual-modality and tri-modality respectively; the standard deviation of ADSC was significantly reduced (p<0.05) with tri-modality; SDlocal averaged over median GTV surface was reduced in patient 2 (from 0.57 cm to 0.39 cm) and patient 3 (from 0.42 cm to 0.36 cm) with the new method. The intra-observer volume variation was also significantly reduced (p = 0.00) with the tri-modality method as compared with using the dual-modality method. With the new tri-modality image fusion method smaller inter- and intra-observer variation in GTV definition for the brain tumors can be achieved, which improves the consistency and accuracy for target delineation in individualized radiotherapy.

State of the art survey on MRI brain tumor segmentation.

PubMed

Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

2013-10-01

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

Brain Tumors

MedlinePlus

A brain tumor is a growth of abnormal cells in the tissues of the brain. Brain tumors can be benign, with no cancer cells, ... cancer cells that grow quickly. Some are primary brain tumors, which start in the brain. Others are ...

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI

NASA Astrophysics Data System (ADS)

Pei, Linmin; Reza, Syed M. S.; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M.

2017-03-01

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.

PubMed

Pei, Linmin; Reza, Syed M S; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M

2017-02-11

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

EBG Based Microstrip Patch Antenna for Brain Tumor Detection via Scattering Parameters in Microwave Imaging System.

PubMed

Inum, Reefat; Rana, Md Masud; Shushama, Kamrun Nahar; Quader, Md Anwarul

2018-01-01

A microwave brain imaging system model is envisaged to detect and visualize tumor inside the human brain. A compact and efficient microstrip patch antenna is used in the imaging technique to transmit equivalent signal and receive backscattering signal from the stratified human head model. Electromagnetic band gap (EBG) structure is incorporated on the antenna ground plane to enhance the performance. Rectangular and circular EBG structures are proposed to investigate the antenna performance. Incorporation of circular EBG on the antenna ground plane provides an improvement of 22.77% in return loss, 5.84% in impedance bandwidth, and 16.53% in antenna gain with respect to the patch antenna with rectangular EBG. The simulation results obtained from CST are compared to those obtained from HFSS to validate the design. Specific absorption rate (SAR) of the modeled head tissue for the proposed antenna is determined. Different SAR values are compared with the established standard SAR limit to provide a safety regulation of the imaging system. A monostatic radar-based confocal microwave imaging algorithm is applied to generate the image of tumor inside a six-layer human head phantom model. S -parameter signals obtained from circular EBG loaded patch antenna in different scanning modes are utilized in the imaging algorithm to effectively produce a high-resolution image which reliably indicates the presence of tumor inside human brain.

EBG Based Microstrip Patch Antenna for Brain Tumor Detection via Scattering Parameters in Microwave Imaging System

PubMed Central

Rana, Md. Masud; Shushama, Kamrun Nahar; Quader, Md. Anwarul

2018-01-01

A microwave brain imaging system model is envisaged to detect and visualize tumor inside the human brain. A compact and efficient microstrip patch antenna is used in the imaging technique to transmit equivalent signal and receive backscattering signal from the stratified human head model. Electromagnetic band gap (EBG) structure is incorporated on the antenna ground plane to enhance the performance. Rectangular and circular EBG structures are proposed to investigate the antenna performance. Incorporation of circular EBG on the antenna ground plane provides an improvement of 22.77% in return loss, 5.84% in impedance bandwidth, and 16.53% in antenna gain with respect to the patch antenna with rectangular EBG. The simulation results obtained from CST are compared to those obtained from HFSS to validate the design. Specific absorption rate (SAR) of the modeled head tissue for the proposed antenna is determined. Different SAR values are compared with the established standard SAR limit to provide a safety regulation of the imaging system. A monostatic radar-based confocal microwave imaging algorithm is applied to generate the image of tumor inside a six-layer human head phantom model. S-parameter signals obtained from circular EBG loaded patch antenna in different scanning modes are utilized in the imaging algorithm to effectively produce a high-resolution image which reliably indicates the presence of tumor inside human brain. PMID:29623087

Quantitative imaging of magnesium distribution at single-cell resolution in brain tumors and infiltrating tumor cells with secondary ion mass spectrometry (SIMS)

PubMed Central

Chandra, Subhash; Parker, Dylan J.; Barth, Rolf F.; Pannullo, Susan C.

2016-01-01

Glioblastoma multiforme (GBM) is one of the deadliest forms of human brain tumors. The infiltrative pattern of growth of these tumors includes the spread of individual and/or clusters of tumor cells at some distance from the main tumor mass in parts of the brain protected by an intact blood-brain-barrier. Pathophysiological studies of GBM could be greatly enhanced by analytical techniques capable of in situ single-cell resolution measurements of infiltrating tumor cells. Magnesium homeostasis is an area of active investigation in high grade gliomas. In the present study, we have used the F98 rat glioma as a model of human GBM and an elemental/isotopic imaging technique of secondary ion mass spectrometry (SIMS), a CAMECA IMS-3f ion microscope, for studying Mg distributions with single-cell resolution in freeze-dried brain tissue cryosections. Quantitative observations were made on tumor cells in the main tumor mass, contiguous brain tissue, and infiltrating tumor cells in adjacent normal brain. The brain tissue contained a significantly lower total Mg concentration of 4.70 ± 0.93 mmol/Kg wet weight (mean ± SD) in comparison to 11.64 ± 1.96 mmol/Kg wet weight in tumor cells of the main tumor mass and 10.72 ± 1.76 mmol/Kg wet weight in infiltrating tumor cells (p<0.05). The nucleus of individual tumor cells contained elevated levels of bound Mg. These observations demonstrate enhanced Mg-influx and increased binding of Mg in tumor cells and provide strong support for further investigation of GBMs for altered Mg homeostasis and activation of Mg-transporting channels as possible therapeutic targets. PMID:26703785

3-D in vivo brain tumor geometry study by scaling analysis

NASA Astrophysics Data System (ADS)

Torres Hoyos, F.; Martín-Landrove, M.

2012-02-01

A new method, based on scaling analysis, is used to calculate fractal dimension and local roughness exponents to characterize in vivo 3-D tumor growth in the brain. Image acquisition was made according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal magnetic resonance T1-weighted images, and comprising the brain volume for image registration. Image segmentation was performed by the application of the k-means procedure upon contrasted images. We analyzed glioblastomas, astrocytomas, metastases and benign brain tumors. The results show significant variations of the parameters depending on the tumor stage and histological origin.

Patient-specific semi-supervised learning for postoperative brain tumor segmentation.

PubMed

Meier, Raphael; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2014-01-01

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Perylene-diimide-based nanoparticles as highly efficient photoacoustic agents for deep brain tumor imaging in living mice

DOE PAGES

Fan, Quli; Cheng, Kai; Yang, Zhen; ...

2014-11-06

In order to promote preclinical and clinical applications of photoacoustic imaging, novel photoacoustic contrast agents are highly desired for molecular imaging of diseases, especially for deep tumor imaging. In this paper, perylene-3,4,9,10-tetracarboxylic diiimide-based near-infrared-absorptive organic nanoparticles are reported as an efficient agent for photoacoustic imaging of deep brain tumors in living mice with enhanced permeability and retention effect

Dynamic perfusion CT in brain tumors.

PubMed

Yeung, Timothy Pok Chi; Bauman, Glenn; Yartsev, Slav; Fainardi, Enrico; Macdonald, David; Lee, Ting-Yim

2015-12-01

Dynamic perfusion CT (PCT) is an imaging technique for assessing the vascular supply and hemodynamics of brain tumors by measuring blood flow, blood volume, and permeability-surface area product. These PCT parameters provide information complementary to histopathologic assessments and have been used for grading brain tumors, distinguishing high-grade gliomas from other brain lesions, differentiating true progression from post-treatment effects, and predicting prognosis after treatments. In this review, the basic principles of PCT are described, and applications of PCT of brain tumors are discussed. The advantages and current challenges, along with possible solutions, of PCT are presented. Copyright © 2015. Published by Elsevier Ireland Ltd.

Metabolic brain imaging correlated with clinical features of brain tumors

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

Alavi, J.; Alavi, A.; Dann, R.

1985-05-01

Nineteen adults with brain tumors have been studied with positron emission tomography utilizing FDG. Fourteen had biopsy proven cerebral malignant glioma, one each had meningioma, hemangiopericytoma, primitive neuroectodermal tumor (PNET), two had unbiopsied lesions, and one patient had an area of biopsy proven radiation necrosis. Three different patterns of glucose metabolism are observed: marked increase in metabolism at the site of the known tumor in (10 high grade gliomas and the PNET), lower than normal metabolism at the tumor (in 1 grade II glioma, 3 grade III gliomas, 2 unbiopsied low density nonenhancing lesions, and the meningioma), no abnormality (1more » enhancing glioma, the hemangiopericytoma and the radiation necrosis.) The metabolic rate of the tumor or the surrounding brain did not appear to be correlated with the history of previous irradiation or chemotherapy. Decreased metabolism was frequently observed in the rest of the affected hemisphere and in the contralateral cerebellum. Tumors of high grade or with enhancing CT characteristics were more likely to show increased metabolism. Among the patients with proven gliomas, survival after PETT scan tended to be longer for those with low metabolic activity tumors than for those with highly active tumors. The authors conclude that PETT may help to predict the malignant potential of tumors, and may add useful clinical information to the CT scan.« less

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation

PubMed Central

Ortega, Samuel; M. Callicó, Gustavo; Juárez, Eduardo; Bulters, Diederik; Szolna, Adam; Piñeiro, Juan F.; Sosa, Coralia; J. O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Morera, Jesús; Ravi, Daniele; Kiran, B. Ravi; Vega, Aurelio; Báez-Quevedo, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Sarmiento, Roberto

2018-01-01

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes. PMID:29389893

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

PubMed

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

2018-02-01

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

NASA Astrophysics Data System (ADS)

Lindner, Lydia; Pfarrkirchner, Birgit; Gsaxner, Christina; Schmalstieg, Dieter; Egger, Jan

2018-03-01

Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neither accurate nor reliable, there exists a need for objective, robust and fast automated segmentation methods that provide competitive performance. Therefore, deep learning based approaches are gaining interest in the field of medical image segmentation. When the training data set is large enough, deep learning approaches can be extremely effective, but in domains like medicine, only limited data is available in the majority of cases. Due to this reason, we propose a method that allows to create a large dataset of brain MRI (Magnetic Resonance Imaging) images containing synthetic brain tumors - glioblastomas more specifically - and the corresponding ground truth, that can be subsequently used to train deep neural networks.

Digital tumor fluoroscopy (DTF)--a new direct imaging system in the therapy planning for brain tumors.

PubMed

Herbst, M; Fröder, M

1990-01-01

Digital Tumor Fluoroscopy is an expanded x-ray video chain optimized to iodine contrast with an extended Gy scale up to 64000 Gy values. Series of pictures are taken before and after injection of contrast medium. With the most recent unit, up to ten images can be taken and stored. The microprogrammable processor allows the subtraction of images recorded at any moment of the examination. Dynamic views of the distribution of contrast medium in the intravasal and extravasal spaces of brain and tumor tissue are gained by the subtraction of stored images. Tumors can be differentiated by studying the storage and drainage behavior of the contrast medium during the period of examination. Meningiomas store contrast medium very intensively during the whole time of investigation, whereas astrocytomas grade 2-3 pick it up less strongly at the beginning and release it within 2 min. Glioblastomas show a massive but delayed accumulation of contrast medium and a decreased flow-off-rate. In comparison with radiography and MR-imaging the most important advantage of Digital Tumor Fluoroscopy is that direct information on tumor localization is gained in relation to the skull-cap. This enables the radiotherapist to mark the treatment field directly on the skull. Therefore it is no longer necessary to calculate the tumor volume from several CT scans for localization. In radiotherapy Digital Tumor Fluoroscopy a unit combined with a simulator can replace CT planning. This would help overcome the disadvantages arising from the lack of a collimating system, and the inaccuracies which result from completely different geometric relationships between a CT unit and a therapy machine.

Brain Tumor Symptoms

MedlinePlus

... Fatigue Other Symptoms Diagnosis Types of Tumors Risk Factors Brain Tumor Statistics 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 ...

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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%.

Quantitative assessment of Cerenkov luminescence for radioguided brain tumor resection surgery

NASA Astrophysics Data System (ADS)

Klein, Justin S.; Mitchell, Gregory S.; Cherry, Simon R.

2017-05-01

Cerenkov luminescence imaging (CLI) is a developing imaging modality that detects radiolabeled molecules via visible light emitted during the radioactive decay process. We used a Monte Carlo based computer simulation to quantitatively investigate CLI compared to direct detection of the ionizing radiation itself as an intraoperative imaging tool for assessment of brain tumor margins. Our brain tumor model consisted of a 1 mm spherical tumor remnant embedded up to 5 mm in depth below the surface of normal brain tissue. Tumor to background contrast ranging from 2:1 to 10:1 were considered. We quantified all decay signals (e±, gamma photon, Cerenkov photons) reaching the brain volume surface. CLI proved to be the most sensitive method for detecting the tumor volume in both imaging and non-imaging strategies as assessed by contrast-to-noise ratio and by receiver operating characteristic output of a channelized Hotelling observer.

Brain tumor - children

MedlinePlus

... children; Neuroglioma - children; Oligodendroglioma - children; Meningioma - children; Cancer - brain tumor (children) ... The cause of primary brain tumors is unknown. Primary brain tumors may ... (spread to nearby areas) Cancerous (malignant) Brain tumors ...

Effect of intravenous gadolinium-DTPA on diffusion-weighted imaging of brain tumors: a short temporal interval assessment.

PubMed

Li, Xiang; Qu, Jin-Rong; Luo, Jun-Peng; Li, Jing; Zhang, Hong-Kai; Shao, Nan-Nan; Kwok, Keith; Zhang, Shou-Ning; Li, Yan-le; Liu, Cui-Cui; Zee, Chi-Shing; Li, Hai-Liang

2014-09-01

To determine the effect of intravenous administration of gadolinium (Gd) contrast medium (Gd-DTPA) on diffusion-weighted imaging (DWI) for the evaluation of normal brain parenchyma vs. brain tumor following a short temporal interval. Forty-four DWI studies using b values of 0 and 1000 s/mm(2) were performed before, immediately after, 1 min after, 3 min after, and 5 min after the administration of Gd-DTPA on 62 separate lesions including 15 meningioma, 17 glioma and 30 metastatic lesions. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) values of the brain tumor lesions and normal brain tissues were measured on pre- and postcontrast images. Statistical analysis using paired t-test between precontrast and postcontrast data were obtained on three brain tumors and normal brain tissue. The SNR and CNR of brain tumors and the SNR of normal brain tissue showed no statistical differences between pre- and postcontrast (P > 0.05). The ADC values on the three cases of brain tumors demonstrated significant initial increase on the immediate time point (P < 0.01) and decrease on following the 1 min time point (P < 0.01) after contrast. Significant decrease of ADC value was still found at 3min and 5min time point in the meningioma group (P < 0.01) with gradual normalization over time. The ADC values of normal brain tissues demonstrated significant initial elevation on the immediately postcontrast DWI sequence (P < 0.01). Contrast medium can cause a slight but statistically significant change on the ADC value within a short temporal interval after the contrast administration. The effect is both time and lesion-type dependent. © 2013 Wiley Periodicals, Inc.

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.

Understanding Brain Tumors

MedlinePlus

... to Know About Brain Tumors . What is a Brain Tumor? A brain tumor is an abnormal growth
 ... Tumors” from Frankly Speaking Frankly Speaking About Cancer: Brain Tumors Download the full book Questions to ask ...

Fluorescence Imaging/Agents in Tumor Resection.

PubMed

Stummer, Walter; Suero Molina, Eric

2017-10-01

Intraoperative fluorescence imaging allows real-time identification of diseased tissue during surgery without being influenced by brain shift and surgery interruption. 5-Aminolevulinic acid, useful for malignant gliomas and other tumors, is the most broadly explored compound approved for fluorescence-guided resection. Intravenous fluorescein sodium has recently received attention, highlighting tumor tissue based on extravasation at the blood-brain barrier (defective in many brain tumors). Fluorescein in perfused brain, unselective extravasation in brain perturbed by surgery, and propagation with edema are concerns. Fluorescein is not approved but targeted fluorochromes with affinity to brain tumor cells, in development, may offer future advantages. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

INVITED REVIEW – NEUROIMAGING RESPONSE ASSESSMENT CRITERIA FOR BRAIN TUMORS IN VETERINARY PATIENTS

PubMed Central

Rossmeisl, John H.; Garcia, Paulo A.; Daniel, Gregory B.; Bourland, John Daniel; Debinski, Waldemar; Dervisis, Nikolaos; Klahn, Shawna

2013-01-01

The evaluation of therapeutic response using cross-sectional imaging techniques, particularly gadolinium-enhanced MRI, is an integral part of the clinical management of brain tumors in veterinary patients. Spontaneous canine brain tumors are increasingly recognized and utilized as a translational model for the study of human brain tumors. However, no standardized neuroimaging response assessment criteria have been formulated for use in veterinary clinical trials. Previous studies have found that the pathophysiologic features inherent to brain tumors and the surrounding brain complicate the use of the Response Evaluation Criteria in Solid Tumors (RECIST) assessment system. Objectives of this review are to describe strengths and limitations of published imaging-based brain tumor response criteria and propose a system for use in veterinary patients. The widely used human Macdonald and Response Assessment in Neuro-oncology (RANO) criteria are reviewed and described as to how they can be applied to veterinary brain tumors. Discussion points will include current challenges associated with the interpretation of brain tumor therapeutic responses such as imaging pseudophenomena and treatment-induced necrosis, and how advancements in perfusion imaging, positron emission tomography, and magnetic resonance spectroscopy have shown promise in differentiating tumor progression from therapy-induced changes. Finally, although objective endpoints such as MR-imaging and survival estimates will likely continue to comprise the foundations for outcome measures in veterinary brain tumor clinical trials, we propose that in order to provide a more relevant therapeutic response metric for veterinary patients, composite response systems should be formulated and validated that combine imaging and clinical assessment criteria. PMID:24219161

Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery.

PubMed

Goldberg-Zimring, Daniel; Talos, Ion-Florin; Bhagwat, Jui G; Haker, Steven J; Black, Peter M; Zou, Kelly H

2005-04-01

Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. Tumor volume range was 22,413-85,189 mm3, and range of number of vertices in triangulated models was 3674-6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 x 10(-5) mm(-1). Summary measures yielded a DSC range of 0.89-0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002-0.0028 (pooled median, 0.0005). 3D shapes of tumors may be approximated by using SH for neurosurgical applications.

Brain Tumor Risk Factors

MedlinePlus

... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

Brain Tumor Diagnosis

MedlinePlus

... updates Please leave this field empty Brain Tumor Diagnosis SHARE Home > Brain Tumor Information > Diagnosis Listen In cases where a brain tumor is ... to help the doctor reach a brain tumor diagnosis. These tests may also be able help the ...

A Dual Tracer 18F-FCH/18F-FDG PET Imaging of an Orthotopic Brain Tumor Xenograft Model.

PubMed

Fu, Yilong; Ong, Lai-Chun; Ranganath, Sudhir H; Zheng, Lin; Kee, Irene; Zhan, Wenbo; Yu, Sidney; Chow, Pierce K H; Wang, Chi-Hwa

2016-01-01

Early diagnosis of low grade glioma has been a challenge to clinicians. Positron Emission Tomography (PET) using 18F-FDG as a radio-tracer has limited utility in this area because of the high background in normal brain tissue. Other radiotracers such as 18F-Fluorocholine (18F-FCH) could provide better contrast between tumor and normal brain tissue but with high incidence of false positives. In this study, the potential application of a dual tracer 18F-FCH/18F-FDG-PET is investigated in order to improve the sensitivity of PET imaging for low grade glioma diagnosis based on a mouse orthotopic xenograft model. BALB/c nude mice with and without orthotopic glioma xenografts from U87 MG-luc2 glioma cell line are used for the study. The animals are subjected to 18F-FCH and 18F-FDG PET imaging, and images acquired from two separate scans are superimposed for analysis. The 18F-FCH counts are subtracted from the merged images to identify the tumor. Micro-CT, bioluminescence imaging (BLI), histology and measurement of the tumor diameter are also conducted for comparison. Results show that there is a significant contrast in 18F-FCH uptake between tumor and normal brain tissue (2.65 ± 0.98), but with a high false positive rate of 28.6%. The difficulty of identifying the tumor by 18F-FDG only is also proved in this study. All the tumors can be detected based on the dual tracer technique of 18F-FCH/18F-FDG-PET imaging in this study, while the false-positive caused by 18F-FCH can be eliminated. Dual tracer 18F-FCH/18F-FDG PET imaging has the potential to improve the visualization of low grade glioma. 18F-FCH delineates tumor areas and the tumor can be identified by subtracting the 18F-FCH counts. The sensitivity was over 95%. Further studies are required to evaluate the possibility of applying this technique in clinical trials.

A Dual Tracer 18F-FCH/18F-FDG PET Imaging of an Orthotopic Brain Tumor Xenograft Model

PubMed Central

Ranganath, Sudhir H.; Zheng, Lin; Kee, Irene; Zhan, Wenbo; Yu, Sidney; Chow, Pierce K. H.; Wang, Chi-Hwa

2016-01-01

Early diagnosis of low grade glioma has been a challenge to clinicians. Positron Emission Tomography (PET) using 18F-FDG as a radio-tracer has limited utility in this area because of the high background in normal brain tissue. Other radiotracers such as 18F-Fluorocholine (18F-FCH) could provide better contrast between tumor and normal brain tissue but with high incidence of false positives. In this study, the potential application of a dual tracer 18F-FCH/18F-FDG-PET is investigated in order to improve the sensitivity of PET imaging for low grade glioma diagnosis based on a mouse orthotopic xenograft model. BALB/c nude mice with and without orthotopic glioma xenografts from U87 MG-luc2 glioma cell line are used for the study. The animals are subjected to 18F-FCH and 18F-FDG PET imaging, and images acquired from two separate scans are superimposed for analysis. The 18F-FCH counts are subtracted from the merged images to identify the tumor. Micro-CT, bioluminescence imaging (BLI), histology and measurement of the tumor diameter are also conducted for comparison. Results show that there is a significant contrast in 18F-FCH uptake between tumor and normal brain tissue (2.65 ± 0.98), but with a high false positive rate of 28.6%. The difficulty of identifying the tumor by 18F-FDG only is also proved in this study. All the tumors can be detected based on the dual tracer technique of 18F-FCH/ 18F-FDG-PET imaging in this study, while the false-positive caused by 18F-FCH can be eliminated. Dual tracer 18F-FCH/18F-FDG PET imaging has the potential to improve the visualization of low grade glioma. 18F-FCH delineates tumor areas and the tumor can be identified by subtracting the 18F-FCH counts. The sensitivity was over 95%. Further studies are required to evaluate the possibility of applying this technique in clinical trials. PMID:26844770

Advances in evaluation of primary brain tumors.

PubMed

Chen, Wei; Silverman, Daniel H S

2008-07-01

The evaluation of primary brain tumor is challenging. Neuroimaging plays a significant role. At diagnosis, imaging is needed to establish a differential diagnosis, provide prognostic information, as well as direct biopsy. After the initial treatment, imaging is needed to distinguish recurrent disease from treatment-related changes such as radiation necrosis. In low-grade gliomas, this also includes monitoring anaplastic transformation into high-grade tumors. Recently, targeted treatments have been an extremely active area of research. Evaluation in clinical trials of such targeted treatments demands advanced roles of imaging such as treatment planning, monitoring response, and predicting treatment outcomes. Current clinical gold standard magnetic resonance imaging provides superior structural detail but poor specificity in identifying viable tumors in treated brain with surgery/radiation/chemotherapy. (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) is capable of identifying anaplastic transformation and has prognostic value. The sensitivity and specificity of FDG in evaluating recurrent tumor and treatment-induced changes can be significantly improved by coregistration with magnetic resonance imaging and potentially by delayed imaging 3 to 8 hours after injection. Amino acid PET tracers can be more sensitive than FDG in imaging some recurrent tumors, in particular recurrent low-grade tumors. They are also promising for differentiating between recurrent tumors and treatment-induced changes. Newer PET tracers to image important aspects of tumor biology have been actively studied. Tracers for imaging membrane transport such as (18)F-choline have shown promise in differential diagnosis. (18)F-labeled nucleotide analogs such as 3'-deoxy-3'-[(18)F]-fluorothymidine (FLT) and (18)F-FMAU have been developed to image proliferation. The use of FLT has demonstrated prognostic power in predicting treatment response in patients treated with an antiangiogenic

Manganese-containing Prussian blue nanoparticles for imaging of pediatric brain tumors

PubMed Central

Dumont, Matthieu F; Yadavilli, Sridevi; Sze, Raymond W; Nazarian, Javad; Fernandes, Rohan

2014-01-01

Pediatric brain tumors (PBTs) are a leading cause of death in children. For an improved prognosis in patients with PBTs, there is a critical need to develop molecularly-specific imaging agents to monitor disease progression and response to treatment. In this paper, we describe manganese-containing Prussian blue nanoparticles as agents for molecular magnetic resonance imaging (MRI) and fluorescence-based imaging of PBTs. Our core-shell nanoparticles consist of a core lattice structure that incorporates and retains paramagnetic Mn2+ ions, and generates MRI contrast (both negative and positive). The biofunctionalized shell is comprised of fluorescent avidin, which serves the dual purpose of enabling fluorescence imaging and functioning as a platform for the attachment of biotinylated ligands that target PBTs. The surfaces of our nanoparticles are modified with biotinylated antibodies targeting neuron-glial antigen 2 or biotinylated transferrin. Both neuron-glial antigen 2 and the transferrin receptor are protein markers overexpressed in PBTs. We describe the synthesis, biofunctionalization, and characterization of these multimodal nanoparticles. Further, we demonstrate the MRI and fluorescence imaging capabilities of manganese-containing Prussian blue nanoparticles in vitro. Finally, we demonstrate the potential of these nanoparticles as PBT imaging agents by measuring their organ and brain biodistribution in an orthotopic mouse model of PBTs using ex vivo fluorescence imaging. PMID:24920896

Semi-automated brain tumor and edema segmentation using MRI.

PubMed

Xie, Kai; Yang, Jie; Zhang, Z G; Zhu, Y M

2005-10-01

Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67+/-4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84+/-0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54+/-4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79+/-0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. The results of this research may have potential applications, both as a staging procedure and a method of

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.

Brain tumor response to nimotuzumab treatment evaluated on magnetic resonance imaging.

PubMed

Dalmau, Evelio Rafael González; Cabal Mirabal, Carlos; Martínez, Giselle Saurez; Dávila, Agustín Lage; Suárez, José Carlos Ugarte; Cabanas Armada, Ricardo; Rodriguez Cruz, Gretel; Darias Zayas, Daniel; Castillo, Martha Ríos; Valle Garrido, Luis; Sotolongo, Luis Quevedo; Fernández, Mercedes Monzón

2014-02-01

Nimotuzumab, a humanized monoclonal antibody anti-epidermal growth factor receptor, has been shown to improve survival and quality of life in patients with pediatric malignant brain tumor. It is necessary, however, to increase the objective response criteria to define the optimal therapeutic schedule. The aim of this study was to obtain magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) quantitative information related to dimensions and morphology, molecular mobility and metabolic activity of the lesion and surroundings in order to evaluate any changes through time. Fourteen pediatric patients treated with nimotuzumab were evaluated on MRI and MRS for >2 years. Each patient was their own control. The MRI/MRS pulse sequence parameters were standardized to ensure experimental reproducibility. A total of 71.4% of patients had stable disease; 21.4% had objective response and 7.1% had progression of disease during the >2 year evaluation period. MRI/MRS data with clinical information provide a clearer picture of treatment response and confirm once again that nimotuzumab is effective in the treatment of pediatric brain tumor. These imaging procedures can be a useful tool for the clinical evaluation of study protocol in clinical practice. © 2013 The Authors. Pediatrics International © 2013 Japan Pediatric Society.

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

PubMed

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

2014-10-01

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

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

PubMed

Blessy, S A Praylin Selva; Sulochana, C Helen

2015-01-01

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

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

Brain tumor - primary - adults

MedlinePlus

... Vestibular schwannoma (acoustic neuroma) - adults; Meningioma - adults; Cancer - brain tumor (adults) ... Primary brain tumors include any tumor that starts in the brain. Primary brain tumors can start from brain cells, ...

Towards tailored management of malignant brain tumors with nanotheranostics.

PubMed

Aparicio-Blanco, Juan; Torres-Suárez, Ana-Isabel

2018-06-01

Malignant brain tumors still represent an unmet medical need given their rapid progression and often fatal outcome within months of diagnosis. Given their extremely heterogeneous nature, the assumption that a single therapy could be beneficial for all patients is no longer plausible. Hence, early feedback on drug accumulation at the tumor site and on tumor response to treatment would help tailor therapies to each patient's individual needs for personalized medicine. In this context, at the intersection between imaging and therapy, theranostic nanomedicine is a promising new technique for individualized management of malignant brain tumors. Although brain nanotheranostics has yet to be translated into clinical practice, this field is now a research hotspot due to the growing demand for personalized therapies. In this review, the barriers to the clinical implementation of theranostic nanomedicine for tracking tumor responses to treatment and for guiding stimulus-activated therapies and surgical resection of malignant brain tumors are discussed. Likewise, the criteria that nanotheranostic systems need to fulfil to become clinically relevant formulations are analyzed in depth, focusing on theranostic agents already tested in vivo. Currently, magnetic nanoparticles exploiting brain targeting strategies represent the first generation of preclinical theranostic nanomedicines for the management of malignant brain tumors. The development of nanocarriers that can be used both in imaging studies and the treatment of brain tumors could help identify which patients are most and least likely to respond to a given treatment. This will enable clinicians to adapt the therapy to the needs of the patient and avoid overdosing non-responders. Given the many different approaches to non-invasive techniques for imaging and treating brain tumors, it is important to focus on the strategies most likely to be implemented and to design the most feasible theranostic biomaterials that will bring

Clinical applications of modern imaging technology: stereo image formation and location of brain cancer

NASA Astrophysics Data System (ADS)

Wang, Dezong; Wang, Jinxiang

1994-05-01

It is very important to locate the tumor for a patient, who has cancer in his brain. If he only gets X-CT or MRI pictures, the doctor does not know the size, shape location of the tumor and the relation between the tumor and other organs. This paper presents the formation of stereo images of cancer. On the basis of color code and color 3D reconstruction. The stereo images of tumor, brain and encephalic truncus are formed. The stereo image of cancer can be round on X, Y, Z-coordinates to show the shape from different directions. In order to show the location of tumor, stereo image of tumor and encephalic truncus are provided on different angles. The cross section pictures are also offered to indicate the relation of brain, tumor and encephalic truncus on cross sections. In this paper the calculating of areas, volume and the space between cancer and the side of the brain are also described.

Research of the multimodal brain-tumor segmentation algorithm

NASA Astrophysics Data System (ADS)

Lu, Yisu; Chen, Wufan

2015-12-01

It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.

"Facilitated" amino acid transport is upregulated in brain tumors.

PubMed

Miyagawa, T; Oku, T; Uehara, H; Desai, R; Beattie, B; Tjuvajev, J; Blasberg, R

1998-05-01

The goal of this study was to determine the magnitude of "facilitated" amino acid transport across tumor and brain capillaries and to evaluate whether amino acid transporter expression is "upregulated" in tumor vessels compared to capillaries in contralateral brain tissue. Aminocyclopentane carboxylic acid (ACPC), a non-metabolized [14C]-labeled amino acid, and a reference molecule for passive vascular permeability, [67Ga]-gallium-diethylenetriaminepentaacetic acid (Ga-DTPA), were used in these studies. Two experimental rat gliomas were studied (C6 and RG2). Brain tissue was rapidly processed for double label quantitative autoradiography 10 minutes after intravenous injection of ACPC and Ga-DTPA. Parametric images of blood-to-brain transport (K1ACPC and K1Ga-DTPA, microL/min/g) produced from the autoradiograms and the histology were obtained from the same tissue section. These three images were registered in an image array processor; regions of interest in tumor and contralateral brain were defined on morphologic criteria (histology) and were transferred to the autoradiographic images to obtain mean values. The facilitated component of ACPC transport (deltaK1ACPC) was calculated from the K1ACPC and K1Ga-DTPA data, and paired comparisons between tumor and contralateral brain were performed. ACPC flux, K1ACPC, across normal brain capillaries (22.6 +/- 8.1 microL/g/min) was >200-fold greater than that of Ga-DTPA (0.09 +/- 0.04 microL/g/min), and this difference was largely (approximately 90%) due to facilitated ACPC transport. Substantially higher K1ACPC values compared to corresponding K1DTPA values were also measured in C6 and RG2 gliomas. The deltaK1ACPC values for C6 glioma were more than twice that of contralateral brain cortex. K1ACPC and deltaK1ACPC values for RG2 gliomas was not significantly higher than that of contralateral cortex, although a approximately 2-fold difference in facilitated transport is obtained after normalization for differences in capillary

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

Novel Nanotechnologies for Brain Cancer Therapeutics and Imaging.

PubMed

Ferroni, Letizia; Gardin, Chiara; Della Puppa, Alessandro; Sivolella, Stefano; Brunello, Giulia; Scienza, Renato; Bressan, Eriberto; D'Avella, Domenico; Zavan, Barbara

2015-11-01

Despite progress in surgery, radiotherapy, and in chemotherapy, an effective curative treatment of brain cancer, specifically malignant gliomas, does not yet exist. The efficacy of current anti-cancer strategies in brain tumors is limited by the lack of specific therapies against malignant cells. Besides, the delivery of the drugs to brain tumors is limited by the presence of the blood-brain barrier. Nanotechnology today offers a unique opportunity to develop more effective brain cancer imaging and therapeutics. In particular, the development of nanocarriers that can be conjugated with several functional molecules including tumor-specific ligands, anticancer drugs, and imaging probes, can provide new devices which are able to overcome the difficulties of the classical strategies. Nanotechnology-based approaches hold great promise for revolutionizing brain cancer medical treatments, imaging, and diagnosis.

Statistical Validation of Brain Tumor Shape Approximation via Spherical Harmonics for Image-Guided Neurosurgery1

PubMed Central

Goldberg-Zimring, Daniel; Talos, Ion-Florin; Bhagwat, Jui G.; Haker, Steven J.; Black, Peter M.; Zou, Kelly H.

2005-01-01

Rationale and Objectives Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. Materials and Methods Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. Results Tumor volume range was 22413–85189 mm3, and range of number of vertices in triangulated models was 3674–6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 × 10−5 mm−1. Summary measures yielded a DSC range of 0.89–0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002–0.0028 (pooled median, 0.0005). Conclusion 3D shapes of tumors may be approximated by using SH for neurosurgical applications. PMID:15831419

Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications.

PubMed

Hamamci, Andac; Kucuk, Nadir; Karaman, Kutlay; Engin, Kayihan; Unal, Gozde

2012-03-01

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.

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

PubMed

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

2013-08-01

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

[Tumor segmentation of brain MRI with adaptive bandwidth mean shift].

PubMed

Hou, Xiaowen; Liu, Qi

2014-10-01

In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.

An accurate segmentation method for volumetry of brain tumor in 3D MRI

NASA Astrophysics Data System (ADS)

Wang, Jiahui; Li, Qiang; Hirai, Toshinori; Katsuragawa, Shigehiko; Li, Feng; Doi, Kunio

2008-03-01

Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Six-color intravital two-photon imaging of brain tumors and their dynamic microenvironment.

PubMed

Ricard, Clément; Debarbieux, Franck Christian

2014-01-01

The majority of intravital studies on brain tumor in living animal so far rely on dual color imaging. We describe here a multiphoton imaging protocol to dynamically characterize the interactions between six cellular components in a living mouse. We applied this methodology to a clinically relevant glioblastoma multiforme (GBM) model designed in reporter mice with targeted cell populations labeled by fluorescent proteins of different colors. This model permitted us to make non-invasive longitudinal and multi-scale observations of cell-to-cell interactions. We provide examples of such 5D (x,y,z,t,color) images acquired on a daily basis from volumes of interest, covering most of the mouse parietal cortex at subcellular resolution. Spectral deconvolution allowed us to accurately separate each cell population as well as some components of the extracellular matrix. The technique represents a powerful tool for investigating how tumor progression is influenced by the interactions of tumor cells with host cells and the extracellular matrix micro-environment. It will be especially valuable for evaluating neuro-oncological drug efficacy and target specificity. The imaging protocol provided here can be easily translated to other mouse models of neuropathologies, and should also be of fundamental interest for investigations in other areas of systems biology.

Childhood Brain Tumor Epidemiology: A Brain Tumor Epidemiology Consortium Review

PubMed Central

Johnson, Kimberly J.; Cullen, Jennifer; Barnholtz-Sloan, Jill S.; Ostrom, Quinn T.; Langer, Chelsea E.; Turner, Michelle C.; McKean-Cowdin, Roberta; Fisher, James L.; Lupo, Philip J.; Partap, Sonia; Schwartzbaum, Judith A.; Scheurer, Michael E.

2014-01-01

Childhood brain tumors are the most common pediatric solid tumor and include several histological subtypes. Although progress has been made in improving survival rates for some subtypes, understanding of risk factors for childhood brain tumors remains limited to a few genetic syndromes and ionizing radiation to the head and neck. In this report, we review descriptive and analytical epidemiology childhood brain tumor studies from the past decade and highlight priority areas for future epidemiology investigations and methodological work that is needed to advance our understanding of childhood brain tumor causes. Specifically, we summarize the results of a review of studies published since 2004 that have analyzed incidence and survival in different international regions and that have examined potential genetic, immune system, developmental and birth characteristics, and environmental risk factors. PMID:25192704

Brain Tumors (For Parents)

MedlinePlus

... Staying Safe Videos for Educators Search English Español Brain Tumors KidsHealth / For Parents / Brain Tumors What's in ... radiation therapy or chemotherapy, or both. Types of Brain Tumors There are many different types of brain ...

[Magnetic resonance imaging of brain tumors].

PubMed

Prayer, Daniela; Brugger, P C

2002-01-01

Investigating intracranial tumors, different MR-related methods permit not only morphological visualization of lesions but also give insights into their metabolism, resulting in information about the biological qualities of the respective tumor. Magnetic resonance protocols are selected based on the type and timing of onset of clinical signs. Combined information from imaging studies and spectroscopy facilitates the differential diagnosis between blastomatous and non-blastomatous lesions before and after therapy.

Childhood Brain Tumors

MedlinePlus

Brain tumors are abnormal growths inside the skull. They are among the most common types of childhood ... still be serious. Malignant tumors are cancerous. Childhood brain and spinal cord tumors can cause headaches and ...

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.

PubMed

Zhao, Xiaomei; Wu, Yihong; Song, Guidong; Li, Zhenye; Zhang, Yazhuo; Fan, Yong

2018-01-01

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans. Copyright © 2017 Elsevier B.V. All rights reserved.

Examination of Blood-Brain Barrier (BBB) Integrity In A Mouse Brain Tumor Model

PubMed Central

On, Ngoc; Mitchell, Ryan; Savant, Sanjot D.; Bachmeier, Corbin. J.; Hatch, Grant M.; Miller, Donald W.

2013-01-01

The present study evaluates, both functionally and biochemically, brain tumor-induced alterations in brain capillary endothelial cells. Brain tumors were induced in Balb/c mice via intracranial injection of Lewis Lung carcinoma (3LL) cells into the right hemisphere of the mouse brain using stereotaxic apparatus. Blood-brain barrier (BBB) permeability was assessed at various stages of tumor development, using both radiolabeled tracer permeability and magnetic resonance imaging (MRI) with gadolinium diethylene-triamine-pentaacetate contrast enhancement (Gad-DTPA). The expression of the drug efflux transporter, P-glycoprotein (P-gp), in the BBB at various stages of tumor development was also evaluated by Western blot and immunohistochemistry. Median mouse survival following tumor cell injection was 17 days. The permeability of the BBB to 3H-mannitol was similar in both brain hemispheres at 7 and 10 days post-injection. By day 15, there was a 2-fold increase in 3H-mannitol permeability in the tumor bearing hemispheres compared to the non-tumor hemispheres. Examination of BBB permeability with Gad-DTPA contrast enhanced MRI indicated cerebral vascular permeability changes were confined to the tumor area. The permeability increase observed at the later stages of tumor development correlated with an increase in cerebral vascular volume suggesting angiogenesis within the tumor bearing hemisphere. Furthermore, the Gad-DPTA enhancement observed within the tumor area was significantly less than Gad-DPTA enhancement within the circumventricular organs not protected by the BBB. Expression of P-gp in both the tumor bearing and non-tumor bearing portions of the brain appeared similar at all time points examined. These studies suggest that although BBB integrity is altered within the tumor site at later stages of development, the BBB is still functional and limiting in terms of solute and drug permeability in and around the tumor. PMID:23184143

Pediatric Brain Tumor Foundation

MedlinePlus

... navigate their brain tumor diagnosis. WATCH AND SHARE Brain tumors and their treatment can be deadly so ... Pediatric Central Nervous System Cancers Read more >> Pediatric Brain Tumor Foundation 302 Ridgefield Court, Asheville, NC 28806 ...

PET/CT imaging of the diapeutic alkylphosphocholine analog 124I-CLR1404 in high and low-grade brain tumors

PubMed Central

Hall, Lance T; Titz, Benjamin; Robins, H Ian; Bednarz, Bryan P; Perlman, Scott B; Weichert, Jamey P; Kuo, John S

2017-01-01

CLR1404 is a cancer-selective alkyl phosphocholine (APC) analog that can be radiolabeled with 124I for PET imaging, 131I for targeted radiotherapy and/or SPECT imaging, or 125I for targeted radiotherapy. Studies have demonstrated avid CLR1404 uptake and prolonged retention in a broad spectrum of preclinical tumor models. The purpose of this pilot trial was to demonstrate avidity of 124I-CLR1404 in human brain tumors and develop a framework to evaluate this uptake for use in larger studies. 12 patients (8 men and 4 women; mean age of 43.9 ± 15.1 y; range 23-66 y) with 13 tumors were enrolled. Eleven patients had suspected tumor recurrence and 1 patient had a new diagnosis of high grade tumor. Patients were injected with 185 MBq ± 10% of 124I-CLR1404 followed by PET/CT imaging at 6-, 24-, and 48-hour. 124I-CLR1404 PET uptake was assessed qualitatively and compared with MRI. After PET image segmentation SUV values and tumor to background ratios were calculated. There was no significant uptake of 124I-CLR1404 in normal brain. In tumors, uptake tended to increase to 48 hours. Positive uptake was detected in 9 of 13 lesions: 5/5 high grade tumors, 1/2 low grade tumors, 1/1 meningioma, and 2/4 patients with treatment related changes. 124I-CLR1404 uptake was not detected in 1/2 low grade tumors, 2/4 lesions from treatment related changes, and 1/1 indeterminate lesion. For 6 malignant tumors, the average tumor to background ratios (TBR) were 9.32 ± 4.33 (range 3.46 to 15.42) at 24 hours and 10.04 ± 3.15 (range 5.17 to 13.17) at 48 hours. For 2 lesions from treatment related change, the average TBR were 5.05 ± 0.4 (range 4.76 to 5.33) at 24 hours and 4.88 ± 1.19 (range 4.04 to 5.72) at 48 hours. PET uptake had areas of both concordance and discordance compared with MRI. 124I-CLR1404 PET demonstrated avid tumor uptake in a variety of brain tumors with high tumor-to-background ratios. There were regions of concordance and discordance compared with MRI, which has

Brain tumor segmentation with Vander Lugt correlator based active contour.

PubMed

Essadike, Abdelaziz; Ouabida, Elhoussaine; Bouzid, Abdenbi

2018-07-01

The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation. In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages. The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image. Results reported on BRATS database reveal that our proposed system improves over the recently published

ALA-induced PpIX spectroscopy for brain tumor image-guided surgery

NASA Astrophysics Data System (ADS)

Valdes, Pablo A.; Leblond, Frederic; Kim, Anthony; Harris, Brent T.; Wilson, Brian C.; Paulsen, Keith D.; Roberts, David W.

2011-03-01

Maximizing the extent of brain tumor resection correlates with improved survival and quality of life outcomes in patients. Optimal surgical resection requires accurate discrimination between normal and abnormal, cancerous tissue. We present our recent experience using quantitative optical spectroscopy in 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence-guided resection. Exogenous administration of ALA leads to preferential accumulation in tumor tissue of the fluorescent compound, PpIX, which can be used for in vivo surgical guidance. Using the state of the art approach with a fluorescence surgical microscope, we have been able to visualize a subset of brain tumors, but the sensitivity and accuracy of fluorescence detection for tumor tissue with this system are low. To take full advantage of the biological selectivity of PpIX accumulation in brain tumors, we used a quantitative optical spectroscopy system for in vivo measurements of PpIX tissue concentrations. We have shown that, using our quantitative approach for determination of biomarker concentrations, ALA-induced PpIX fluorescence-guidance can achieve accuracies of greater than 90% for most tumor histologies. Here we show multivariate analysis of fluorescence and diffuse reflectance signals in brain tumors with comparable diagnostic performance to our previously reported quantitative approach. These results are promising, since they show that technological improvements in current fluorescence-guided surgical technologies and more biologically relevant approaches are required to take full advantage of fluorescent biomarkers, achieve better tumor identification, increase extent of resection, and subsequently, lead to improve survival and quality of life in patients.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

Brain Tumors: The Influence of Tumor Type and Routine MR Imaging Characteristics at BOLD Functional MR Imaging in the Primary Motor Gyrus

PubMed Central

Fraga de Abreu, Vitor Hugo; Peck, Kyung K.; Petrovich-Brennan, Nicole M.; Woo, Kaitlin M.

2016-01-01

Purpose To evaluate the effects of histologic features and anatomic magnetic resonance (MR) imaging characteristics of brain tumors on the functional MR imaging signal in the primary motor cortex (PMC), as false-negative blood oxygen level–dependent (BOLD) functional MR imaging activation can limit the accurate localization of eloquent cortices. Materials and Methods Institutional review board approval was obtained, and informed consent was waived for this HIPAA-compliant retrospective study. It comprised 63 patients referred between 2006 and 2014 for preoperative functional MR imaging localization of the Rolandic cortex. The patients had glioblastoma multiforme (GBM) (n = 20), metastasis (n = 21), or meningioma (n = 22). The volumes of functional MR imaging activation were measured during performance of a bilateral hand motor task. Ratios of functional MR imaging activation were normalized to PMC volume. Statistical analysis was performed for the following: (a) differences between hemispheres within each histologic tumor type (paired Wilcoxon test), (b) differences across tumor types (Kruskal-Wallis and Fisher tests), (c) pairwise tests between tumor types (Mann-Whitney U test), (d) relationships between fast fluid-attenuated inversion recovery (FLAIR) data and enhancement volume with activation (Spearman rank correlation coefficient), and (e) differences in activation volumes by tumor location (Mann-Whitney U test). Results A significant interhemispheric difference was found between the activation volumes in GBMs (mean, 511.43 voxels ± 307.73 [standard deviation] and 330.78 voxels ± 278.95; P < .01) but not in metastases (504.68 voxels ± 220.98 and 460.22 voxels ± 276.83; P = .15) or meningiomas (424.07 voxels ± 247.58 and 415.18 voxels ± 222.36; P = .85). GBMs showed significantly lower activation ratios (median, 0.49; range, 0.04–1.15) than metastases (median, 0.79; range, 0.28–1.66; P = .043) and meningiomas (median, 0.91; range, 0.52–2.05; P

Recent technological advances in pediatric brain tumor surgery.

PubMed

Zebian, Bassel; Vergani, Francesco; Lavrador, José Pedro; Mukherjee, Soumya; Kitchen, William John; Stagno, Vita; Chamilos, Christos; Pettorini, Benedetta; Mallucci, Conor

2017-01-01

X-rays and ventriculograms were the first imaging modalities used to localize intracranial lesions including brain tumors as far back as the 1880s. Subsequent advances in preoperative radiological localization included computed tomography (CT; 1971) and MRI (1977). Since then, other imaging modalities have been developed for clinical application although none as pivotal as CT and MRI. Intraoperative technological advances include the microscope, which has allowed precise surgery under magnification and improved lighting, and the endoscope, which has improved the treatment of hydrocephalus and allowed biopsy and complete resection of intraventricular, pituitary and pineal region tumors through a minimally invasive approach. Neuronavigation, intraoperative MRI, CT and ultrasound have increased the ability of the neurosurgeon to perform safe and maximal tumor resection. This may be facilitated by the use of fluorescing agents, which help define the tumor margin, and intraoperative neurophysiological monitoring, which helps identify and protect eloquent brain.

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

PubMed

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

2013-01-01

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

Imaging proliferation in brain tumors with 18F-FLT PET: comparison with 18F-FDG.

PubMed

Chen, Wei; Cloughesy, Timothy; Kamdar, Nirav; Satyamurthy, Nagichettiar; Bergsneider, Marvin; Liau, Linda; Mischel, Paul; Czernin, Johannes; Phelps, Michael E; Silverman, Daniel H S

2005-06-01

3'-Deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) is a recently developed PET tracer to image tumor cell proliferation. We characterized (18)F-FLT PET of brain gliomas and compared (18)F-FLT with (18)F-FDG PET in side-by-side studies of the same patients. Twenty-five patients with newly diagnosed or previously treated glioma underwent PET with (18)F-FLT and (18)F-FDG on consecutive days. Three stable patients in long-term remission were included as negative control subjects. Tracer kinetics in normal brain and tumor were measured. Uptake of (18)F-FLT and (18)F-FDG was quantified by the standardized uptake value (SUV) and the tumor-to-normal tissue (T/N) ratio. The accuracy of (18)F-FLT and (18)F-FDG PET in evaluating newly diagnosed and recurrent gliomas was compared. More than half of the patients underwent resection after the PET study and correlations between PET uptake and the Ki-67 proliferation index were examined. Patients were monitored for a mean of 15.4 mo (range, 12-20 mo). The predictive power of PET for tumor progression and survival was analyzed using Kaplan-Meier statistics. (18)F-FLT uptake in tumors was rapid, peaking at 5-10 min after injection and remaining stable up to 75 min. Hence, a 30-min scan beginning at 5 min after injection was sufficient for imaging. (18)F-FLT visualized all high-grade (grade III or IV) tumors. Grade II tumor did not show appreciable (18)F-FLT uptake and neither did the stable lesions. The absolute uptake of (18)F-FLT was low (maximum-pixel SUV [SUV(max)], 1.33) but image contrast was better than with (18)F-FDG (T/N ratio, 3.85 vs. 1.49). (18)F-FDG PET studies were negative in 5 patients with recurrent high-grade glioma who subsequently suffered tumor progression within 1-3 mo. (18)F-FLT SUV(max) correlated more strongly with Ki-67 index (r = 0.84; P < 0.0001) than (18)F-FDG SUV(max) (r = 0.51; P = 0.07). (18)F-FLT uptake also had more significant predictive power with respect to tumor progression and survival (P = 0

SU-E-J-212: MR Diffusion Tensor Imaging for Assessment of Tumor and Normal Brain Tissue Responses of Juvenile Pilocytic Astrocytoma Treated by Proton Therapy

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

Hou, P; Park, P; Li, H

Purpose: Diffusion tensor imaging (DTI) can measure molecular mobility at the cellular level, quantified by the apparent diffusion coefficient (ADC). DTI may also reveal axonal fiber directional information in the white matter, quantified by the fractional anisotropy (FA). Juvenile pilocytic astrocytoma (JPA) is a rare brain tumor that occurs in children and young adults. Proton therapy (PT) is increasingly used in the treatment of pediatric brain tumors including JPA. However, the response of both tumors and normal tissues to PT is currently under investigation. We report tumor and normal brain tissue responses for a pediatric case of JPA treated withmore » PT assessed using DTI. Methods: A ten year old male with JPA of the left thalamus received passive scattered PT to a dose of 50.4 Gy (RBE) in 28 fractions. Post PT, the patient has been followed up in seven years. At each follow up, MRI imaging including DTI was performed to assess response. MR images were registered to the treatment planning CT and the GTV mapped onto each MRI. The GTV contour was then mirrored to the right side of brain through the patient’s middle line to represent normal brain tissue. ADC and FA were measured within the ROIs. Results: Proton therapy can completely spare contra lateral brain while the target volume received full prescribed dose. From a series of MRI ADC images before and after PT at different follow ups, the enhancement corresponding to GTV had nearly disappeared more than 2 years after PT. Both ADC and FA demonstrate that contralateral normal brain tissue were not affect by PT and the tumor volume reverted to normal ADC and FA values. Conclusion: DTI allowed quantitative evaluation of tumor and normal brain tissue responses to PT. Further study in a larger cohort is warranted.« less

Optically enhanced blood-brain-barrier crossing of plasmonic-active nanoparticles in preclinical brain tumor animal models

NASA Astrophysics Data System (ADS)

Yuan, Hsiangkuo; Wilson, Christy M.; Li, Shuqin; Fales, Andrew M.; Liu, Yang; Grant, Gerald; Vo-Dinh, Tuan

2014-02-01

Nanotechnology provides tremendous biomedical opportunities for cancer diagnosis, imaging, and therapy. In contrast to conventional chemotherapeutic agents where their actual target delivery cannot be easily imaged, integrating imaging and therapeutic properties into one platform facilitates the understanding of pharmacokinetic profiles, and enables monitoring of the therapeutic process in each individual. Such a concept dubbed "theranostics" potentiates translational research and improves precision medicine. One particular challenging application of theranostics involves imaging and controlled delivery of nanoplatforms across blood-brain-barrier (BBB) into brain tissues. Typically, the BBB hinders paracellular flux of drug molecules into brain parenchyma. BBB disrupting agents (e.g. mannitol, focused ultrasound), however, suffer from poor spatial confinement. It has been a challenge to design a nanoplatform not only acts as a contrast agent but also improves the BBB permeation. In this study, we demonstrated the feasibility of plasmonic gold nanoparticles as both high-resolution optical contrast agent and focalized tumor BBB permeation-inducing agent. We specifically examined the microscopic distribution of nanoparticles in tumor brain animal models. We observed that most nanoparticles accumulated at the tumor periphery or perivascular spaces. Nanoparticles were present in both endothelial cells and interstitial matrices. This study also demonstrated a novel photothermal-induced BBB permeation. Fine-tuning the irradiating energy induced gentle disruption of the vascular integrity, causing short-term extravasation of nanomaterials but without hemorrhage. We conclude that our gold nanoparticles are a powerful biocompatible contrast agent capable of inducing focal BBB permeation, and therefore envision a strong potential of plasmonic gold nanoparticle in future brain tumor imaging and therapy.

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.

Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

NASA Astrophysics Data System (ADS)

Sadeghi-Goughari, M.; Mojra, A.; Sadeghi, S.

2016-02-01

Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors.

Neuronavigation in the surgical management of brain tumors: current and future trends

PubMed Central

Orringer, Daniel A; Golby, Alexandra; Jolesz, Ferenc

2013-01-01

Neuronavigation has become an ubiquitous tool in the surgical management of brain tumors. This review describes the use and limitations of current neuronavigational systems for brain tumor biopsy and resection. Methods for integrating intraoperative imaging into neuronavigational datasets developed to address the diminishing accuracy of positional information that occurs over the course of brain tumor resection are discussed. In addition, the process of integration of functional MRI and tractography into navigational models is reviewed. Finally, emerging concepts and future challenges relating to the development and implementation of experimental imaging technologies in the navigational environment are explored. PMID:23116076

Non-invasive monitoring of hemodynamic changes in orthotropic brain tumor

NASA Astrophysics Data System (ADS)

Kashyap, Dheerendra; Sharma, Vikrant; Liu, Hanli

2007-02-01

Radio surgical interventions such as Gamma Knife and Cyberknife have become attractive as therapeutic interventions. However, one of the drawbacks of cyberknife is radionecrosis, which is caused by excessive radiation to surrounding normal tissues. Radionecrosis occurs in about 10-15% of cases and could have adverse effects leading to death. Currently available imaging techniques have failed to reliably distinguish radionecrosis from tumor growth. Development of imaging techniques that could provide distinction between tumor growth and radionecrosis would give us ability to monitor effects of radiation therapy non-invasively. This paper investigates the use of near infrared spectroscopy (NIRS) as a new technique to monitor the growth of brain tumors. Brain tumors (9L glioma cell line) were implanted in right caudate nucleus of rats (250-300 gms, Male Fisher C) through a guide screw. A new algorithm was developed, which used broadband steady-state reflectance measurements made using a single source-detector pair, to quantify absolute concentrations of hemoglobin derivatives and reduced scattering coefficients. Preliminary results from the brain tumors indicated decreases in oxygen saturation, oxygenated hemoglobin concentrations and increases in deoxygenated hemoglobin concentrations with tumor growth. The study demonstrates that NIRS technology could provide an efficient, noninvasive means of monitoring vascular oxygenation dynamics of brain tumors and further facilitate investigations of efficacy of tumor treatments.

Presurgical functional magnetic resonance imaging in patients with brain tumors.

PubMed

Ravn, Søren; Holmberg, Mats; Sørensen, Preben; Frokjaer, Jens B; Carl, Jesper

2016-01-01

Clinical functional magnetic resonance imaging (fMRI) is still an upcoming diagnostic tool because it is time-consuming to perform the post-scan calculations and interpretations. A standardized and easily used method for the clinical assessment of fMRI scans could decrease the workload and make fMRI more attractive for clinical use. To evaluate a standardized clinical approach for distance measurement between benign brain tumors and eloquent cortex in terms of the ability to predict pre- and postoperative neurological deficits after intraoperative neuronavigation-assisted surgery. A retrospective study of 34 patients. The fMRI data were reanalyzed using a standardized distance measurement procedure combining data from both fMRI and three-dimensional T1 MRI scans. The pre- and postoperative neurological status of each patient was obtained from hospital records. Data analysis was performed using logistic regression analysis to determine whether the distance measured between the tumor margin and fMRI activity could serve as a predictor for neurological deficits. An odds ratio of 0.89 mm(-1) (P = 0.03) was found between the risk of preoperative neurological motor deficits and the tumor-fMRI distance. An odds ratio of 0.82 mm(-1) (P = 0.04) was found between the risk of additional postoperative neurological motor deficits and the tumor-fMRI distance. The tumor was radically removed in 10 cases; five patients experienced additional postoperative motor deficits (tumor-fMRI distance <18 mm) and five did not (tumor-fMRI distance >18 mm) (P = 0.008). This study indicates that the distance measured between the tumor margin and fMRI activation could serve as a valuable predictor of neurological motor deficits. © The Foundation Acta Radiologica 2014.

Brain tumor segmentation in MR slices using improved GrowCut algorithm

NASA Astrophysics Data System (ADS)

Ji, Chunhong; Yu, Jinhua; Wang, Yuanyuan; Chen, Liang; Shi, Zhifeng; Mao, Ying

2015-12-01

The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.

Applications of Ultrasound in the Resection of Brain Tumors

PubMed Central

Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve; Frisken, Sarah; Kapur, Tina; Wells, William; Golby, Alexandra J.

2016-01-01

Neurosurgery makes use of pre-operative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of pre-operative imaging for neuronavigation, however, is diminished by the well characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography have dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies. PMID:27541694

Progress on the diagnosis and evaluation of brain tumors

PubMed Central

Gao, Huile

2013-01-01

Abstract Brain tumors are one of the most challenging disorders encountered, and early and accurate diagnosis is essential for the management and treatment of these tumors. In this article, diagnostic modalities including single-photon emission computed tomography, positron emission tomography, magnetic resonance imaging, and optical imaging are reviewed. We mainly focus on the newly emerging, specific imaging probes, and their potential use in animal models and clinical settings. PMID:24334439

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

Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

PubMed

Zhao, Liang; Wu, Wei; Corso, Jason J

2013-01-01

Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories-the medical image analog to optical flow and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.

Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects.

PubMed

Albi, Angela; Meola, Antonio; Zhang, Fan; Kahali, Pegah; Rigolo, Laura; Tax, Chantal M W; Ciris, Pelin Aksit; Essayed, Walid I; Unadkat, Prashin; Norton, Isaiah; Rathi, Yogesh; Olubiyi, Olutayo; Golby, Alexandra J; O'Donnell, Lauren J

2018-03-01

Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients' white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. Quantitative results of mean tract distortions on the order of 1-2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings. Copyright © 2018 by the American Society of Neuroimaging.

Antisense imaging of gene expression in the brain in vivo

NASA Astrophysics Data System (ADS)

Shi, Ningya; Boado, Ruben J.; Pardridge, William M.

2000-12-01

Antisense radiopharmaceuticals could be used to image gene expression in the brain in vivo, should these polar molecules be made transportable through the blood-brain barrier. The present studies describe an antisense imaging agent comprised of an iodinated peptide nucleic acid (PNA) conjugated to a monoclonal antibody to the rat transferrin receptor by using avidin-biotin technology. The PNA was a 16-mer antisense to the sequence around the methionine initiation codon of the luciferase mRNA. C6 rat glioma cells were permanently transfected with a luciferase expression plasmid, and C6 experimental brain tumors were developed in adult rats. The expression of the luciferase transgene in the tumors in vivo was confirmed by measurement of luciferase enzyme activity in the tumor extract. The [125I]PNA conjugate was injected intravenously in anesthetized animals with brain tumors and killed 2 h later for frozen sectioning of brain and film autoradiography. No image of the luciferase gene expression was obtained after the administration of either the unconjugated antiluciferase PNA or a PNA conjugate that was antisense to the mRNA of a viral transcript. In contrast, tumors were imaged in all rats administered the [125I]PNA that was antisense to the luciferase sequence and was conjugated to the targeting antibody. In conclusion, these studies demonstrate gene expression in the brain in vivo can be imaged with antisense radiopharmaceuticals that are conjugated to a brain drug-targeting system.

The modern brain tumor operating room: from standard essentials to current state-of-the-art.

PubMed

Barnett, Gene H; Nathoo, Narendra

2004-01-01

It is just over a century since successful brain tumor resection. Since then the diagnosis, imaging, and management of brain tumors have improved, in large part due to technological advances. Similarly, the operating room (OR) for brain tumor surgery has increased in complexity and specificity with multiple forms of equipment now considered necessary as technical adjuncts. It is evident that the theme of minimalism in combination with advanced image-guidance techniques and a cohort of sophisticated technologies (e.g., robotics and nanotechnology) will drive changes in the current OR environment for the foreseeable future. In this report we describe what may be regarded today as standard essentials in an operating room for the surgical management of brain tumors and what we believe to be the current 'state-of-the-art' brain tumor OR. Also, we speculate on the additional capabilities of the brain tumor OR of the near future.

The diagnostic accuracy of multiparametric MRI to determine pediatric brain tumor grades and types.

PubMed

Koob, Mériam; Girard, Nadine; Ghattas, Badih; Fellah, Slim; Confort-Gouny, Sylviane; Figarella-Branger, Dominique; Scavarda, Didier

2016-04-01

Childhood brain tumors show great histological variability. The goal of this retrospective study was to assess the diagnostic accuracy of multimodal MR imaging (diffusion, perfusion, MR spectroscopy) in the distinction of pediatric brain tumor grades and types. Seventy-six patients (range 1 month to 18 years) with brain tumors underwent multimodal MR imaging. Tumors were categorized by grade (I-IV) and by histological type (A-H). Multivariate statistical analysis was performed to evaluate the diagnostic accuracy of single and combined MR modalities, and of single imaging parameters to distinguish the different groups. The highest diagnostic accuracy for tumor grading was obtained with diffusion-perfusion (73.24%) and for tumor typing with diffusion-perfusion-MR spectroscopy (55.76%). The best diagnostic accuracy was obtained for tumor grading in I and IV and for tumor typing in embryonal tumor and pilocytic astrocytoma. Poor accuracy was seen in other grades and types. ADC and rADC were the best parameters for tumor grading and typing followed by choline level with an intermediate echo time, CBV for grading and Tmax for typing. Multiparametric MR imaging can be accurate in determining tumor grades (primarily grades I and IV) and types (mainly pilocytic astrocytomas and embryonal tumors) in children.

Focused ultrasound delivery of Raman nanoparticles across the blood-brain barrier: potential for targeting experimental brain tumors.

PubMed

Diaz, Roberto Jose; McVeigh, Patrick Z; O'Reilly, Meaghan A; Burrell, Kelly; Bebenek, Matthew; Smith, Christian; Etame, Arnold B; Zadeh, Gelareh; Hynynen, Kullervo; Wilson, Brian C; Rutka, James T

2014-07-01

Spectral mapping of nanoparticles with surface enhanced Raman scattering (SERS) capability in the near-infrared range is an emerging molecular imaging technique. We used magnetic resonance image-guided transcranial focused ultrasound (TcMRgFUS) to reversibly disrupt the blood-brain barrier (BBB) adjacent to brain tumor margins in rats. Glioma cells were found to internalize SERS capable nanoparticles of 50nm or 120nm physical diameter. Surface coating with anti-epidermal growth factor receptor antibody or non-specific human immunoglobulin G, resulted in enhanced cell uptake of nanoparticles in-vitro compared to nanoparticles with methyl terminated 12-unit polyethylene glycol surface. BBB disruption permitted the delivery of SERS capable spherical 50 or 120nm gold nanoparticles to the tumor margins. Thus, nanoparticles with SERS imaging capability can be delivered across the BBB non-invasively using TcMRgFUS and have the potential to be used as optical tracking agents at the invasive front of malignant brain tumors. This study demonstrates the use of magnetic resonance image-guided transcranial focused ultrasound to open the BBB and enable spectral mapping of nanoparticles with surface enhanced Raman scattering (SERS)-based molecular imaging for experimental tumor tracking. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Biomaterial-based technologies for brain anti-cancer therapeutics and imaging.

PubMed

Orive, G; Ali, O A; Anitua, E; Pedraz, J L; Emerich, D F

2010-08-01

Treating malignant brain tumors represents one of the most formidable challenges in oncology. Contemporary treatment of brain tumors has been hampered by limited drug delivery across the blood-brain barrier (BBB) to the tumor bed. Biomaterials are playing an increasingly important role in developing more effective brain tumor treatments. In particular, polymer (nano)particles can provide prolonged drug delivery directly to the tumor following direct intracerebral injection, by making them physiochemically able to cross the BBB to the tumor, or by functionalizing the material surface with peptides and ligands allowing the drug-loaded material to be systemically administered but still specifically target the tumor endothelium or tumor cells themselves. Biomaterials can also serve as targeted delivery devices for novel therapies including gene therapy, photodynamic therapy, anti-angiogenic and thermotherapy. Nanoparticles also have the potential to play key roles in the diagnosis and imaging of brain tumors by revolutionizing both preoperative and intraoperative brain tumor detection, allowing early detection of pre-cancerous cells, and providing real-time, longitudinal, non-invasive monitoring/imaging of the effects of treatment. Additional efforts are focused on developing biomaterial systems that are uniquely capable of delivering tumor-associated antigens, immunotherapeutic agents or programming immune cells in situ to identify and facilitate immune-mediated tumor cell killing. The continued translation of current research into clinical practice will rely on solving challenges relating to the pharmacology of nanoparticles but it is envisioned that novel biomaterials will ultimately allow clinicians to target tumors and introduce multiple, pharmaceutically relevant entities for simultaneous targeting, imaging, and therapy in a unique and unprecedented manner. Copyright 2010 Elsevier B.V. All rights reserved.

Tumor-specific delivery of BSH-3R for boron neutron capture therapy and positron emission tomography imaging in a mouse brain tumor model.

PubMed

Iguchi, Yoshiya; Michiue, Hiroyuki; Kitamatsu, Mizuki; Hayashi, Yuri; Takenaka, Fumiaki; Nishiki, Tei-Ichi; Matsui, Hideki

2015-07-01

Glioblastoma, a malignant brain tumor with poor disease outcomes, is managed in modern medicine by multimodality therapy. Boron neutron capture therapy (BNCT) is an encouraging treatment under clinical investigation. In malignant cells, BNCT consists of two major factors: neutron radiation and boron uptake. To increase boron uptake in cells, we created a mercapto-closo-undecahydrododecaborate ([B12HnSH](2-)2Na(+), BSH) fused with a short arginine peptide (1R, 2R, 3R) and checked cellular uptake in vitro and in vivo. In a mouse brain tumor model, only BSH with at least three arginine domains could penetrate cell membranes of glioma cells in vitro and in vivo. Furthermore, to monitor the pharmacokinetic properties of these agents in vivo, we fused BSH and BSH-3R with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA); DOTA is a metal chelating agent for labeling positron emission tomography (PET) probe with (64)Cu. We administered BSH-DOTA-(64)Cu and BSH-3R-DOTA-(64)Cu to the tumor model through a mouse tail vein and determined the drugs' pharmacokinetics by PET imaging. BSH-3R showed a high uptake in the tumor area on PET imaging. We concluded that BSH-3R is the ideal boron compound for clinical use during BNCT and that in developing this compound for clinical use, the BSH-3R PET probe is essential for pharmacokinetic imaging. Copyright © 2015 Elsevier Ltd. All rights reserved.

A novel content-based active contour model for brain tumor segmentation.

PubMed

Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

2012-06-01

Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. Copyright © 2012 Elsevier Inc. All rights reserved.

Brain tumor locating in 3D MR volume using symmetry

NASA Astrophysics Data System (ADS)

Dvorak, Pavel; Bartusek, Karel

2014-03-01

This work deals with the automatic determination of a brain tumor location in 3D magnetic resonance volumes. The aim of this work is not the precise segmentation of the tumor and its parts but only the detection of its location. This work is the first step in the tumor segmentation process, an important topic in neuro-image processing. The algorithm expects 3D magnetic resonance volumes of brain containing a tumor. The detection is based on locating the area that breaks the left-right symmetry of the brain. This is done by multi-resolution comparing of corresponding regions in left and right hemisphere. The output of the computation is the probabilistic map of the tumor location. The created algorithm was tested on 80 volumes from publicly available BRATS databases containing 3D brain volumes afflicted by a brain tumor. These pathological structures had various sizes and shapes and were located in various parts of the brain. The locating performance of the algorithm was 85% for T1-weighted volumes, 91% for T1-weighted contrast enhanced volumes, 96% for FLAIR and T2-wieghted volumes and 95% for their combinations.

Focused ultrasound delivery of Raman nanoparticles across the blood-brain barrier: Potential for targeting experimental brain tumors

PubMed Central

Diaz, Roberto Jose; McVeigh, Patrick Z.; O’Reilly, Meaghan A.; Burrell, Kelly; Bebenek, Matthew; Smith, Christian; Etame, Arnold; Zadeh, Gelareh; Hynynen, Kullervo; Wilson, Brian C.; Rutka, James T.

2014-01-01

Spectral mapping of nanoparticles with surface enhanced Raman scattering (SERS) capability in the near-infrared range is an emerging molecular imaging technique. We used magnetic resonance image-guided transcranial focused ultrasound (TcMRgFUS) to reversibly disrupt the blood-brain barrier (BBB) adjacent to brain tumor margins in rats. Glioma cells were found to internalize SERS capable nanoparticles of 50 nm or 120 nm physical diameter. Surface coating with anti-epidermal growth factor receptor antibody or non-specific human immunoglobulin G, resulted in enhanced cell uptake of nanoparticles in-vitro compared to nanoparticles with methyl terminated 12-unit polyethylene glycol surface. BBB disruption permitted the delivery of SERS capable spherical 50 or 120 nm gold nanoparticles to the tumor margins. Thus, nanoparticles with SERS imaging capability can be delivered across the BBB non-invasively using TcMRgFUS and have the potential to be used as optical tracking agents at the invasive front of malignant brain tumors. PMID:24374363

Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model

PubMed Central

Sui, Yi; Wang, He; Liu, Guanzhong; Damen, Frederick W.; Wanamaker, Christian; Li, Yuhua

2015-01-01

Purpose To demonstrate that a new set of parameters (D, β, and μ) from a fractional order calculus (FROC) diffusion model can be used to improve the accuracy of MR imaging for differentiating among low- and high-grade pediatric brain tumors. Materials and Methods The institutional review board of the performing hospital approved this study, and written informed consent was obtained from the legal guardians of pediatric patients. Multi-b-value diffusion-weighted magnetic resonance (MR) imaging was performed in 67 pediatric patients with brain tumors. Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity), and a microstructural quantity μ were calculated by fitting the multi-b-value diffusion-weighted images to an FROC model. D, β, and μ values were measured in solid tumor regions, as well as in normal-appearing gray matter as a control. These values were compared between the low- and high-grade tumor groups by using the Mann-Whitney U test. The performance of FROC parameters for differentiating among patient groups was evaluated with receiver operating characteristic (ROC) analysis. Results None of the FROC parameters exhibited significant differences in normal-appearing gray matter (P ≥ .24), but all showed a significant difference (P < .002) between low- (D, 1.53 μm2/msec ± 0.47; β, 0.87 ± 0.06; μ, 8.67 μm ± 0.95) and high-grade (D, 0.86 μm2/msec ± 0.23; β, 0.73 ± 0.06; μ, 7.8 μm ± 0.70) brain tumor groups. The combination of D and β produced the largest area under the ROC curve (0.962) in the ROC analysis compared with individual parameters (β, 0.943; D,0.910; and μ, 0.763), indicating an improved performance for tumor differentiation. Conclusion The FROC parameters can be used to differentiate between low- and high-grade pediatric brain tumor groups. The combination of FROC parameters or individual parameters may serve as in vivo, noninvasive, and quantitative imaging markers for classifying

Regional brain glucose metabolism in patients with brain tumors before and after radiotherapy

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

Wang, G.J.; Volkow, N.D.; Lau, Y.H.

1994-05-01

This study was performed to measure regional glucose metabolism in nonaffected brain regions of patients with primary or metastatic brain tumors. Seven female and four male patients (mean age 51.5{plus_minus}14.0 years old) were compared with eleven age and sex matched normal subjects. None of the patients had hydrocephalus and/or increased intracranial pressure. Brain glucose metabolism was measured using FDG-PET scan. Five of the patients were reevaluated one week after receiving radiation treatment (RT) to the brain. Patients were on Decadron and/or Dilantin at the time of both scan. PET images were analyzed with a template of 115 nonoverlapping regions ofmore » interest and then grouped into eight gray matter regions on each hemisphere. Brain regions with tumors and edema shown in MR imaging were excluded. Z scores were used to compare individual patients` regional values with those of normal subjects. The number of regional values with Z scores of less than - 3.0 were considered abnormal and were quantified. The mean global glucose metabolic rate (mean of all regions) in nonaffected brain regions of patients was significantly lower than that of normal controls (32.1{plus_minus}9.0 versus 44.8{plus_minus}6.3 {mu}mol/100g/min, p<0.001). Analyses of individual subjects revealed that none of the controls and 8 of the 11 patients had at least one abnormal region. In these 8 patients the regions which were abnormal were most frequently localized in right (n=5) and left occipital (n=6) and right orbital frontal cortex (n=7) whereas the basal ganglia was not affected. Five of the patients who had repeated scans following RT showed decrements in tumor metabolism (41{plus_minus}20.5%) and a significant increase in whole brain metabolism (8.6{plus_minus}5.3%, p<0.001). The improvement in whole brain metabolism after RT suggests that the brain metabolic decrements in the patients were related to the presence of tumoral tissue and not just a medication effect.« less

Growth of melanoma brain tumors monitored by photoacoustic microscopy

NASA Astrophysics Data System (ADS)

Staley, Jacob; Grogan, Patrick; Samadi, Abbas K.; Cui, Huizhong; Cohen, Mark S.; Yang, Xinmai

2010-07-01

Melanoma is a primary malignancy that is known to metastasize to the brain and often causes death. The ability to image the growth of brain melanoma in vivo can provide new insights into its evolution and response to therapies. In our study, we use a reflection mode photoacoustic microscopy (PAM) system to detect the growth of melanoma brain tumor in a small animal model. The melanoma tumor cells are implanted in the brain of a mouse at the beginning of the test. Then, PAM is used to scan the region of implantation in the mouse brain, and the growth of the melanoma is monitored until the death of the animal. It is demonstrated that PAM is capable of detecting and monitoring the brain melanoma growth noninvasively in vivo.

In vivo imaging and characterization of hypoxia-induced neovascularization and tumor invasion.

PubMed

Lungu, Gina F; Li, Meng-Lin; Xie, Xueyi; Wang, Lihong V; Stoica, George

2007-01-01

Hypoxia is a critical event in tumor progression and angiogenesis. Hypoxia can be detected noninvasively by a novel spectroscopic photoacoustic tomography technology (SPAT) and this finding is supported by our molecular biology investigation aimed to elucidate the etiopathogenesis of SPAT detected hypoxia and angiogenesis. The present study provides an integrated approach to define oxygen status (hypoxia) of intracranial tumor xenografts using spectroscopic photoacoustic tomography. Brain tumors can be identified based on their distorted vascular architecture and oxygen saturation (SO2) images. Noninvasive in vivo tumor oxygenation imaging using SPAT is based on the spectroscopic absorption differences between oxyhemoglobin (O2Hb) and deoxyhemoblobin (HHb). Sprague-Dawley rats inoculated intracranially with ENU1564, a carcinogen-induced rat mammary adenocarcinoma cell line, were imaged with SPAT three weeks post inoculation. Proteins important for tumor angiogenesis and invasion were detected in hypoxic brain foci identified by SPAT and were elevated compared with control brain. Immunohistochemistry, Western blotting, and semi-quantitative RT-PCR showed that HIF-1 alpha, VEGF-A, and VEGFR2 (Flk-1) protein and mRNA expression levels were significantly higher (P < 0.05) in brain tumor tissues compared to normal brain. Gelatin zymography and RT-PCR demonstrated the upregulation of MMP-9 in tumor foci compared with brain control. Together these results suggest the critical role of hypoxia in driving tumor angiogenesis and invasion through upregulation of target genes important for these functions. Moreover this report validates our hypothesis that a novel noninvasive technology (SPAT) developed in our laboratory is suitable for detection of tumors, hypoxia, and angiogenesis.

"Comet tail sign": A pitfall of post-gadolinium magnetic resonance imaging findings for metastatic brain tumors.

PubMed

Mitsuya, Koichi; Nakasu, Yoko; Narita, Yoshitaka; Nakasu, Satoshi; Ohno, Makoto; Miyakita, Yasuji; Abe, Masato; Ito, Ichiro; Hayashi, Nakamasa; Endo, Masahiro

2016-05-01

A highly enhanced cap attached to the surface of metastatic tumors in the brain parenchyma is occasionally encountered on magnetic resonance (MR) images. This atypical enhanced cap tends to occur in severe peritumoral edema and may produce the characteristic bulge of a metastatic mass lesion termed the "comet tail sign" (CTS). The purpose of this study was to demonstrate the features of the CTS using MR imaging and pathological findings, and to clarify its clinical relevance. We selected 21 consecutive cases of newly diagnosed metastases from MR imaging studies that demonstrated the CTS; all had diffuse peritumoral edema. The MR T2-weighted images showed similarly homogenous and high intensity signals in both the tail and peritumoral edema. Fourteen of the 21 patients underwent surgical resection of their tumors, and 12 tails were separately removed for pathological examination, no tumor cells which revealed. We speculate that the CTS does not contain neoplastic tissues but is observed as a result of the leakage of contrast medium from the tumor body into the interstitial space of the white matter. Although CTS is a peculiar and uncommon enhancement pattern, it has clinical significance in determining the extent of the margin for invasive local treatments, such as surgical resection or stereotactic radiotherapy; this is particularly true in and near the eloquent areas.

Adult Brain Tumors and Pseudotumors: Interesting (Bizarre) Cases.

PubMed

Causil, Lazaro D; Ames, Romy; Puac, Paulo; Castillo, Mauricio

2016-11-01

Some brain tumors results are interesting due to their rarity at presentation and overwhelming imaging characteristics, posing a diagnostic challenge in the eyes of any experienced neuroradiologist. This article focuses on the most important features regarding epidemiology, location, clinical presentation, histopathology, and imaging findings of cases considered "bizarre." A review of the most recent literature dealing with these unusual tumors and pseudotumors is presented, highlighting key points related to the diagnosis, treatments, outcomes, and differential diagnosis. Copyright © 2016 Elsevier Inc. All rights reserved.

Glial brain tumor detection by using symmetry analysis

NASA Astrophysics Data System (ADS)

Pedoia, Valentina; Binaghi, Elisabetta; Balbi, Sergio; De Benedictis, Alessandro; Monti, Emanuele; Minotto, Renzo

2012-02-01

In this work a fully automatic algorithm to detect brain tumors by using symmetry analysis is proposed. In recent years a great effort of the research in field of medical imaging was focused on brain tumors segmentation. The quantitative analysis of MRI brain tumor allows to obtain useful key indicators of disease progression. The complex problem of segmenting tumor in MRI can be successfully addressed by considering modular and multi-step approaches mimicking the human visual inspection process. The tumor detection is often an essential preliminary phase to solvethe segmentation problem successfully. In visual analysis of the MRI, the first step of the experts cognitive process, is the detection of an anomaly respect the normal tissue, whatever its nature. An healthy brain has a strong sagittal symmetry, that is weakened by the presence of tumor. The comparison between the healthy and ill hemisphere, considering that tumors are generally not symmetrically placed in both hemispheres, was used to detect the anomaly. A clustering method based on energy minimization through Graph-Cut is applied on the volume computed as a difference between the left hemisphere and the right hemisphere mirrored across the symmetry plane. Differential analysis involves the loss the knowledge of the tumor side. Through an histogram analysis the ill hemisphere is recognized. Many experiments are performed to assess the performance of the detection strategy on MRI volumes in presence of tumors varied in terms of shapes positions and intensity levels. The experiments showed good results also in complex situations.

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

2017-12-01

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

NASA Astrophysics Data System (ADS)

Gaffney, Kevin P.; Aghaei, Faranak; Battiste, James; Zheng, Bin

2017-03-01

Detection of residual brain tumor is important to evaluate efficacy of brain cancer surgery, determine optimal strategy of further radiation therapy if needed, and assess ultimate prognosis of the patients. Brain MR is a commonly used imaging modality for this task. In order to distinguish between residual tumor and surgery induced scar tissues, two sets of MRI scans are conducted pre- and post-gadolinium contrast injection. The residual tumors are only enhanced in the post-contrast injection images. However, subjective reading and quantifying this type of brain MR images faces difficulty in detecting real residual tumor regions and measuring total volume of the residual tumor. In order to help solve this clinical difficulty, we developed and tested a new interactive computer-aided detection scheme, which consists of three consecutive image processing steps namely, 1) segmentation of the intracranial region, 2) image registration and subtraction, 3) tumor segmentation and refinement. The scheme also includes a specially designed and implemented graphical user interface (GUI) platform. When using this scheme, two sets of pre- and post-contrast injection images are first automatically processed to detect and quantify residual tumor volume. Then, a user can visually examine segmentation results and conveniently guide the scheme to correct any detection or segmentation errors if needed. The scheme has been repeatedly tested using five cases. Due to the observed high performance and robustness of the testing results, the scheme is currently ready for conducting clinical studies and helping clinicians investigate the association between this quantitative image marker and outcome of patients.

Diagnostic value of the fast-FLAIR sequence in MR imaging of intracranial tumors.

PubMed

Husstedt, H W; Sickert, M; Köstler, H; Haubitz, B; Becker, H

2000-01-01

The aim of this study was to quantify imaging characteristics of fast fluid-attenuated inversion recovery (FLAIR) sequence in brain tumors compared with T1-postcontrast- and T2-sequences. Fast-FLAIR-, T2 fast spin echo (FSE)-, and T1 SE postcontrast images of 74 patients with intracranial neoplasms were analyzed. Four neuroradiologists rated signal intensity and inhomogeneity of the tumor, rendering of cystic parts, demarcation of the tumor vs brain, of the tumor vs edema and of brain vs edema, as well as the presence of motion and of other artifacts. Data analysis was performed for histologically proven astrocytomas, glioblastomas, and meningiomas, for tumors with poor contrast enhancement, and for all patients pooled. Only for tumors with poor contrast enhancement (n = 12) did fast FLAIR provide additional information about the lesion. In these cases, signal intensity, demarcation of the tumor vs brain, and differentiation of the tumor vs edema were best using fast FLAIR. In all cases, rendering of the tumor's inner structure was poor. For all other tumor types, fast FLAIR did not give clinically relevant information, the only exception being a better demarcation of the edema from brain tissue. Artifacts rarely interfered with evaluation of fast-FLAIR images. Thus, fast FLAIR cannot replace T2-weighted series. It provides additional information only in tumors with poor contrast enhancement. It is helpful for defining the exact extent of the edema of any tumor but gives little information about their inner structure.

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

NASA Astrophysics Data System (ADS)

Alizadeh Savareh, Behrouz; Emami, Hassan; Hajiabadi, Mohamadreza; Ghafoori, Mahyar; Majid Azimi, Seyed

2018-03-01

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

Histogram analysis of ADC in brain tumor patients

NASA Astrophysics Data System (ADS)

Banerjee, Debrup; Wang, Jihong; Li, Jiang

2011-03-01

At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, post-contrast T1) were registered to the corresponding DTI scan at patient's first visit. Conventionally, hyper-intensity regions on T1-post contrast images are believed to represent the core tumor region while regions highlighted by FLAIR may overestimate tumor size. Thus we annotated tumor regions on the T1-post contrast scans and ADC intensity values for pixels were extracted inside tumor regions as defined on T1-post scans. We fit a mixture Gaussian (MG) model for the extracted pixels using the Expectation-Maximization (EM) algorithm, which produced a set of parameters (mean, various and mixture coefficients) for the MG model. This procedure was performed for each visits resulting in a series of GM parameters. We studied the parameters fitted for ADC and see if they can be used as indicators for tumor progression. Additionally, we studied the ADC characteristics in the peri-tumoral region as identified by hyper-intensity on FLAIR scans. The results show that ADC histogram analysis of the tumor region supports the two compartment model that suggests the low ADC value subregion corresponding to densely packed cancer cell while the higher ADC value region corresponding to a mixture of viable and necrotic cells with superimposed edema. Careful studies of the composition and relative volume of the two compartments in tumor

Children's Brain Tumor Foundation

MedlinePlus

... 2 Family Donate Volunteer Justin's Hope Fund Children’s Brain Tumor Foundation, A non-profit organization, was founded ... and the long term outlook for children with brain and spinal cord tumors through research, support, education, ...

Dual-Targeting Lactoferrin-Conjugated Polymerized Magnetic Polydiacetylene-Assembled Nanocarriers with Self-Responsive Fluorescence/Magnetic Resonance Imaging for In Vivo Brain Tumor Therapy.

PubMed

Fang, Jen-Hung; Chiu, Tsung-Lang; Huang, Wei-Chen; Lai, Yen-Ho; Hu, Shang-Hsiu; Chen, You-Yin; Chen, San-Yuan

2016-03-01

Maintaining a high concentration of therapeutic agents in the brain is difficult due to the restrictions of the blood-brain barrier (BBB) and rapid removal from blood circulation. To enable controlled drug release and enhance the blood-brain barrier (BBB)-crossing efficiency for brain tumor therapy, a new dual-targeting magnetic polydiacetylene nanocarriers (PDNCs) delivery system modified with lactoferrin (Lf) is developed. The PDNCs are synthesized using the ultraviolet (UV) cross-linkable 10,12-pentacosadiynoic acid (PCDA) monomers through spontaneous assembling onto the surface of superparamagnetic iron oxide (SPIO) nanoparticles to form micelles-polymerized structures. The results demonstrate that PDNCs will reduce the drug leakage and further control the drug release, and display self-responsive fluorescence upon intracellular uptake for cell trafficking and imaging-guided tumor treatment. The magnetic Lf-modified PDNCs with magnetic resonance imaging (MRI) and dual-targeting ability can enhance the transportation of the PDNCs across the BBB for tracking and targeting gliomas. An enhanced therapeutic efficiency can be obtained using Lf-Cur (Curcumin)-PDNCs by improving the retention time of the encapsulated Cur and producing fourfold higher Cur amounts in the brain compared to free Cur. Animal studies also confirm that Lf targeting and controlled release act synergistically to significantly suppress tumors in orthotopic brain-bearing rats. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors.

PubMed

Bergamino, M; Bonzano, L; Levrero, F; Mancardi, G L; Roccatagliata, L

2014-09-01

In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated. Copyright © 2014 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Automated tumor volumetry using computer-aided image segmentation.

PubMed

Gaonkar, Bilwaj; Macyszyn, Luke; Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A; Ali, Zarina S; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M; Davatzikos, Christos

2015-05-01

Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0-5 rating scale where 5 indicated perfect segmentation. The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Automated Tumor Volumetry Using Computer-Aided Image Segmentation

PubMed Central

Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

2015-01-01

Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

Awake Craniotomy for Tumor Resection: Further Optimizing Therapy of Brain Tumors.

PubMed

Mehdorn, H Maximilian; Schwartz, Felix; Becker, Juliane

2017-01-01

In recent years more and more data have emerged linking the most radical resection to prolonged survival in patients harboring brain tumors. Since total tumor resection could increase postoperative morbidity, many methods have been suggested to reduce the risk of postoperative neurological deficits: awake craniotomy with the possibility of continuous patient-surgeon communication is one of the possibilities of finding out how radical a tumor resection can possibly be without causing permanent harm to the patient.In 1994 we started to perform awake craniotomy for glioma resection. In 2005 the use of intraoperative high-field magnetic resonance imaging (MRI) was included in the standard tumor therapy protocol. Here we review our experience in performing awake surgery for gliomas, gained in 219 patients.Patient selection by the operating surgeon and a neuropsychologist is of primary importance: the patient should feel as if they are part of the surgical team fighting against the tumor. The patient will undergo extensive neuropsychological testing, functional MRI, and fiber tractography in order to define the relationship between the tumor and the functionally relevant brain areas. Attention needs to be given at which particular time during surgery the intraoperative MRI is performed. Results from part of our series (without and with ioMRI scan) are presented.

American Brain Tumor Association

MedlinePlus

... Brain Tumor Association Names Leslie M. Stokes Interim Chief Executive Officer and Begins Search for Permanent CEO September 7, ... American Brain Tumor Association Names Kelly Sitkin as Chief Advancement Officer Read More ABTA Live ABTA Facebook Follow @theabta ...

Non-invasive intraoperative optical coherence tomography of the resection cavity during surgery of intrinsic brain tumors

NASA Astrophysics Data System (ADS)

Giese, A.; Böhringer, H. J.; Leppert, J.; Kantelhardt, S. R.; Lankenau, E.; Koch, P.; Birngruber, R.; Hüttmann, G.

2006-02-01

Optical coherence tomography (OCT) is a non-invasive imaging technique with a micrometer resolution. It allows non-contact / non-invasive analysis of central nervous system tissues with a penetration depth of 1-3,5 mm reaching a spatial resolution of approximately 4-15 μm. We have adapted spectral-domain OCT (SD-OCT) and time-domain OCT (TD-OCT) for intraoperative detection of residual tumor during brain tumor surgery. Human brain tumor tissue and areas of the resection cavity were analyzed during the resection of gliomas using this new technology. The site of analysis was registered using a neuronavigation system and biopsies were taken and submitted to routine histology. We have used post image acquisition processing to compensate for movements of the brain and to realign A-scan images for calculation of a light attenuation factor. OCT imaging of normal cortex and white matter showed a typical light attenuation profile. Tumor tissue depending on the cellularity of the specimen showed a loss of the normal light attenuation profile resulting in altered light attenuation coefficients compared to normal brain. Based on this parameter and the microstructure of the tumor tissue, which was entirely absent in normal tissue, OCT analysis allowed the discrimination of normal brain tissue, invaded brain, solid tumor tissue, and necrosis. Following macroscopically complete resections OCT analysis of the resection cavity displayed the typical microstructure and light attenuation profile of tumor tissue in some specimens, which in routine histology contained microscopic residual tumor tissue. We have demonstrated that this technology may be applied to the intraoperative detection of residual tumor during resection of human gliomas.

Find a Brain Tumor Center

MedlinePlus

... Ways to Give Charitable Shopping Close Find a Brain Tumor Center Below is a listing of brain ... center is in your insurance plan’s covered network Brain Tumor Treatment Centers: Filter: Mayo Clinic Arizona Mayo ...

Determination of intra-axial brain tumors cellularity through the analysis of T2 Relaxation time of brain tumors before surgery using MATLAB software.

PubMed

Abdolmohammadi, Jamil; Shafiee, Mohsen; Faeghi, Fariborz; Arefan, Douman; Zali, Alireza; Motiei-Langroudi, Rouzbeh; Farshidfar, Zahra; Nazarlou, Ali Kiani; Tavakkoli, Ali; Yarham, Mohammad

2016-08-01

Timely diagnosis of brain tumors could considerably affect the process of patient treatment. To do so, para-clinical methods, particularly MRI, cannot be ignored. MRI has so far answered significant questions regarding tumor characteristics, as well as helping neurosurgeons. In order to detect the tumor cellularity, neuro-surgeons currently have to sample specimens by biopsy and then send them to the pathology unit. The aim of this study is to determine the tumor cellularity in the brain. In this cross-sectional study, 32 patients (18 males and 14 females from 18-77 y/o) were admitted to the neurosurgery department of Shohada-E Tajrish Hospital in Tehran, Iran from April 2012 to February 2014. In addition to routine pulse sequences, T2W Multi echo pulse sequences were taken and the images were analyzed using the MATLAB software to determine the brain tumor cellularity, compared with the biopsy. These findings illustrate the need for more T2 relaxation time decreases, the higher classes of tumors will stand out in the designed table. In this study, the results show T2 relaxation time with a 85% diagnostic weight, compared with the biopsy, to determine the brain tumor cellularity (p<0.05). Our results indicate that the T2 relaxation time feature is the best method to distinguish and present the degree of intra-axial brain tumors cellularity (85% accuracy compared to biopsy). The use of more data is recommended in order to increase the percent accuracy of this techniques.

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.

Ex vivo micro-CT imaging of murine brain models using non-ionic iodinated contrast

NASA Astrophysics Data System (ADS)

Salas Bautista, N.; Martínez-Dávalos, A.; Rodríguez-Villafuerte, M.; Murrieta-Rodríguez, T.; Manjarrez-Marmolejo, J.; Franco-Pérez, J.; Calvillo-Velasco, M. E.

2014-11-01

Preclinical investigation of brain tumors is frequently carried out by means of intracranial implantation of brain tumor xenografts or allografts, with subsequent analysis of tumor growth using conventional histopathology. However, very little has been reported on the use contrast-enhanced techniques in micro-CT imaging for the study of malignant brain tumors in small animal models. The aim of this study has been to test a protocol for ex vivo imaging of murine brain models of glioblastoma multiforme (GBM) after treatment with non-ionic iodinated solution, using an in-house developed laboratory micro-CT. We have found that the best compromise between acquisition time and image quality is obtained using a 50 kVp, 0.5 mAs, 1° angular step on a 360 degree orbit acquisition protocol, with 70 μm reconstructed voxel size using the Feldkamp algorithm. With this parameters up to 4 murine brains can be scanned in tandem in less than 15 minutes. Image segmentation and analysis of three sample brains allowed identifying tumor volumes as small as 0.4 mm3.

Simultaneous 11C-Methionine Positron Emission Tomography/Magnetic Resonance Imaging of Suspected Primary Brain Tumors

PubMed Central

Deuschl, Cornelius; Goericke, Sophia; Grueneisen, Johannes; Sawicki, Lino Morris; Goebel, Juliane; El Hindy, Nicolai; Wrede, Karsten; Binse, Ina; Poeppel, Thorsten; Quick, Harald; Forsting, Michael; Hense, Joerg; Umutlu, Lale; Schlamann, Marc

2016-01-01

Introduction The objective of this study was to assess the diagnostic value of integrated 11C- methionine PET/MRI for suspected primary brain tumors, in comparison to MRI alone. Material and Methods Forty-eight consecutive patients with suspected primary brain tumor were prospectively enrolled for an integrated 11C-methionine PET/MRI. Two neuro-radiologists separately evaluated the MRI alone and the integrated PET/MRI data sets regarding most likely diagnosis and diagnostic confidence on a 5-point scale. Reference standard was histopathology or follow-up imaging. Results Fifty-one suspicious lesions were detected: 16 high-grade glioma and 25 low-grade glioma. Ten non-malignant cerebral lesions were described by the reference standard. MRI alone and integrated PET/MRI each correctly classified 42 of the 51 lesions (82.4%) as neoplastic lesions (WHO grade II, III and IV) or non-malignant lesions (infectious and neoplastic lesions). Diagnostic confidence for all lesions, low-grade astrocytoma and high-grade astrocytoma (3.7 vs. 4.2, 3,1 vs. 3.8, 4.0 vs. 4,7) were significantly (p < 0.05) better with integrated PET/MRI than in MRI alone. Conclusions The present study demonstrates the high potential of integrated 11C-methionine-PET/MRI for the assessment of suspected primary brain tumors. Although integrated methionine PET/MRI does not lead to an improvement of correct diagnoses, diagnostic confidence is significantly improved. PMID:27907162

Imaging the delivery of brain-penetrating PLGA nanoparticles in the brain using magnetic resonance.

PubMed

Strohbehn, Garth; Coman, Daniel; Han, Liang; Ragheb, Ragy R T; Fahmy, Tarek M; Huttner, Anita J; Hyder, Fahmeed; Piepmeier, Joseph M; Saltzman, W Mark; Zhou, Jiangbing

2015-02-01

Current therapy for glioblastoma multiforme (GBM) is largely ineffective, with nearly universal tumor recurrence. The failure of current therapy is primarily due to the lack of approaches for the efficient delivery of therapeutics to diffuse tumors in the brain. In our prior study, we developed brain-penetrating nanoparticles that are capable of penetrating brain tissue and distribute over clinically relevant volumes when administered via convection-enhanced delivery (CED). We demonstrated that these particles are capable of efficient delivery of chemotherapeutics to diffuse tumors in the brain, indicating that they may serve as a groundbreaking approach for the treatment of GBM. In the original study, nanoparticles in the brain were imaged using positron emission tomography (PET). However, clinical translation of this delivery platform can be enabled by engineering a non-invasive detection modality using magnetic resonance imaging (MRI). For this purpose, we developed chemistry to incorporate superparamagnetic iron oxide (SPIO) into the brain-penetrating nanoparticles. We demonstrated that SPIO-loaded nanoparticles, which retain the same morphology as nanoparticles without SPIO, have an excellent transverse (T(2)) relaxivity. After CED, the distribution of nanoparticles in the brain (i.e., in the vicinity of injection site) can be detected using MRI and the long-lasting signal attenuation of SPIO-loaded brain-penetrating nanoparticles lasted over a one-month timecourse. Development of these nanoparticles is significant as, in future clinical applications, co-administration of SPIO-loaded nanoparticles will allow for intraoperative monitoring of particle distribution in the brain to ensure drug-loaded nanoparticles reach tumors as well as for monitoring the therapeutic benefit with time and to evaluate tumor relapse patterns.

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

PubMed

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

2017-01-01

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

Dynamic glucose enhanced (DGE) MRI for combined imaging of blood-brain barrier break down and increased blood volume in brain cancer.

PubMed

Xu, Xiang; Chan, Kannie W Y; Knutsson, Linda; Artemov, Dmitri; Xu, Jiadi; Liu, Guanshu; Kato, Yoshinori; Lal, Bachchu; Laterra, John; McMahon, Michael T; van Zijl, Peter C M

2015-12-01

Recently, natural d-glucose was suggested as a potential biodegradable contrast agent. The feasibility of using d-glucose for dynamic perfusion imaging was explored to detect malignant brain tumors based on blood brain barrier breakdown. Mice were inoculated orthotopically with human U87-EGFRvIII glioma cells. Time-resolved glucose signal changes were detected using chemical exchange saturation transfer (glucoCEST) MRI. Dynamic glucose enhanced (DGE) MRI was used to measure tissue response to an intravenous bolus of d-glucose. DGE images of mouse brains bearing human glioma showed two times higher and persistent changes in tumor compared with contralateral brain. Area-under-curve (AUC) analysis of DGE delineated blood vessels and tumor and had contrast comparable to the AUC determined using dynamic contrast enhanced (DCE) MRI with GdDTPA, both showing a significantly higher AUC in tumor than in brain (P < 0.005). Both CEST and relaxation effects contribute to the signal change. DGE MRI is a feasible technique for studying brain tumor enhancement reflecting differences in tumor blood volume and permeability with respect to normal brain. We expect DGE will provide a low-risk and less expensive alternative to DCE MRI for imaging cancer in vulnerable populations, such as children and patients with renal impairment. © 2015 Wiley Periodicals, Inc.

Dynamic Glucose Enhanced (DGE) MRI for Combined Imaging of Blood Brain Barrier Break Down and Increased Blood Volume in Brain Cancer

PubMed Central

Xu, Xiang; Chan, Kannie WY; Knutsson, Linda; Artemov, Dmitri; Xu, Jiadi; Liu, Guanshu; Kato, Yoshinori; Lal, Bachchu; Laterra, John; McMahon, Michael T.; van Zijl, Peter C.M.

2015-01-01

Purpose Recently, natural d-glucose was suggested as a potential biodegradable contrast agent. The feasibility of using d-glucose for dynamic perfusion imaging was explored to detect malignant brain tumors based on blood brain barrier breakdown. Methods Mice were inoculated orthotopically with human U87-EGFRvIII glioma cells. Time-resolved glucose signal changes were detected using chemical exchange saturation transfer (glucoCEST) MRI. Dynamic glucose enhanced (DGE) MRI was used to measure tissue response to an intravenous bolus of d-glucose. Results DGE images of mouse brains bearing human glioma showed two times higher and persistent changes in tumor compared to contralateral brain. Area-under-curve (AUC) analysis of DGE delineated blood vessels and tumor and had contrast comparable to the AUC determined using dynamic contrast enhanced (DCE) MRI with GdDTPA, both showing a significantly higher AUC in tumor than in brain (p<0.005). Both CEST and relaxation effects contribute to the signal change. Conclusion DGE MRI is a feasible technique for studying brain tumor enhancement reflecting differences in tumor blood volume and permeability with respect to normal brain. We expect DGE will provide a low-risk and less expensive alternative to DCE MRI for imaging cancer in vulnerable populations, such as children and patients with renal impairment. PMID:26404120

Spectral domain optical coherence tomography for ex vivo brain tumor analysis

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Jaedicke, Volker; Stroop, Ralf; Schmieder, Kirsten; Hofmann, Martin R.

2015-07-01

Non-contact imaging methods to distinguish between healthy tissue and brain tumor tissue during surgery would be highly desirable but are not yet available. Optical Coherence Tomography (OCT) is a non-invasive imaging technology with a resolution around 1-15 μm and a penetration depth of 1-2 mm that may satisfy the demands. To analyze its potential, we measured ex vivo human brain tumor tissue samples from 10 patients with a Spectral Domain OCT system (Thorlabs Callisto: center wavelength of 930 nm) and compared the results with standard histology. In detail, three different measurements were made for each sample. First the sample was measured directly after surgery. Then it was embedded in paraffin (also H and E staining) and examined for the second time. At last, the slices of each paraffin block cut by the pathology were measured. Each time a B-scan was created and for a better comparison with the histology a 3D image was generated, in order to get the corresponding en face images. In both, histopathological diagnosis and the analysis of the OCT images, different types of brain tumor showed difference in structure. This has been affirmed by two blinded investigators. Nevertheless the difference between two images of samples taken directly after surgery is less distinct. To enhance the contrast in the images further, we employ Spectroscopic OCT and pattern recognition algorithms and compare these results to the histopathological standard.

Trans sodium crocetinate: functional neuroimaging studies in a hypoxic brain tumor.

PubMed

Sheehan, Jason P; Popp, Britney; Monteith, Stephen; Toulmin, Sushila; Tomlinson, Jennifer; Martin, Jessica; Cifarelli, Christopher P; Lee, Dae-Hee; Park, Deric M

2011-10-01

Intratumoral hypoxia is believed to be exhibited in high-grade gliomas. Trans sodium crocetinate (TSC) has been shown to increase oxygen diffusion to hypoxic tissues. In this research, the authors use oxygen-sensitive PET studies to evaluate the extent of hypoxia in vivo in a glioblastoma model and the effect of TSC on the baseline oxygenation of the tumor. The C6 glioma cells were stereotactically implanted in the right frontal region of rat brains. Formation of intracranial tumors was confirmed on MR imaging. Animals were injected with Copper(II) diacetyl-di(N4-methylthiosemicarbazone) (Cu-ATSM) and then either TSC or saline (6 rats each). Positron emission tomography imaging was performed, and relative uptake values were computed to determine oxygenation within the tumor and normal brain parenchyma. Additionally, TSC or saline was infused into the animals, and carbonic anhydrase 9 (CA9) and hypoxia-inducing factor-1α (HIF-1α) protein expression were measured 1 day afterward. On PET imaging, all glioblastoma tumors demonstrated a statistically significant decrease in uptake of Cu-ATSM compared with the contralateral cerebral hemisphere (p = 0.000002). The mean relative uptake value of the tumor was 3900 (range 2203-6836), and that of the contralateral brain tissue was 1017 (range 488-2304). The mean relative hypoxic tumor volume for the saline group and TSC group (6 rats each) was 1.01 ± 0.063 and 0.69 ± 0.062, respectively (mean ± SEM, p = 0.002). Infusion of TSC resulted in a 31% decrease in hypoxic volume. Immunoblot analysis revealed expression of HIF-1α and CA9 in all tumor specimens. Some glioblastomas exhibit hypoxia that is demonstrable on oxygen-specific PET imaging. It appears that TSC lessens intratumoral hypoxia on functional imaging. Further studies should explore relative hypoxia in glioblastoma and the potential therapeutic gains that can be achieved by lessening hypoxia during delivery of adjuvant treatment.

Development of stereotactic mass spectrometry for brain tumor surgery.

PubMed

Agar, Nathalie Y R; Golby, Alexandra J; Ligon, Keith L; Norton, Isaiah; Mohan, Vandana; Wiseman, Justin M; Tannenbaum, Allen; Jolesz, Ferenc A

2011-02-01

Surgery remains the first and most important treatment modality for the majority of solid tumors. Across a range of brain tumor types and grades, postoperative residual tumor has a great impact on prognosis. The principal challenge and objective of neurosurgical intervention is therefore to maximize tumor resection while minimizing the potential for neurological deficit by preserving critical tissue. To introduce the integration of desorption electrospray ionization mass spectrometry into surgery for in vivo molecular tissue characterization and intraoperative definition of tumor boundaries without systemic injection of contrast agents. Using a frameless stereotactic sampling approach and by integrating a 3-dimensional navigation system with an ultrasonic surgical probe, we obtained image-registered surgical specimens. The samples were analyzed with ambient desorption/ionization mass spectrometry and validated against standard histopathology. This new approach will enable neurosurgeons to detect tumor infiltration of the normal brain intraoperatively with mass spectrometry and to obtain spatially resolved molecular tissue characterization without any exogenous agent and with high sensitivity and specificity. Proof of concept is presented in using mass spectrometry intraoperatively for real-time measurement of molecular structure and using that tissue characterization method to detect tumor boundaries. Multiple sampling sites within the tumor mass were defined for a patient with a recurrent left frontal oligodendroglioma, World Health Organization grade II with chromosome 1p/19q codeletion, and mass spectrometry data indicated a correlation between lipid constitution and tumor cell prevalence. The mass spectrometry measurements reflect a complex molecular structure and are integrated with frameless stereotaxy and imaging, providing 3-dimensional molecular imaging without systemic injection of any agents, which can be implemented for surgical margins delineation of

Intra-operative visualization of brain tumors with 5-aminolevulinic acid-induced fluorescence.

PubMed

Widhalm, Georg

2014-01-01

Precise histopathological diagnosis of brain tumors is essential for the correct patient management. Furthermore, complete resection of brain tumors is associated with an improved patient prognosis. However, histopathological undergrading and incomplete tumor removal are not uncommon, especially due to insufficient intra-operative visualization of brain tumor tissue. The fluorescent dye 5-aminolevulinic acid (5-ALA) is currently applied for fluorescence-guided resections of high-grade gliomas. The value of 5-ALA-induced protoporphyrin (PpIX) fluorescence for intra-operative visualization of other tumors than high-grade gliomas remains unclear. Within the frame of this thesis, we found a significantly higher rate of complete resections of our high-grade gliomas as compared to control cases by using the newly established 5-ALA fluorescence technology at our department. Additionally, we showed that MRI spectroscopy-based chemical shift imaging (CSI) is capable to identify intratumoral high-grade glioma areas (= anaplastic foci) during navigation guided resections to avoid histopathological undergrading. However, the accuracy of navigation systems with integrated pre-operative imaging data such as CSI declines during resections due to intra-operative brainshift. In two further studies, we found that 5-ALA induced PpIX fluorescence is capable as a novel intra-operative marker to detect anaplastic foci within initially suspected low-grade gliomas independent of brainshift. Finally, we showed that the application of 5-ALA is also of relevance in needle biopsies for intra-operative identification of representative brain tumor tissue. These data indicate that 5-ALA is not only of major importance for resection of high-grade gliomas, but also for intra-operative visualization of anaplastic foci as well as representative brain tumor tissue in needle biopsies unaffected by brainshift. Consequently, this new technique might become a novel standard in brain tumor surgery that

Clinical presentation, diagnosis, and pharmacotherapy of patients with primary brain tumors.

PubMed

Newton, H B; Turowski, R C; Stroup, T J; McCoy, L K

1999-01-01

To briefly review the clinical presentation and diagnosis of patients with primary brain tumors, followed by an in-depth survey of the pertinent pharmacotherapy. A detailed search of the neurologic, neurosurgical, and oncologic literature for basic science research, clinical studies, and review articles related to chemotherapy and pharmacotherapy of primary brain tumors. Relevant studies on tissue culture systems, animals, and humans examining the mechanisms of action, pharmacokinetics, clinical pharmacology, and treatment results of chemotherapeutic agents for primary brain tumors. In addition, studies of pharmacologic agents administered for supportive care and symptom control are reviewed. Primary brain tumors derive from cells within the intracranial cavity and generally present with headache, seizure activity, cognitive changes, and weakness. They are diagnosed most efficiently with magnetic resonance imaging. After diagnosis, the most common supportive medications include corticosteroids, gastric acid inhibitors, and anticonvulsants. Chemotherapy is adjunctive treatment for patients with malignant tumors and selected recurrent or progressive benign neoplasms. In general, the most effective chemotherapeutic drugs are alkylating agents such as the nitrosoureas, procarbazine, cisplatin, and carboplatin. Other agents used include cyclophosphamide, methotrexate, vincristine, and etoposide. Angiogenesis inhibitors and gene therapy comprise some of the novel therapeutic strategies under investigation. The efficacy of chemotherapy for primary brain tumors remains modest. Novel agents must be discovered that are more specific and attack tumor cells at the molecular level of tumorigenesis. Furthermore, strategies must be developed to counteract the pervasive problem of brain tumor chemoresistance.

Toward a preoperative planning tool for brain tumor resection therapies.

PubMed

Coffey, Aaron M; Miga, Michael I; Chen, Ishita; Thompson, Reid C

2013-01-01

Neurosurgical procedures involving tumor resection require surgical planning such that the surgical path to the tumor is determined to minimize the impact on healthy tissue and brain function. This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding an optimal model-selected patient orientation that minimizes lateral brain shift in the field of view. Such orientations may facilitate tumor access and removal, possibly reduce the need for retraction, and could minimize the impact of brain shift on image-guided procedures. In this study, preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy and cortical surface to produce patient-specific finite element models of intraoperative shift for 6 cases. These cases were used to calibrate a model (i.e., provide general rules for the application of patient positioning parameters) as well as determine the current model-based framework predictive capabilities. Finally, an objective function is proposed that minimizes shift subject to patient position parameters. Patient positioning parameters were then optimized and compared to our neurosurgeon as a preliminary study. The proposed model-driven brain shift minimization objective function suggests an overall reduction of brain shift by 23 % over experiential methods. This work recasts surgical simulation from a trial-and-error process to one where options are presented to the surgeon arising from an optimization of surgical goals. To our knowledge, this is the first realization of an evaluative tool for surgical planning that attempts to optimize surgical approach by means of shift minimization in this manner.

Brain Tumor Statistics

MedlinePlus

... Scientific Advisory Council & Reviewers The International Low Grade Glioma Registry Get Involved Advocacy Breakthrough for Brain Tumors ... an estimated 29,320 new cases in 2018. Gliomas , a broad term which includes all tumors arising ...

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.

A correlative optical microscopy and scanning electron microscopy approach to locating nanoparticles in brain tumors.

PubMed

Kempen, Paul J; Kircher, Moritz F; de la Zerda, Adam; Zavaleta, Cristina L; Jokerst, Jesse V; Mellinghoff, Ingo K; Gambhir, Sanjiv S; Sinclair, Robert

2015-01-01

The growing use of nanoparticles in biomedical applications, including cancer diagnosis and treatment, demands the capability to exactly locate them within complex biological systems. In this work a correlative optical and scanning electron microscopy technique was developed to locate and observe multi-modal gold core nanoparticle accumulation in brain tumor models. Entire brain sections from mice containing orthotopic brain tumors injected intravenously with nanoparticles were imaged using both optical microscopy to identify the brain tumor, and scanning electron microscopy to identify the individual nanoparticles. Gold-based nanoparticles were readily identified in the scanning electron microscope using backscattered electron imaging as bright spots against a darker background. This information was then correlated to determine the exact location of the nanoparticles within the brain tissue. The nanoparticles were located only in areas that contained tumor cells, and not in the surrounding healthy brain tissue. This correlative technique provides a powerful method to relate the macro- and micro-scale features visible in light microscopy with the nanoscale features resolvable in scanning electron microscopy. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards hyperpolarized 13C-succinate imaging of brain cancer

PubMed Central

Bhattacharya, Pratip; Chekmenev, Eduard Y.; Perman, William H.; Harris, Kent C.; Lin, Alexander P.; Norton, Valerie A.; Tan, Chou T.; Ross, Brian D.; Weitekamp, Daniel P.

2009-01-01

We describe a novel 13C enriched precursor molecule, sodium 1-13C acetylenedicarboxylate, which after hydrogenation by PASADE-NA (Parahydrogen and Synthesis Allows Dramatically Enhanced Nuclear Alignment) under controlled experimental conditions, becomes hyperpolarized 13C sodium succinate. Fast in vivo 3D FIESTA MR imaging demonstrated that, following carotid arterial injection, the hyperpolarized 13C-succinate appeared in the head and cerebral circulation of normal and tumor-bearing rats. At this time, no in vivo hyperpolarized signal has been localized to normal brain or brain tumor. On the other hand, ex vivo samples of brain harvested from rats bearing a 9L brain tumor, 1 h or more following in vivo carotid injection of hyperpolarized 13C sodium succinate, contained significant concentrations of the injected substrate, 13C sodium succinate, together with 13C maleate and succinate metabolites 1-13C-glutamate, 5-13C-glutamate, 1-13C-glutamine and 5-13C-glutamine. The 13C substrates and products were below the limits of NMR detection in ex vivo samples of normal brain consistent with an intact blood–brain barrier. These ex vivo results indicate that hyperpolarized 13C sodium succinate may become a useful tool for rapid in vivo identification of brain tumors, providing novel biomarkers in 13C MR spectral-spatial images. PMID:17303454

Towards hyperpolarized 13C-succinate imaging of brain cancer

NASA Astrophysics Data System (ADS)

Bhattacharya, Pratip; Chekmenev, Eduard Y.; Perman, William H.; Harris, Kent C.; Lin, Alexander P.; Norton, Valerie A.; Tan, Chou T.; Ross, Brian D.; Weitekamp, Daniel P.

2007-05-01

We describe a novel 13C enriched precursor molecule, sodium 1- 13C acetylenedicarboxylate, which after hydrogenation by PASADENA (Parahydrogen and Synthesis Allows Dramatically Enhanced Nuclear Alignment) under controlled experimental conditions, becomes hyperpolarized 13C sodium succinate. Fast in vivo 3D FIESTA MR imaging demonstrated that, following carotid arterial injection, the hyperpolarized 13C-succinate appeared in the head and cerebral circulation of normal and tumor-bearing rats. At this time, no in vivo hyperpolarized signal has been localized to normal brain or brain tumor. On the other hand, ex vivo samples of brain harvested from rats bearing a 9L brain tumor, 1 h or more following in vivo carotid injection of hyperpolarized 13C sodium succinate, contained significant concentrations of the injected substrate, 13C sodium succinate, together with 13C maleate and succinate metabolites 1- 13C-glutamate, 5- 13C-glutamate, 1- 13C-glutamine and 5- 13C-glutamine. The 13C substrates and products were below the limits of NMR detection in ex vivo samples of normal brain consistent with an intact blood-brain barrier. These ex vivo results indicate that hyperpolarized 13C sodium succinate may become a useful tool for rapid in vivo identification of brain tumors, providing novel biomarkers in 13C MR spectral-spatial images.

Proton magnetic resonance spectroscopy imaging in the study of human brain cancer.

PubMed

Martínez-Bisbal, M C; Celda, B

2009-12-01

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive imaging technique that provides metabolic information on brain tumor. This biochemical information can be processed and presented as density maps of several metabolites, among them N-acetylaspartate (marker of neuronal viability), choline (marker of membrane turnover), creatine (related to the energy state of the cells), myo-Inositol (exclusively found in astrocytes), lipids and lactate (observed in necrosis and other pathological processes) which mean relevant information in the context of brain tumors. Thus, this technique is a multiparametrical molecular imaging method that can complete the magnetic resonance imaging (MRI) study enabling the detection of biochemical patterns of different features and aspects of brain tumors. In this article, the role of MRSI as a molecular imaging technique to provide biochemical information on human brain tumors is reviewed. The most frequent questions and situations in the study of human brain tumors in clinical settings will be considered, as well as the distinction of neoplastic lesions from non neoplastic, the tumor type identification, the study of heterogeneity and infiltration of normal appearing white matter and the therapy following with detection of side effects. The great amount of data in MRSI acquisition compared to the single voxel techniques requires the use of automated methods of quantification, but the possibility to obtain self-reference in the non-affected areas allows different strategies for data handling and interpretation, as presented in the literature. The combination of MRSI with other physiological MRI techniques and positron emission tomography is also included in this review.

Epilepsy and brain tumors

PubMed Central

ENGLOT, DARIO J.; CHANG, EDWARD F.; VECHT, CHARLES J.

2016-01-01

Seizures are common in patients with brain tumors, and epilepsy can significantly impact patient quality of life. Therefore, a thorough understanding of rates and predictors of seizures, and the likelihood of seizure freedom after resection, is critical in the treatment of brain tumors. Among all tumor types, seizures are most common with glioneuronal tumors (70–80%), particularly in patients with frontotemporal or insular lesions. Seizures are also common in individuals with glioma, with the highest rates of epilepsy (60–75%) observed in patients with low-grade gliomas located in superficial cortical or insular regions. Approximately 20–50% of patients with meningioma and 20–35% of those with brain metastases also suffer from seizures. After tumor resection, approximately 60–90% are rendered seizure-free, with most favorable seizure outcomes seen in individuals with glioneuronal tumors. Gross total resection, earlier surgical therapy, and a lack of generalized seizures are common predictors of a favorable seizure outcome. With regard to anticonvulsant medication selection, evidence-based guidelines for the treatment of focal epilepsy should be followed, and individual patient factors should also be considered, including patient age, sex, organ dysfunction, comorbidity, or cotherapy. As concomitant chemotherapy commonly forms an essential part of glioma treatment, enzyme-inducing anticonvulsants should be avoided when possible. Seizure freedom is the ultimate goal in the treatment of brain tumor patients with epilepsy, given the adverse effects of seizures on quality of life. PMID:26948360

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

PubMed

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Physiological Imaging-Defined, Response-Driven Subvolumes of a Tumor

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

Farjam, Reza; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Tsien, Christina I.

2013-04-01

Purpose: To develop an image analysis framework to delineate the physiological imaging-defined subvolumes of a tumor in relating to treatment response and outcome. Methods and Materials: Our proposed approach delineates the subvolumes of a tumor based on its heterogeneous distributions of physiological imaging parameters. The method assigns each voxel a probabilistic membership function belonging to the physiological parameter classes defined in a sample of tumors, and then calculates the related subvolumes in each tumor. We applied our approach to regional cerebral blood volume (rCBV) and Gd-DTPA transfer constant (K{sup trans}) images of patients who had brain metastases and were treatedmore » by whole-brain radiation therapy (WBRT). A total of 45 lesions were included in the analysis. Changes in the rCBV (or K{sup trans})–defined subvolumes of the tumors from pre-RT to 2 weeks after the start of WBRT (2W) were evaluated for differentiation of responsive, stable, and progressive tumors using the Mann-Whitney U test. Performance of the newly developed metrics for predicting tumor response to WBRT was evaluated by receiver operating characteristic (ROC) curve analysis. Results: The percentage decrease in the high-CBV-defined subvolumes of the tumors from pre-RT to 2W was significantly greater in the group of responsive tumors than in the group of stable and progressive tumors (P<.007). The change in the high-CBV-defined subvolumes of the tumors from pre-RT to 2W was a predictor for post-RT response significantly better than change in the gross tumor volume observed during the same time interval (P=.012), suggesting that the physiological change occurs before the volumetric change. Also, K{sup trans} did not add significant discriminatory information for assessing response with respect to rCBV. Conclusion: The physiological imaging-defined subvolumes of the tumors delineated by our method could be candidates for boost target, for which further development and

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Drugs Approved for Brain Tumors

MedlinePlus

... Ask about Your Treatment Research Drugs Approved for Brain Tumors This page lists cancer drugs approved by ... that are not listed here. Drugs Approved for Brain Tumors Afinitor (Everolimus) Afinitor Disperz (Everolimus) Avastin (Bevacizumab) ...

Robust and brilliant Raman tags based on core-satellite assemblies for brain tumor cell imaging

NASA Astrophysics Data System (ADS)

Chang, Yung-Ching; Huang, Li-Ching; Sun, Wei-Lun; Chuang, Shih Yi; Lin, Tien-Hsin; Wu, Yi-Syuan; Sze, Chun-I.; Chen, Shiuan-Yeh

2018-02-01

GBM (Glioblastoma Multiforme), a fatal brain tumor, is highly infiltrative and difficult to be completely removed by the surgery. In this work, the Raman tags based on the plasmonic core-satellite assemblies with 1 nm internal gap accompanied by extremely high gap field have been fabricated and applied to GBM cell labeling. The brightness of the Raman tags is comparable to the fluorophores. The GBM cells with overexpression of EGFR are labeled with these Raman tags and can be distinguished from the normal cells through Raman imaging.

An automatic method of brain tumor segmentation from MRI volume based on the symmetry of brain and level set method

NASA Astrophysics Data System (ADS)

Li, Xiaobing; Qiu, Tianshuang; Lebonvallet, Stephane; Ruan, Su

2010-02-01

This paper presents a brain tumor segmentation method which automatically segments tumors from human brain MRI image volume. The presented model is based on the symmetry of human brain and level set method. Firstly, the midsagittal plane of an MRI volume is searched, the slices with potential tumor of the volume are checked out according to their symmetries, and an initial boundary of the tumor in the slice, in which the tumor is in the largest size, is determined meanwhile by watershed and morphological algorithms; Secondly, the level set method is applied to the initial boundary to drive the curve evolving and stopping to the appropriate tumor boundary; Lastly, the tumor boundary is projected one by one to its adjacent slices as initial boundaries through the volume for the whole tumor. The experiment results are compared with hand tracking of the expert and show relatively good accordance between both.

Deregulated proliferation and differentiation in brain tumors

PubMed Central

Swartling, Fredrik J; Čančer, Matko; Frantz, Aaron; Weishaupt, Holger; Persson, Anders I

2014-01-01

Neurogenesis, the generation of new neurons, is deregulated in neural stem cell (NSC)- and progenitor-derived murine models of malignant medulloblastoma and glioma, the most common brain tumors of children and adults, respectively. Molecular characterization of human malignant brain tumors, and in particular brain tumor stem cells (BTSCs), has identified neurodevelopmental transcription factors, microRNAs, and epigenetic factors known to inhibit neuronal and glial differentiation. We are starting to understand how these factors are regulated by the major oncogenic drivers in malignant brain tumors. In this review, we will focus on the molecular switches that block normal neuronal differentiation and induce brain tumor formation. Genetic or pharmacological manipulation of these switches in BTSCs has been shown to restore the ability of tumor cells to differentiate. We will discuss potential brain tumor therapies that will promote differentiation in order to reduce treatment-resistance, suppress tumor growth, and prevent recurrence in patients. PMID:25416506

Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

PubMed

Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

2014-01-01

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior

PubMed Central

Lu, Yisu; Jiang, Jun; Chen, Wufan

2014-01-01

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064

Association of functional magnetic resonance imaging indices with postoperative language outcomes in patients with primary brain tumors.

PubMed

Kundu, Bornali; Penwarden, Amy; Wood, Joel M; Gallagher, Thomas A; Andreoli, Matthew J; Voss, Jed; Meier, Timothy; Nair, Veena A; Kuo, John S; Field, Aaron S; Moritz, Chad; Meyerand, M Elizabeth; Prabhakaran, Vivek

2013-04-01

Functional MRI (fMRI) has the potential to be a useful presurgical planning tool to treat patients with primary brain tumor. In this study the authors retrospectively explored relationships between language-related postoperative outcomes in such patients and multiple factors, including measures estimated from task fMRI maps (proximity of lesion to functional activation area, or lesion-to-activation distance [LAD], and activation-based language lateralization, or lateralization index [LI]) used in the clinical setting for presurgical planning, as well as other factors such as patient age, patient sex, tumor grade, and tumor volume. Patient information was drawn from a database of patients with brain tumors who had undergone preoperative fMRI-based language mapping of the Broca and Wernicke areas. Patients had performed a battery of tasks, including word-generation tasks and a text-versus-symbols reading task, as part of a clinical fMRI protocol. Individually thresholded task fMRI activation maps had been provided for use in the clinical setting. These clinical imaging maps were used to retrospectively estimate LAD and LI for the Broca and Wernicke areas. There was a relationship between postoperative language deficits and the proximity between tumor and Broca area activation (the LAD estimate), where shorter LADs were related to the presence of postoperative aphasia. Stratification by tumor location further showed that for posterior tumors within the temporal and parietal lobes, more bilaterally oriented Broca area activation (LI estimate close to 0) and a shorter Wernicke area LAD were associated with increased postoperative aphasia. Furthermore, decreasing LAD was related to decreasing LI for both Broca and Wernicke areas. Preoperative deficits were related to increasing patient age and a shorter Wernicke area LAD. Overall, LAD and LI, as determined using fMRI in the context of these paradigms, may be useful indicators of postsurgical outcomes. Whereas tumor

In vivo Magnetic Resonance Imaging of Tumor Protease Activity

PubMed Central

Haris, Mohammad; Singh, Anup; Mohammed, Imran; Ittyerah, Ranjit; Nath, Kavindra; Nanga, Ravi Prakash Reddy; Debrosse, Catherine; Kogan, Feliks; Cai, Kejia; Poptani, Harish; Reddy, Damodar; Hariharan, Hari; Reddy, Ravinder

2014-01-01

Increased expression of cathepsins has diagnostic as well as prognostic value in several types of cancer. Here, we demonstrate a novel magnetic resonance imaging (MRI) method, which uses poly-L-glutamate (PLG) as an MRI probe to map cathepsin expression in vivo, in a rat brain tumor model. This noninvasive, high-resolution and non-radioactive method exploits the differences in the CEST signals of PLG in the native form and cathepsin mediated cleaved form. The method was validated in phantoms with known physiological concentrations, in tumor cells and in an animal model of brain tumor along with immunohistochemical analysis. Potential applications in tumor diagnosis and evaluation of therapeutic response are outlined. PMID:25124082

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

PubMed

Han, Xu; Kwitt, Roland; Aylward, Stephen; Bakas, Spyridon; Menze, Bjoern; Asturias, Alexander; Vespa, Paul; Van Horn, John; Niethammer, Marc

2018-08-01

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and

Brain Tumor Epidemiology Consortium (BTEC)

Cancer.gov

The Brain Tumor Epidemiology Consortium is an open scientific forum organized to foster the development of multi-center, international and inter-disciplinary collaborations that will lead to a better understanding of the etiology, outcomes, and prevention of brain tumors.

Molecular Imaging of Tumors Using a Quantitative T1 Mapping Technique via Magnetic Resonance Imaging

PubMed Central

Herrmann, Kelsey; Johansen, Mette L.; Craig, Sonya E.; Vincent, Jason; Howell, Michael; Gao, Ying; Lu, Lan; Erokwu, Bernadette; Agnes, Richard S.; Lu, Zheng-Rong; Pokorski, Jonathan K.; Basilion, James; Gulani, Vikas; Griswold, Mark; Flask, Chris; Brady-Kalnay, Susann M.

2015-01-01

Magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM) with molecular imaging agents would allow for the specific localization of brain tumors. Prior studies using T1-weighted MR imaging demonstrated that the SBK2-Tris-(Gd-DOTA)3 molecular imaging agent labeled heterotopic xenograft models of brain tumors more intensely than non-specific contrast agents using conventional T1-weighted imaging techniques. In this study, we used a dynamic quantitative T1 mapping strategy to more objectively compare intra-tumoral retention of the SBK2-Tris-(Gd-DOTA)3 agent over time in comparison to non-targeted control agents. Our results demonstrate that the targeted SBK2-Tris-(Gd-DOTA)3 agent, a scrambled-Tris-(Gd-DOTA)3 control agent, and the non-specific clinical contrast agent Optimark™ all enhanced flank tumors of human glioma cells with similar maximal changes on T1 mapping. However, the retention of the agents differs. The non-specific agents show significant recovery within 20 min by an increase in T1 while the specific agent SBK2-Tris-(Gd-DOTA)3 is retained in the tumors and shows little recovery over 60 min. The retention effect is demonstrated by percent change in T1 values and slope calculations as well as by calculations of gadolinium concentration in tumor compared to muscle. Quantitative T1 mapping demonstrates the superior binding and retention in tumors of the SBK2-Tris-(Gd-DOTA)3 agent over time compared to the non-specific contrast agent currently in clinical use. PMID:26435847

What underlies the diversity of brain tumors?

PubMed Central

Swartling, Fredrik J.; Hede, Sanna-Maria; Weiss, William A.

2012-01-01

Glioma and medulloblastoma represent the most commonly occurring malignant brain tumors in adults and in children respectively. Recent genomic and transcriptional approaches present a complex group of diseases, and delineate a number of molecular subgroups within tumors that share a common histopathology. Differences in cells of origin, regional niches, developmental timing and genetic events all contribute to this heterogeneity. In an attempt to recapitulate the diversity of brain tumors, an increasing array of genetically engineered mouse models (GEMMs) has been developed. These models often utilize promoters and genetic drivers from normal brain development, and can provide insight into specific cells from which these tumors originate. GEMMs show promise in both developmental biology and developmental therapeutics. This review describes numerous murine brain tumor models in the context of normal brain development, and the potential for these animals to impact brain tumor research. PMID:23085857

SU-G-IeP1-07: Inaccuracy of Lesion Blood Flow Quantification Related to the Proton Density Reference Image in Arterial Spin Labeling MRI of Brain Tumors

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

Jen, M; Johnson, J; Hou, P

Purpose: Cerebral blood flow quantification in arterial spin labeling (ASL) MRI requires an estimate of the equilibrium magnetization of blood, which is often obtained by a set of proton density (PD) reference image. Normally, a constant blood-brain partition coefficient is assumed across the brain. However, this assumption may not be valid for brain lesions. This study aimed to evaluate the impact of lesion-related PD variations on ASL quantification in patients with brain tumors. Methods: MR images for posttreatment evaluation of 42 patients with brain tumors were retrospectively analyzed. These images were acquired on a 3T MRI scanner, including T2-weighted FLAIR,more » 3D pseudo-continuous ASL and post-contrast T1-weighted images. Anatomical images were coregistered with ASL images using the SPM software. Regions of interest (ROIs) of the enhancing and FLAIR lesions were manually drawn on the coregistered images. ROIs of the contralateral normal appearing tissues were also determined, with the consideration of approximating coil sensitivity patterns in lesion ROIs. Relative lesion blood flow (lesion/contralateral tissue) was calculated from both the CBF map (dependent on the PD) and the ΔM map for comparison. Results: The signal intensities in both enhancing and FLAIR lesions were significantly different than contralateral tissues on the PD reference image (p<0.001). The percent signal difference ranged from −15.9 to 19.2%, with a mean of 5.4% for the enhancing lesion, and from −2.8 to 22.9% with a mean of 10.1% for the FLAIR lesion. The high/low lesion-related PD signal resulted in inversely proportional under-/over-estimation of blood flow in both enhancing and FLAIR lesions. Conclusion: Significant signal differences were found between lesions and contralateral tissues in the PD reference image, which introduced errors in blood flow quantification in ASL. The error can be up to 20% in individual patients with an average of 5- 10% for the group of

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.

Detection of human brain tumor infiltration with multimodal multiscale optical analysis

NASA Astrophysics Data System (ADS)

Poulon, Fanny; Metais, Camille; Jamme, Frederic; Zanello, Marc; Varlet, Pascale; Devaux, Bertrand; Refregiers, Matthieu; Abi Haidar, Darine

2017-02-01

Brain tumor surgeries are facing major challenges to improve patients' quality of life. The extent of resection while preserving surrounding eloquent brain areas is necessary to equilibrate the onco-functional. A tool able to increase the accuracy of tissue analysis and to deliver an immediate diagnostic on tumor, could drastically improve actual surgeries and patient survival rates. To achieve such performances a complete optical study, ranging from ultraviolet to infrared, of biopsies has been started by our group. Four different contrasts were used: 1) spectral analysis covering the DUV to IR range, 2) two photon fluorescence lifetime imaging and one photon time domain measurement, 3) second harmonic generation imaging and 4) fluorescence imaging using DUV to IR, one and two photon excitation. All these measurements were done on the endogenous fluorescence of tissues to avoid any bias and further clinical complication due to the introduction of external markers. The different modalities are then crossed to build a matrix of criteria to discriminate tumorous tissues. The results of multimodal optical analysis on human biopsies were compared to the gold standard histopathology.

Changes in Signal Intensity of the Dentate Nucleus and Globus Pallidus in Pediatric Patients: Impact of Brain Irradiation and Presence of Primary Brain Tumors Independent of Linear Gadolinium-based Contrast Agent Administration.

PubMed

Tamrazi, Benita; Nguyen, Binh; Liu, Chia-Shang J; Azen, Colleen G; Nelson, Mary B; Dhall, Girish; Nelson, Marvin D

2018-05-01

Purpose To determine whether whole-brain irradiation, chemotherapy, and primary brain pathologic conditions affect magnetic resonance (MR) imaging signal changes in pediatric patients independent of the administration of gadolinium-based contrast agents (GBCAs). Materials and Methods This institutional review board-approved, HIPAA-compliant study included 144 pediatric patients who underwent intravenous GBCA-enhanced MR imaging examinations (55 patients with primary brain tumors and whole-brain irradiation, 19 with primary brain tumors and chemotherapy only, 52 with primary brain tumors without any treatment, and 18 with neuroblastoma without brain metastatic disease). The signal intensities (SIs) in the globus pallidus (GP), thalamus (T), dentate nucleus (DN), and pons (P) were measured on unenhanced T1-weighted images. GP:T and DN:P SI ratios were compared between groups by using the analysis of variance and were analyzed relative to group, total cumulative number of doses of GBCA, age, and sex by using multivariable linear models. Results DN:P ratio for the radiation therapy group was greater than that for the other groups except for the group of brain tumors treated with chemotherapy (P < .05). The number of GBCA doses was correlated with the DN:P ratio for the nontreated brain tumor group (P < .0001). The radiation therapy-treated brain tumor group demonstrated higher DN:P ratios than the nontreated brain tumor group for number of doses less than or equal to 10 (P < .0001), whereas ratios in the nontreated brain tumor group were higher than those in the radiation therapy-treated brain tumor group for doses greater than 20 (P = .05). The GP:T ratios for the brain tumor groups were greater than that for the neuroblastoma group (P = .01). Conclusion Changes in SI of the DN and GP that are independent of the administration of GBCA occur in patients with brain tumors undergoing brain irradiation, as well as in patients with untreated primary brain tumors. © RSNA

Origins of Brain Tumor Macrophages.

PubMed

De Palma, Michele

2016-12-12

The ontogeny of brain-tumor-associated macrophages is poorly understood. New findings indicate that both resident microglia and blood-derived monocytes generate the pool of macrophages that infiltrate brain tumors of either primary or metastatic origin. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Time-resolved fluorescence spectroscopy of human brain tumors

NASA Astrophysics Data System (ADS)

Marcu, Laura; Thompson, Reid C.; Garde, Smita; Sedrak, Mark; Black, Keith L.; Yong, William H.

2002-05-01

Fluorescence spectroscopy of the endogenous emission of brain tumors has been researched as a potentially important method for the intraoperative localization of brain tumor margins. In this study, we investigate the use of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) for demarcation of primary brain tumors by studying the time-resolved spectra of gliomas of different histologic grades. Time-resolved fluorescence (3 ns, 337 nm excitation) from excised human brain tumor show differences between the time-resolved emission of malignant glioma and normal brain tissue (gray and white matter). Our findings suggest that brain tumors can be differentiated from normal brain tissue based upon unique time-resolved fluorescence signature.

Optimization of scan initiation timing after 11C-methionine administration for the diagnosis of suspected recurrent brain tumors.

PubMed

Nakajima, Reiko; Abe, Koichiro; Momose, Mitsuru; Fukushima, Kenji; Matsuo, Yuka; Kimura, Ken; Kondo, Chisato; Sakai, Shuji

2017-02-01

11 C-Methionine (MET) positron emission tomography (PET) imaging is a valuable technique for the evaluation of primary and recurrent brain tumors. Many studies have used MET-PET for data acquisition starting at 20 min after the tracer injection, while others have used scan initiation times at 5-15 min postinjection. No previous studies have identified the best acquisition timing during MET-PET imaging for suspected recurrent brain tumors. Here we sought to determine the optimal scan initiating timing after MET administration for the detection of recurrent brain tumors. Twenty-three consecutive patients with suspected recurrent brain tumors underwent MET-PET examinations. Brain PET images were reconstructed from the four serial data sets (10-15, 15-20, 20-25, and 25-30 min postinjection) that were obtained using the list-mode acquisition technique. We determined the maximal standardized uptake values (SUVmax) of the target lesions and the target-to-normal-tissue ratios (TNRs), calculated as the SUVmax to the SUVmean of a region of interest placed on the normal contralateral frontal cortex. Target lesions without significant MET uptake were excluded. Thirty-one lesions from 23 patients were enrolled. There were no significant differences in MET SUVmax or TNR values among the PET images that were reconstructed with the data extracted from the four phases postinjection. The MET uptake in the suspected recurrent brain tumors was comparable among all data extraction time phases from 10 to 30 min postinjection. The scan initiation time of MET-PET at 10 min after the injection is allowable for the detection of recurrent brain tumors. The registration identification number of the original study is 1002.

Chemo brain or tumor brain - that is the question: the presence of extracranial tumors profoundly affects molecular processes in the prefrontal cortex of TumorGraft mice

PubMed Central

Kovalchuk, Anna; Ilnytskyy, Yaroslav; Rodriguez-Juarez, Rocio; Shpyleva, Svitlana; Melnyk, Stepan; Pogribny, Igor; Katz, Amanda; Sidransky, David; Kovalchuk, Olga; Kolb, Bryan

2017-01-01

Cancer chemotherapy causes numerous persistent central nervous system complications. This condition is known as chemo brain. Cognitive impairments occur even before treatment, and hence are referred to as cancer associated cognitive changes, or tumor brain. There is much yet to be learned about the mechanisms of both chemo brain and tumor brain. The frequency and timing of chemo brain and tumor brain occurrence and persistence strongly suggest they may be epigenetic in nature and associated with altered gene expression. Here we used TumorGraftTM models wherein part of a patient's tumor is removed and grafted into immune-deficient mice and conducted global gene expression and DNA methylation analysis. We show that malignant non-central nervous system tumor growth causes profound molecular alterations in the brain. Mice harbouring triple negative or progesterone positive breast cancer TumorGrafts exhibited altered gene expression, decreased levels of DNA methylation, increased levels of DNA hydroxymethylation, and oxidative stress in the prefrontal cortex. Interestingly, chemotherapy did not have any additional synergistic effects on the analyzed processes. The molecular changes observed in this study are known signs of neurodegeneration and brain aging. This study provides an important roadmap for future large-scale analysis of the molecular and cellular mechanisms of tumor brain. PMID:28758896

Targeting Brain Tumors with Nanomedicines: Overcoming Challenges of Blood Brain Barrier.

PubMed

Ningaraj, Nagendra S; Reddy, Polluru L; Khaitan, Divya

2018-04-12

This review elucidates ongoing research, which show improved delivery of anticancer drugs alone and/ or enclosed in carriers collectively called nanomedicines to cross the Blood brain barrier (BBB) / blood-brain tumor barrier (BTB) to kill tumor cells and impact patient survival. We highlighted various advances in understanding the mechanism of BTB function that impact on anticancer therapeutics delivery. We discussed latest breakthroughs in developing pharmaceutical strategies, including nanomedicines and delivering them across BTB for brain tumor management and treatment. We highlight various studies on regulation of BTB permeability regulation with respect to nanotech-based nanomedicines for targeted treatment of brain tumors. We have reviewed latest literature on development of specialized molecules and nanospheres for carrying pay load of anticancer agents to brain tumor cells across the BBB/ BTB and avoid drug efflux systems. We discuss identification and development of distinctive BTB biomarkers for targeted anti-cancer drug delivery to brain tumors. In addition, we discussed nanomedicines and multimeric molecular therapeutics that were encapsulated in nanospheres for treatment and monitoring of brain tumors. In this context, we highlight our research on calcium-activated potassium channels (KCa) and ATP-sensitive potassium channels (KATP) as portals of enhanced antineoplastic drugs delivery. This review might interest both academic and drug company scientists involved in drug delivery to brain tumors. We further seek to present evidence that BTB modulators can be clinically developed as combination drug or/ and as stand-alone anticancer drugs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Correlation between language function and the left arcuate fasciculus detected by diffusion tensor imaging tractography after brain tumor surgery.

PubMed

Hayashi, Yutaka; Kinoshita, Masashi; Nakada, Mitsutoshi; Hamada, Jun-ichiro

2012-11-01

Disturbance of the arcuate fasciculus in the dominant hemisphere is thought to be associated with language-processing disorders, including conduction aphasia. Although the arcuate fasciculus can be visualized in vivo with diffusion tensor imaging (DTI) tractography, its involvement in functional processes associated with language has not been shown dynamically using DTI tractography. In the present study, to clarify the participation of the arcuate fasciculus in language functions, postoperative changes in the arcuate fasciculus detected by DTI tractography were evaluated chronologically in relation to postoperative changes in language function after brain tumor surgery. Preoperative and postoperative arcuate fasciculus area and language function were examined in 7 right-handed patients with a brain tumor in the left hemisphere located in proximity to part of the arcuate fasciculus. The arcuate fasciculus was depicted, and its area was calculated using DTI tractography. Language functions were measured using the Western Aphasia Battery (WAB). After tumor resection, visualization of the arcuate fasciculus was increased in 5 of the 7 patients, and the total WAB score improved in 6 of the 7 patients. The relative ratio of postoperative visualized area of the arcuate fasciculus to preoperative visualized area of the arcuate fasciculus was increased in association with an improvement in postoperative language function (p = 0.0039). The role of the left arcuate fasciculus in language functions can be evaluated chronologically in vivo by DTI tractography after brain tumor surgery. Because increased postoperative visualization of the fasciculus was significantly associated with postoperative improvement in language functions, the arcuate fasciculus may play an important role in language function, as previously thought. In addition, postoperative changes in the arcuate fasciculus detected by DTI tractography could represent a predicting factor for postoperative language

Microglia function in brain tumors.

PubMed

Watters, Jyoti J; Schartner, Jill M; Badie, Behnam

2005-08-01

Microglia play an important role in inflammatory diseases of the central nervous system (CNS). These cells have also been identified in brain neoplasms; however, as of yet their function largely remains unclear. More recent studies designed to characterize further tumor-associated microglia suggest that the immune effector function of these cells may be suppressed in CNS tumors. Furthermore, microglia and macrophages can secrete various cytokines and growth factors that may contribute to the successful immune evasion, growth, and invasion of brain neoplasms. A better understanding of microglia and macrophage function is essential for the development of immune-based treatment strategies against malignant brain tumors. (c) 2005 Wiley-Liss, Inc.

Nanobiotechnology-based delivery strategies: New frontiers in brain tumor targeted therapies.

PubMed

Mangraviti, Antonella; Gullotti, David; Tyler, Betty; Brem, Henry

2016-10-28

Despite recent technological advancements and promising preclinical experiments, brain tumor patients are still met with limited treatment options. Some of the barriers to clinical improvements include the systemic toxicity of cytotoxic compounds, the impedance of the blood brain barrier (BBB), and the lack of therapeutic agents that can selectively target the intracranial tumor environment. To overcome such barriers, a number of chemotherapeutic agents and nucleic acid-based therapies are rapidly being synthesized and tested as new brain tumor-targeted delivery strategies. Novel carriers include liposomal and polymeric nanoparticles, wafers, microchips, microparticle-based nanoplatforms and cells-based vectors. Strong preclinical results suggest that these nanotechnologies are set to transform the therapeutic paradigm for brain tumor treatment. In addition to new tumoricidal agents, parallel work is also being conducted on the BBB front. Preclinical testing of chemical and physical modulation strategies is yielding improved intracranial concentrations. New diagnostic and therapeutic imaging techniques, such as high-intensity focused ultrasound and MRI-guided focused ultrasound, are being used to modulate the BBB in a more precise and non-invasive manner. This review details some of the tremendous advances that are being explored in current brain tumor targeted therapies, including local implant development, nanobiotechnology-based delivery strategies, and techniques of BBB manipulation. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

Detection of brain tumor margins using optical coherence tomography

NASA Astrophysics Data System (ADS)

Juarez-Chambi, Ronald M.; Kut, Carmen; Rico-Jimenez, Jesus; Campos-Delgado, Daniel U.; Quinones-Hinojosa, Alfredo; Li, Xingde; Jo, Javier

2018-02-01

In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, noncancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancerinfiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End-Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.

Detection of brain tumor margins using optical coherence tomography

NASA Astrophysics Data System (ADS)

Juarez-Chambi, Ronald M.; Kut, Carmen; Rico-Jimenez, Jesus; Campos-Delgado, Daniel U.; Quinones-Hinojosa, Alfredo; Li, Xingde; Jo, Javier

2018-02-01

In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End- Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

2016-12-21

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

NASA Astrophysics Data System (ADS)

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

2016-12-01

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

Fractal analysis of the susceptibility weighted imaging patterns in malignant brain tumors during antiangiogenic treatment: technical report on four cases serially imaged by 7 T magnetic resonance during a period of four weeks.

PubMed

Di Ieva, Antonio; Matula, Christian; Grizzi, Fabio; Grabner, Günther; Trattnig, Siegfried; Tschabitscher, Manfred

2012-01-01

The need for new and objective indexes for the neuroradiologic follow-up of brain tumors and for monitoring the effects of antiangiogenic strategies in vivo led us to perform a technical study on four patients who received computerized analysis of tumor-associated vasculature with ultra-high-field (7 T) magnetic resonance imaging (MRI). The image analysis involved the application of susceptibility weighted imaging (SWI) to evaluate vascular structures. Four patients affected by recurrent malignant brain tumors were enrolled in the present study. After the first 7-T SWI MRI procedure, the patients underwent antiangiogenic treatment with bevacizumab. The imaging was repeated every 2 weeks for a period of 4 weeks. The SWI patterns visualized in the three MRI temporal sequences were analyzed by means of a computer-aided fractal-based method to objectively quantify their geometric complexity. In two clinically deteriorating patients we found an increase of the geometric complexity of the space-filling properties of the SWI patterns over time despite the antiangiogenic treatment. In one patient, who showed improvement with the therapy, the fractal dimension of the intratumoral structure decreased, whereas in the fourth patient, no differences were found. The qualitative changes of the intratumoral SWI patterns during a period of 4 weeks were quantified with the fractal dimension. Because SWI patterns are also related to the presence of vascular structures, the quantification of their space-filling properties with fractal dimension seemed to be a valid tool for the in vivo neuroradiologic follow-up of brain tumors. Copyright © 2012 Elsevier Inc. All rights reserved.

Multi-fractal texture features for brain tumor and edema segmentation

NASA Astrophysics Data System (ADS)

Reza, S.; Iftekharuddin, K. M.

2014-03-01

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

Identification of disappearing brain lesions with intraoperative magnetic resonance imaging prevents surgery.

PubMed

Sutherland, Christina S; Kelly, John Jp; Morrish, William; Sutherland, Garnette R

2010-10-01

Typically, neurosurgery is performed several weeks after diagnostic imaging. In the majority of cases, histopathology confirms the diagnosis of neoplasia. In a small number of cases, a different diagnosis is established or histopathology is nondiagnostic. The frequency with which these outcomes occur has not been established. To determine the frequency and outcome of disappearing brain lesions within a group of patients undergoing surgery for suspected brain tumor. Over the past decade, 982 patients were managed in the intraoperative magnetic resonance imaging unit at the University of Calgary, Calgary, Alberta, Canada. These patients have been prospectively evaluated. In 652 patients, a brain tumor was suspected. In 6 of the 652 patients, histopathology indicated a nontumor diagnosis. In 5 patients, intraoperative images, acquired after induction of anesthesia, showed complete or nearly complete resolution of the suspected tumor identified on diagnostic magnetic resonance imaging acquired 6 ± 4 (mean ± SD) weeks previously. Anesthesia was reversed, and the surgical procedure aborted. The lesions have not progressed with 6 ± 2 years of follow-up. Intraoperative magnetic resonance imaging prevented surgery on 5 patients with disappearing lesions.

Measurement of the perfusion fraction in brain tumors with intravoxel incoherent motion MR imaging: validation with histopathological vascular density in meningiomas.

PubMed

Togao, Osamu; Hiwatashi, Akio; Yamashita, Koji; Kikuchi, Kazufumi; Momosaka, Daichi; Yoshimoto, Koji; Kuga, Daisuke; Mizoguchi, Masahiro; Suzuki, Satoshi O; Iwaki, Toru; Van Cauteren, Marc; Iihara, Koji; Honda, Hiroshi

2018-05-01

To evaluate the quantification performance of the perfusion fraction (f) measured with intravoxel incoherent motion (IVIM) MR imaging in a comparison with the histological vascular density in meningiomas. 29 consecutive patients with meningioma (59.0 ± 16.8 years old, 8 males and 21 females) who underwent a subsequent surgical resection were examined with both IVIM imaging and a histopathological analysis. IVIM imaging was conducted using a single-shot SE-EPI sequence with 13 b-factors (0, 10, 20, 30, 50, 80, 100, 200, 300, 400, 600, 800, 1000 s mm - 2 ) at 3T. The perfusion fraction (f) was calculated by fitting the IVIM bi-exponential model. The 90-percentile f-value in the tumor region-of-interest (ROI) was defined as the maximum f-value (f-max). Histopathological vascular density (%Vessel) was measured on CD31-immunostainted histopathological specimens. The correlation and agreement between the f-values and %Vessel was assessed. The f-max (15.5 ± 5.5%) showed excellent agreement [intraclass correlation coefficient (ICC) = 0.754] and a significant correlation (r = 0.69, p < 0.0001) with the %Vessel (12.9 ± 9.4%) of the tumors. The Bland-Altman plot analysis showed excellent agreement between the f-max and %Vessel (bias, -2.6%; 95% limits of agreement, from -16.0 to 10.8%). The f-max was not significantly different among the histological subtypes of meningioma. An excellent agreement and a significant correlation were observed between the f-values and %Vessel. The f-value can be used as a noninvasive quantitative imaging measure to directly assess the vascular volume fraction in brain tumors. Advances in knowledge: The f-value measured by IVIM imaging showed a significant correlation and an excellent agreement with the histological vascular density in the meningiomas. The f-value can be used as a noninvasive and quantitative imaging measure to directly assess the volume fraction of capillaries in brain tumors.

The brain-penetrant clinical ATM inhibitor AZD1390 radiosensitizes and improves survival of preclinical brain tumor models

PubMed Central

Wang, Yingchun; Chen, Kan; Zhang, Lingli; Zhang, Tianwei; Yang, Zhenfan; Riches, Lucy; Trinidad, Antonio G.; Pike, Kurt G.; Wilson, Joanne; Smith, Aaron; Colclough, Nicola; Johnström, Peter; Varnäs, Katarina; Takano, Akihiro; Ling, Stephanie; Orme, Jonathan; Stott, Jonathan; Barrett, Ian; Jones, Gemma; Allen, Jasmine; Kahn, Jenna; Sule, Amrita; Cronin, Anna; Chapman, Melissa; Illingworth, Ruth; Pass, Martin

2018-01-01

Poor survival rates of patients with tumors arising from or disseminating into the brain are attributed to an inability to excise all tumor tissue (if operable), a lack of blood-brain barrier (BBB) penetration of chemotherapies/targeted agents, and an intrinsic tumor radio-/chemo-resistance. Ataxia-telangiectasia mutated (ATM) protein orchestrates the cellular DNA damage response (DDR) to cytotoxic DNA double-strand breaks induced by ionizing radiation (IR). ATM genetic ablation or pharmacological inhibition results in tumor cell hypersensitivity to IR. We report the primary pharmacology of the clinical-grade, exquisitely potent (cell IC50, 0.78 nM), highly selective [>10,000-fold over kinases within the same phosphatidylinositol 3-kinase–related kinase (PIKK) family], orally bioavailable ATM inhibitor AZD1390 specifically optimized for BBB penetration confirmed in cynomolgus monkey brain positron emission tomography (PET) imaging of microdosed 11C-labeled AZD1390 (Kp,uu, 0.33). AZD1390 blocks ATM-dependent DDR pathway activity and combines with radiation to induce G2 cell cycle phase accumulation, micronuclei, and apoptosis. AZD1390 radiosensitizes glioma and lung cancer cell lines, with p53 mutant glioma cells generally being more radiosensitized than wild type. In in vivo syngeneic and patient-derived glioma as well as orthotopic lung-brain metastatic models, AZD1390 dosed in combination with daily fractions of IR (whole-brain or stereotactic radiotherapy) significantly induced tumor regressions and increased animal survival compared to IR treatment alone. We established a pharmacokinetic-pharmacodynamic-efficacy relationship by correlating free brain concentrations, tumor phospho-ATM/phospho-Rad50 inhibition, apoptotic biomarker (cleaved caspase-3) induction, tumor regression, and survival. On the basis of the data presented here, AZD1390 is now in early clinical development for use as a radiosensitizer in central nervous system malignancies. PMID:29938225

The brain-penetrant clinical ATM inhibitor AZD1390 radiosensitizes and improves survival of preclinical brain tumor models.

PubMed

Durant, Stephen T; Zheng, Li; Wang, Yingchun; Chen, Kan; Zhang, Lingli; Zhang, Tianwei; Yang, Zhenfan; Riches, Lucy; Trinidad, Antonio G; Fok, Jacqueline H L; Hunt, Tom; Pike, Kurt G; Wilson, Joanne; Smith, Aaron; Colclough, Nicola; Reddy, Venkatesh Pilla; Sykes, Andrew; Janefeldt, Annika; Johnström, Peter; Varnäs, Katarina; Takano, Akihiro; Ling, Stephanie; Orme, Jonathan; Stott, Jonathan; Roberts, Caroline; Barrett, Ian; Jones, Gemma; Roudier, Martine; Pierce, Andrew; Allen, Jasmine; Kahn, Jenna; Sule, Amrita; Karlin, Jeremy; Cronin, Anna; Chapman, Melissa; Valerie, Kristoffer; Illingworth, Ruth; Pass, Martin

2018-06-01

Poor survival rates of patients with tumors arising from or disseminating into the brain are attributed to an inability to excise all tumor tissue (if operable), a lack of blood-brain barrier (BBB) penetration of chemotherapies/targeted agents, and an intrinsic tumor radio-/chemo-resistance. Ataxia-telangiectasia mutated (ATM) protein orchestrates the cellular DNA damage response (DDR) to cytotoxic DNA double-strand breaks induced by ionizing radiation (IR). ATM genetic ablation or pharmacological inhibition results in tumor cell hypersensitivity to IR. We report the primary pharmacology of the clinical-grade, exquisitely potent (cell IC 50 , 0.78 nM), highly selective [>10,000-fold over kinases within the same phosphatidylinositol 3-kinase-related kinase (PIKK) family], orally bioavailable ATM inhibitor AZD1390 specifically optimized for BBB penetration confirmed in cynomolgus monkey brain positron emission tomography (PET) imaging of microdosed 11 C-labeled AZD1390 ( K p,uu , 0.33). AZD1390 blocks ATM-dependent DDR pathway activity and combines with radiation to induce G 2 cell cycle phase accumulation, micronuclei, and apoptosis. AZD1390 radiosensitizes glioma and lung cancer cell lines, with p53 mutant glioma cells generally being more radiosensitized than wild type. In in vivo syngeneic and patient-derived glioma as well as orthotopic lung-brain metastatic models, AZD1390 dosed in combination with daily fractions of IR (whole-brain or stereotactic radiotherapy) significantly induced tumor regressions and increased animal survival compared to IR treatment alone. We established a pharmacokinetic-pharmacodynamic-efficacy relationship by correlating free brain concentrations, tumor phospho-ATM/phospho-Rad50 inhibition, apoptotic biomarker (cleaved caspase-3) induction, tumor regression, and survival. On the basis of the data presented here, AZD1390 is now in early clinical development for use as a radiosensitizer in central nervous system malignancies.

Cryo-image Analysis of Tumor Cell Migration, Invasion, and Dispersal in a Mouse Xenograft Model of Human Glioblastoma Multiforme

PubMed Central

Qutaish, Mohammed Q.; Sullivant, Kristin E.; Burden-Gulley, Susan M.; Lu, Hong; Roy, Debashish; Wang, Jing; Basilion, James P.; Brady-Kalnay, Susann M.; Wilson, David L.

2012-01-01

Purpose The goals of this study were to create cryo-imaging methods to quantify characteristics (size, dispersal, and blood vessel density) of mouse orthotopic models of glioblastoma multiforme (GBM) and to enable studies of tumor biology, targeted imaging agents, and theranostic nanoparticles. Procedures Green fluorescent protein-labeled, human glioma LN-229 cells were implanted into mouse brain. At 20–38 days, cryo-imaging gave whole brain, 4-GB, 3D microscopic images of bright field anatomy, including vasculature, and fluorescent tumor. Image analysis/visualization methods were developed. Results Vessel visualization and segmentation methods successfully enabled analyses. The main tumor mass volume, the number of dispersed clusters, the number of cells/cluster, and the percent dispersed volume all increase with age of the tumor. Histograms of dispersal distance give a mean and median of 63 and 56 μm, respectively, averaged over all brains. Dispersal distance tends to increase with age of the tumors. Dispersal tends to occur along blood vessels. Blood vessel density did not appear to increase in and around the tumor with this cell line. Conclusion Cryo-imaging and software allow, for the first time, 3D, whole brain, microscopic characterization of a tumor from a particular cell line. LN-229 exhibits considerable dispersal along blood vessels, a characteristic of human tumors that limits treatment success. PMID:22125093

MR Imaging Evaluation of Intracerebral Hemorrhages and T2 Hyperintense White Matter Lesions Appearing after Radiation Therapy in Adult Patients with Primary Brain Tumors.

PubMed

Yoo, Dong Hyun; Song, Sang Woo; Yun, Tae Jin; Kim, Tae Min; Lee, Se-Hoon; Kim, Ji-Hoon; Sohn, Chul-Ho; Park, Sung-Hye; Park, Chul-Kee; Kim, Il Han; Choi, Seung Hong

2015-01-01

The purpose of our study was to determine the frequency and severity of intracerebral hemorrhages and T2 hyperintense white matter lesions (WMLs) following radiation therapy for brain tumors in adult patients. Of 648 adult brain tumor patients who received radiation therapy at our institute, magnetic resonance (MR) image data consisting of a gradient echo (GRE) and FLAIR T2-weighted image were available three and five years after radiation therapy in 81 patients. Intracerebral hemorrhage was defined as a hypointense dot lesion appearing on GRE images after radiation therapy. The number and size of the lesions were evaluated. The T2 hyperintense WMLs observed on the FLAIR sequences were graded according to the extent of the lesion. Intracerebral hemorrhage was detected in 21 (25.9%) and 35 (43.2) patients in the three- and five-year follow-up images, respectively. The number of intracerebral hemorrhages per patient tended to increase as the follow-up period increased, whereas the size of the intracerebral hemorrhages exhibited little variation over the course of follow-up. T2 hyperintense WMLs were observed in 27 (33.3%) and 32 (39.5) patients in the three and five year follow-up images, respectively. The age at the time of radiation therapy was significantly higher (p < 0.001) in the patients with T2 hyperintense WMLs than in those without lesions. Intracerebral hemorrhages are not uncommon in adult brain tumor patients undergoing radiation therapy. The incidence and number of intracerebral hemorrhages increased over the course of follow-up. T2 hyperintense WMLs were observed in more than one-third of the study population.

Combining Cytotoxic and Immune-Mediated Gene Therapy to Treat Brain Tumors

PubMed Central

Curtin, James F.; King, Gwendalyn D.; Candolfi, Marianela; Greeno, Remy B.; Kroeger, Kurt M.; Lowenstein, Pedro R.; Castro, Maria G.

2006-01-01

Glioblastoma (GBM) is a type of intracranial brain tumor, for which there is no cure. In spite of advances in surgery, chemotherapy and radiotherapy, patients die within a year of diagnosis. Therefore, there is a critical need to develop novel therapeutic approaches for this disease. Gene therapy, which is the use of genes or other nucleic acids as drugs, is a powerful new treatment strategy which can be developed to treat GBM. Several treatment modalities are amenable for gene therapy implementation, e.g. conditional cytotoxic approaches, targeted delivery of toxins into the tumor mass, immune stimulatory strategies, and these will all be the focus of this review. Both conditional cytotoxicity and targeted toxin mediated tumor death, are aimed at eliminating an established tumor mass and preventing further growth. Tumors employ several defensive strategies that suppress and inhibit anti-tumor immune responses. A better understanding of the mechanisms involved in eliciting anti-tumor immune responses has identified promising targets for immunotherapy. Immunotherapy is designed to aid the immune system to recognize and destroy tumor cells in order to eliminate the tumor burden. Also, immune-therapeutic strategies have the added advantage that an activated immune system has the capability of recognizing tumor cells at distant sites from the primary tumor, therefore targeting metastasis distant from the primary tumor locale. Pre-clinical models and clinical trials have demonstrated that in spite of their location within the central nervous system (CNS), a tissue described as ‘immune privileged’, brain tumors can be effectively targeted by the activated immune system following various immunotherapeutic strategies. This review will highlight recent advances in brain tumor immunotherapy, with particular emphasis on advances made using gene therapy strategies, as well as reviewing other novel therapies that can be used in combination with immunotherapy. Another

Label-Free Delineation of Brain Tumors by Coherent Anti-Stokes Raman Scattering Microscopy in an Orthotopic Mouse Model and Human Glioblastoma

PubMed Central

Tamosaityte, Sandra; Leipnitz, Elke; Geiger, Kathrin D.; Schackert, Gabriele; Koch, Edmund; Steiner, Gerald; Kirsch, Matthias

2014-01-01

Background Coherent anti-Stokes Raman scattering (CARS) microscopy provides fine resolution imaging and displays morphochemical properties of unstained tissue. Here, we evaluated this technique to delineate and identify brain tumors. Methods Different human tumors (glioblastoma, brain metastases of melanoma and breast cancer) were induced in an orthotopic mouse model. Cryosections were investigated by CARS imaging tuned to probe C-H molecular vibrations, thereby addressing the lipid content of the sample. Raman microspectroscopy was used as reference. Histopathology provided information about the tumor's localization, cell proliferation and vascularization. Results The morphochemical contrast of CARS images enabled identifying brain tumors irrespective of the tumor type and properties: All tumors were characterized by a lower CARS signal intensity than the normal parenchyma. On this basis, tumor borders and infiltrations could be identified with cellular resolution. Quantitative analysis revealed that the tumor-related reduction of CARS signal intensity was more pronounced in glioblastoma than in metastases. Raman spectroscopy enabled relating the CARS intensity variation to the decline of total lipid content in the tumors. The analysis of the immunohistochemical stainings revealed no correlation between tumor-induced cytological changes and the extent of CARS signal intensity reductions. The results were confirmed on samples of human glioblastoma. Conclusions CARS imaging enables label-free, rapid and objective identification of primary and secondary brain tumors. Therefore, it is a potential tool for diagnostic neuropathology as well as for intraoperative tumor delineation. PMID:25198698

Deep learning for segmentation of brain tumors: can we train with images from different institutions?

NASA Astrophysics Data System (ADS)

Paredes, David; Saha, Ashirbani; Mazurowski, Maciej A.

2017-03-01

Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. However, in the environment which fosters data-sharing and collection of multi-institutional datasets, a question arises: does training with data from another institution with potentially different imaging equipment, contrast protocol, and patient population impact the segmentation performance of the CNN? Our study presents preliminary data towards answering this question. Specifically, we used MRI data of glioblastoma (GBM) patients for two institutions present in The Cancer Imaging Archive. We performed a process of training and testing CNN multiple times such that half of the time the CNN was tested on data from the same institution that was used for training and half of the time it was tested on another institution, keeping the training and testing set size constant. We observed a decrease in performance as measured by Dice coefficient when the CNN was trained with data from a different institution as compared to training with data from the same institution. The changes in performance for the entire tumor and for four different labels within the tumor were: 0.72 to 0.65 (p=0.06), 0.61 to 0.58 (p=0.49), 0.54 to 0.51 (p=0.82), 0.31 to 0.24 (p<0.03), and 0.43 to 0.31(p<0.003) respectively. In summary, we found that while data across institutions can be used for development of CNNs, this might be associated with a decrease in performance.

Development and characterization of non-resonant multiphoton photoacoustic spectroscopy (NMPPAS) for brain tumor margining

NASA Astrophysics Data System (ADS)

Dahal, Sudhir

During tumor removal surgery, due to the problems associated with obtaining high-resolution, real-time chemical images of where exactly the tumor ends and healthy tissue begins (tumor margining), it is often necessary to remove a much larger volume of tissue than the tumor itself. In the case of brain tumor surgery, however, it is extremely unsafe to remove excess tissue. Therefore, without an accurate image of the tumor margins, some of the tumor's finger-like projections are inevitably left behind in the surrounding parenchyma to grow again. For this reason, the development of techniques capable of providing high-resolution real-time images of tumor margins up to centimeters below the surface of a tissue is ideal for the diagnosis and treatment of tumors, as well as surgical guidance during brain tumor excision. A novel spectroscopic technique, non-resonant multiphoton photoacoustic spectroscopy (NMPPAS), is being developed with the capabilities of obtaining high-resolution subsurface chemical-based images of underlying tumors. This novel technique combines the strengths of multiphoton tissue spectroscopy and photoacoustic spectroscopy into a diagnostic methodology that will, ultimately, provide unparalleled chemical information and images to provide the state of sub-surface tissues. The NMPPAS technique employs near-infrared light (in the diagnostic window) to excite ultraviolet and/or visible light absorbing species deep below the tissue's surface. Once a multiphoton absorption event occurs, non-radiative relaxation processes generates a localized thermal expansion and subsequent acoustic wave that can be detected using a piezoelectric transducer. Since NMPPAS employs an acoustic detection modality, much deeper diagnoses can be performed than that is possible using current state of the art high-resolution chemical imaging techniques such as multiphoton fluorescence spectroscopy. NMPPAS was employed to differentiate between excised brain tumors (astrocytoma III

Monte Carlo simulation studies on scintillation detectors and image reconstruction of brain-phantom tumors in TOFPET

PubMed Central

Mondal, Nagendra Nath

2009-01-01

This study presents Monte Carlo Simulation (MCS) results of detection efficiencies, spatial resolutions and resolving powers of a time-of-flight (TOF) PET detector systems. Cerium activated Lutetium Oxyorthosilicate (Lu2SiO5: Ce in short LSO), Barium Fluoride (BaF2) and BriLanCe 380 (Cerium doped Lanthanum tri-Bromide, in short LaBr3) scintillation crystals are studied in view of their good time and energy resolutions and shorter decay times. The results of MCS based on GEANT show that spatial resolution, detection efficiency and resolving power of LSO are better than those of BaF2 and LaBr3, although it possesses inferior time and energy resolutions. Instead of the conventional position reconstruction method, newly established image reconstruction (talked about in the previous work) method is applied to produce high-tech images. Validation is a momentous step to ensure that this imaging method fulfills all purposes of motivation discussed by reconstructing images of two tumors in a brain phantom. PMID:20098551

A New Way to Treat Brain Tumors: Targeting Proteins Coded by Microcephaly Genes?: Brain tumors and microcephaly arise from opposing derangements regulating progenitor growth. Drivers of microcephaly could be attractive brain tumor targets.

PubMed

Lang, Patrick Y; Gershon, Timothy R

2018-05-01

New targets for brain tumor therapies may be identified by mutations that cause hereditary microcephaly. Brain growth depends on the repeated proliferation of stem and progenitor cells. Microcephaly syndromes result from mutations that specifically impair the ability of brain progenitor or stem cells to proliferate, by inducing either premature differentiation or apoptosis. Brain tumors that derive from brain progenitor or stem cells may share many of the specific requirements of their cells of origin. These tumors may therefore be susceptible to disruptions of the protein products of genes that are mutated in microcephaly. The potential for the products of microcephaly genes to be therapeutic targets in brain tumors are highlighted hereby reviewing research on EG5, KIF14, ASPM, CDK6, and ATR. Treatments that disrupt these proteins may open new avenues for brain tumor therapy that have increased efficacy and decreased toxicity. © 2018 WILEY Periodicals, Inc.

Android application for determining surgical variables in brain-tumor resection procedures.

PubMed

Vijayan, Rohan C; Thompson, Reid C; Chambless, Lola B; Morone, Peter J; He, Le; Clements, Logan W; Griesenauer, Rebekah H; Kang, Hakmook; Miga, Michael I

2017-01-01

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

Recent Advancement of the Molecular Diagnosis in Pediatric Brain Tumor.

PubMed

Bae, Jeong-Mo; Won, Jae-Kyung; Park, Sung-Hye

2018-05-01

Recent discoveries of brain tumor-related genes and fast advances in genomic testing technologies have led to the era of molecular diagnosis of brain tumor. Molecular profiling of brain tumor became the significant step in the diagnosis, the prediction of prognosis and the treatment of brain tumor. Because traditional molecular testing methods have limitations in time and cost for multiple gene tests, next-generation sequencing technologies are rapidly introduced into clinical practice. Targeted sequencing panels using these technologies have been developed for brain tumors. In this article, focused on pediatric brain tumor, key discoveries of brain tumor-related genes are reviewed and cancer panels used in the molecular profiling of brain tumor are discussed.

Recent Advancement of the Molecular Diagnosis in Pediatric Brain Tumor

PubMed Central

Bae, Jeong-Mo; Won, Jae-Kyung; Park, Sung-Hye

2018-01-01

Recent discoveries of brain tumor-related genes and fast advances in genomic testing technologies have led to the era of molecular diagnosis of brain tumor. Molecular profiling of brain tumor became the significant step in the diagnosis, the prediction of prognosis and the treatment of brain tumor. Because traditional molecular testing methods have limitations in time and cost for multiple gene tests, next-generation sequencing technologies are rapidly introduced into clinical practice. Targeted sequencing panels using these technologies have been developed for brain tumors. In this article, focused on pediatric brain tumor, key discoveries of brain tumor-related genes are reviewed and cancer panels used in the molecular profiling of brain tumor are discussed. PMID:29742887

Half brain irradiation in a murine model of breast cancer brain metastasis: magnetic resonance imaging and histological assessments of dose-response.

PubMed

Zarghami, Niloufar; Murrell, Donna H; Jensen, Michael D; Dick, Frederick A; Chambers, Ann F; Foster, Paula J; Wong, Eugene

2018-06-01

Brain metastasis is becoming increasingly prevalent in breast cancer due to improved extra-cranial disease control. With emerging availability of modern image-guided radiation platforms, mouse models of brain metastases and small animal magnetic resonance imaging (MRI), we examined brain metastases' responses from radiotherapy in the pre-clinical setting. In this study, we employed half brain irradiation to reduce inter-subject variability in metastases dose-response evaluations. Half brain irradiation was performed on a micro-CT/RT system in a human breast cancer (MDA-MB-231-BR) brain metastasis mouse model. Radiation induced DNA double stranded breaks in tumors and normal mouse brain tissue were quantified using γ-H2AX immunohistochemistry at 30 min (acute) and 11 days (longitudinal) after half-brain treatment for doses of 8, 16 and 24 Gy. In addition, tumor responses were assessed volumetrically with in-vivo longitudinal MRI and histologically for tumor cell density and nuclear size. In the acute setting, γ-H2AX staining in tumors saturated at higher doses while normal mouse brain tissue continued to increase linearly in the phosphorylation of H2AX. While γ-H2AX fluorescence intensities returned to the background level in the brain 11 days after treatment, the residual γ-H2AX phosphorylation in the radiated tumors remained elevated compared to un-irradiated contralateral tumors. With radiation, MRI-derived relative tumor growth was significantly reduced compared to the un-irradiated side. While there was no difference in MRI tumor volume growth between 16 and 24 Gy, there was a significant reduction in tumor cell density from histology with increasing dose. In the longitudinal study, nuclear size in the residual tumor cells increased significantly as the radiation dose was increased. Radiation damages to the DNAs in the normal brain parenchyma are resolved over time, but remain unrepaired in the treated tumors. Furthermore, there is a radiation dose

Brain tumor segmentation with Deep Neural Networks.

PubMed

Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

2017-01-01

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

Primary brain tumors, neural stem cell, and brain tumor cancer cells: where is the link?

PubMed Central

Germano, Isabelle; Swiss, Victoria; Casaccia, Patrizia

2010-01-01

The discovery of brain tumor-derived cells (BTSC) with the properties of stem cells has led to the formulation of the hypothesis that neural stem cells could be the cell of origin of primary brain tumors (PBT). In this review we present the most common molecular changes in PBT, define the criteria of identification of BTSC and discuss the similarities between the characteristics of these cells and those of the endogenous population of neural stem cells (NPCs) residing in germinal areas of the adult brain. Finally, we propose possible mechanisms of cancer initiation and progression and suggest a model of tumor initiation that includes intrinsic changes of resident NSC and potential changes in the microenvironment defining the niche where the NSC reside. PMID:20045420

Brain Tumor Surgery

MedlinePlus

... Proton Therapy Alternative & Integrative Medicine Clinical Trials GBM AGILE TTFields – Optune™ Brain Tumor Treatment Locations Treatment Side Effects & their Management Support and Resources Caregiver Resource Center Pediatric Caregiver ...

Presurgical evaluation of language using functional magnetic resonance imaging in brain tumor patients with previous surgery.

PubMed

Peck, Kyung K; Bradbury, Michelle; Petrovich, Nicole; Hou, Bob L; Ishill, Nicole; Brennan, Cameron; Tabar, Viviane; Holodny, Andrei I

2009-04-01

Functional magnetic resonance imaging (fMRI) is used to assess language laterality in preoperative brain tumor patients. In postsurgical patients, susceptibility artifacts can potentially alter ipsilateral fMRI activation volumes and the assessment of language laterality. The purpose of this study was to investigate the ability of fMRI to correctly measure language dominance in brain tumor patients with previous surgery because this patient cohort is vulnerable to type II statistical errors and subsequent misjudgment of laterality. Twenty-six right-handed patients with left-hemisphere gliomas (16 with and 10 without previous surgery) underwent preoperative language fMRI. Language laterality was measured using hemispheric and Broca's area regions of interest (ROIs). Hemisphere dominance, as established by laterality measurements, was compared with that determined by intraoperative electrocorticography and behavioral assessments. Localization of primary language cortices was achieved in 24 of 26 patients studied. The hemisphere dominance evaluated by fMRI was verified by intraoperative corticography in only 14 patients (10 with and 4 without previous surgery), and only 12 of them had complete neuropsychological testing. Complete concordance of the laterality with intraoperative electrocorticography and behavioral assessments was found in patients without previous surgery. In patients with previous surgery, concordance was 75% using Broca's area ROI and 88% using hemispheric ROI, notwithstanding susceptibility artifacts. Differences in laterality between pre- and postsurgical patients, based on either hemispheric (P = 0.81) or Broca's area (P = 0.19) ROI measurements were not statistically significant. However, hemispheric ROI analyses were found to be less affected by postsurgical artifacts and may be more suitable for establishing hemisphere dominance. fMRI mapping of eloquent language cortices in brain tumor patients after surgery is feasible and can serve as a

Intraoperative brain tumor resection cavity characterization with conoscopic holography

NASA Astrophysics Data System (ADS)

Simpson, Amber L.; Burgner, Jessica; Chen, Ishita; Pheiffer, Thomas S.; Sun, Kay; Thompson, Reid C.; Webster, Robert J., III; Miga, Michael I.

2012-02-01

Brain shift compromises the accuracy of neurosurgical image-guided interventions if not corrected by either intraoperative imaging or computational modeling. The latter requires intraoperative sparse measurements for constraining and driving model-based compensation strategies. Conoscopic holography, an interferometric technique that measures the distance of a laser light illuminated surface point from a fixed laser source, was recently proposed for non-contact surface data acquisition in image-guided surgery and is used here for validation of our modeling strategies. In this contribution, we use this inexpensive, hand-held conoscopic holography device for intraoperative validation of our computational modeling approach to correcting for brain shift. Laser range scan, instrument swabbing, and conoscopic holography data sets were collected from two patients undergoing brain tumor resection therapy at Vanderbilt University Medical Center. The results of our study indicate that conoscopic holography is a promising method for surface acquisition since it requires no contact with delicate tissues and can characterize the extents of structures within confined spaces. We demonstrate that for two clinical cases, the acquired conoprobe points align with our model-updated images better than the uncorrected images lending further evidence that computational modeling approaches improve the accuracy of image-guided surgical interventions in the presence of soft tissue deformations.

Blood brain barrier: a challenge for effectual therapy of brain tumors.

PubMed

Bhowmik, Arijit; Khan, Rajni; Ghosh, Mrinal Kanti

2015-01-01

Brain tumors are one of the most formidable diseases of mankind. They have only a fair to poor prognosis and high relapse rate. One of the major causes of extreme difficulty in brain tumor treatment is the presence of blood brain barrier (BBB). BBB comprises different molecular components and transport systems, which in turn create efflux machinery or hindrance for the entry of several drugs in brain. Thus, along with the conventional techniques, successful modification of drug delivery and novel therapeutic strategies are needed to overcome this obstacle for treatment of brain tumors. In this review, we have elucidated some critical insights into the composition and function of BBB and along with it we have discussed the effective methods for delivery of drugs to the brain and therapeutic strategies overcoming the barrier.

Dietary Selenium Supplementation Modulates Growth of Brain Metastatic Tumors and Changes the Expression of Adhesion Molecules in Brain Microvessels.

PubMed

Wrobel, Jagoda K; Wolff, Gretchen; Xiao, Rijin; Power, Ronan F; Toborek, Michal

2016-08-01

Various dietary agents can modulate tumor invasiveness. The current study explored whether selenoglycoproteins (SeGPs) extracted from selenium-enriched yeast affect tumor cell homing and growth in the brain. Mice were fed diets enriched with specific SeGPs (SeGP40 or SeGP65, 1 mg/kg Se each), glycoproteins (GP40 or GP65, 0.2-0.3 mg/kg Se each) or a control diet (0.2-0.3 mg/kg Se) for 12 weeks. Then, murine Lewis lung carcinoma cells were infused into the brain circulation. Analyses were performed at early (48 h) and late stages (3 weeks) post tumor cell infusion. Imaging of tumor progression in the brain revealed that mice fed SeGP65-enriched diet displayed diminished metastatic tumor growth, fewer extravasating tumor cells and smaller metastatic lesions. While administration of tumor cells resulted in a significant upregulation of adhesion molecules in the early stage of tumor progression, overexpression of VCAM-1 (vascular call adhesion molecule-1) and ALCAM (activated leukocyte cell adhesion molecule) messenger RNA (mRNA) was diminished in SeGP65 supplemented mice. Additionally, mice fed SeGP65 showed decreased expression of acetylated NF-κB p65, 48 h post tumor cell infusion. The results indicate that tumor progression in the brain can be modulated by specific SeGPs. Selenium-containing compounds were more effective than their glycoprotein controls, implicating selenium as a potential negative regulator of metastatic process.

[Brain tumor immunotherapy: Illusion or hope?

PubMed

Migliorini, Denis; Dutoit, Valérie; Walker, Paul R; Dietrich, Pierre-Yves

2017-05-01

Immunotherapy has proven efficient for many tumors and is now part of standard of care in many indications. What is the picture for brain tumors? The recent development of anti-CTLA-4 and PD1 immune checkpoint inhibitors, which have the ability to restore T lymphocytes activity, has gathered enthusiasm and is now paving the way towards more complex models of immune system manipulation. These models include, among others, vaccination and adoptive T cell transfer technologies. Complementary to those strategies, molecules capable of reshaping the immune tumor microenvironment are currently being investigated in early phase trials. Indeed, the tumor bed is hostile to anti-tumor immune responses due to many escape mechanisms, and this is particularly true in the context of brain tumors, a master in eliciting immunosuppressive cells and molecules. The goal of this review is to describe the hopes and challenges of brain tumors immunotherapy and to propose an inventory of the current clinical research with specific focus on the therapies targeting the tumor microenvironment. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.

PubMed

Kaur, Taranjit; Saini, Barjinder Singh; Gupta, Savita

2018-03-01

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

Biodegradable brain-penetrating DNA nanocomplexes and their use to treat malignant brain tumors

PubMed Central

Mastorakos, Panagiotis; Zhang, Clark; Song, Eric; Kim, Young Eun; Park, Hee Won; Berry, Sneha; Choi, Won Kyu; Hanes, Justin; Suk, Jung Soo

2018-01-01

The discovery of powerful genetic targets has spurred clinical development of gene therapy approaches to treat patients with malignant brain tumors. However, lack of success in the clinic has been attributed to the inability of conventional gene vectors to achieve gene transfer throughout highly disseminated primary brain tumors. Here, we demonstrate ex vivo that small nanocomplexes composed of DNA condensed by a blend of biodegradable polymer, poly(β-amino ester) (PBAE), with PBAE conjugated with 5 kDa polyethylene glycol (PEG) molecules (PBAE-PEG) rapidly penetrate healthy brain parenchyma and orthotopic brain tumor tissues in rats. Rapid diffusion of these DNA-loaded nanocomplexes observed in fresh tissues ex vivo demonstrated that they avoided adhesive trapping in the brain owing to their dense PEG coating, which was critical to achieving widespread transgene expression throughout orthotopic rat brain tumors in vivo following administration by convection enhanced delivery. Transgene expression with the PBAE/PBAE-PEG blended nanocomplexes (DNA-loaded brain-penetrating nanocomplexes, or DNA-BPN) was uniform throughout the tumor core compared to nanocomplexes composed of DNA with PBAE only (DNA-loaded conventional nanocomplexes, or DNA-CN), and transgene expression reached beyond the tumor edge, where infiltrative cancer cells are found, only for the DNA-BPN formulation. Finally, DNA-BPN loaded with anti-cancer plasmid DNA provided significantly enhanced survival compared to the same plasmid DNA loaded in DNA-CN in two aggressive orthotopic brain tumor models in rats. These findings underscore the importance of achieving widespread delivery of therapeutic nucleic acids within brain tumors and provide a promising new delivery platform for localized gene therapy in the brain. PMID:28694032

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

Optical guidance for stereotactic brain tumor biopsy procedures: preliminary clinical evaluation (Conference Presentation)

NASA Astrophysics Data System (ADS)

Haj-Hosseini, Neda; Richter, Johan; Milos, Peter; Hallbeck, Martin; Wârdell, Karin

2017-02-01

In the routine of stereotactic biopsy on suspected tumors located deep in the brain or patients with multiple lesions, tissue samples are harvested to determine the type of malignancy. Biopsies are taken from pre-calculated positions based on the preoperative radiologic images susceptible to brain shift. In such cases the biopsy procedure may need to be repeated leading to a longer operation time. To provide guidance for targeting diagnostic tumor tissue and to avoid vessel rupture on the insertion path of the tumor, an application specific fiber optic probe was developed. The setup incorporated spectroscopy for 5-aminolevulinic acid induced protopophyrin IX (PpIX) fluorescence in the tumor and laser Doppler for measuring microvascular blood flow which recorded backscattered light (TLI) at 780 nm and blood perfusion. The recorded signals were compared to the histopathologic diagnosis of the tissue samples (n=16) and to the preoperative radiologic images. All together 146 fluorescence and 276 laser Doppler signals were recorded along 5 trajectories in 4 patients. On all occasions strong PpIX fluorescence peaks were visible during real-time guidance. Comparing the gliotic tumor marginal zone with the tumor, the PpIX (51 vs. 528 a.u., [0-1790], p < 0.05) was higher and TLI (2.9 vs. 2.0 a.u., [0-4.1], p < 0.05) was lower in tumor. The autofluorescence (104 vs.70 a.u., [0-442], p > 0.05) and blood perfusion (8.3 vs. 17 a.u., [0-254], p > 0.05) were not significantly different. In conclusion, the optical guidance probe made real-time tumor detection and vessel tracking possible during the stereotactic biopsy procedures. Moreover, the fluorescence and blood perfusion in the tumor could be studied at controlled positions in the brain and the tumor.

Potential application of a handheld confocal endomicroscope imaging system using a variety of fluorophores in experimental gliomas and normal brain.

PubMed

Martirosyan, Nikolay L; Georges, Joseph; Eschbacher, Jennifer M; Cavalcanti, Daniel D; Elhadi, Ali M; Abdelwahab, Mohammed G; Scheck, Adrienne C; Nakaji, Peter; Spetzler, Robert F; Preul, Mark C

2014-02-01

The authors sought to assess the feasibility of a handheld visible-wavelength confocal endomicroscope imaging system (Optiscan 5.1, Optiscan Pty., Ltd.) using a variety of rapid-acting fluorophores to provide histological information on gliomas, tumor margins, and normal brain in animal models. Mice (n = 25) implanted with GL261 cells were used to image fluorescein sodium (FNa), 5-aminolevulinic acid (5-ALA), acridine orange (AO), acriflavine (AF), and cresyl violet (CV). A U251 glioma xenograft model in rats (n = 5) was used to image sulforhodamine 101 (SR101). A swine (n = 3) model with AO was used to identify confocal features of normal brain. Images of normal brain, obvious tumor, and peritumoral zones were collected using the handheld confocal endomicroscope. Histological samples were acquired through biopsies from matched imaging areas. Samples were visualized with a benchtop confocal microscope. Histopathological features in corresponding confocal images and photomicrographs of H & E-stained tissues were reviewed. Fluorescence induced by FNa, 5-ALA, AO, AF, CV, and SR101 and detected with the confocal endomicroscope allowed interpretation of histological features. Confocal endomicroscopy revealed satellite tumor cells within peritumoral tissue, a definitive tumor border, and striking fluorescent cellular and subcellular structures. Fluorescence in various tumor regions correlated with standard histology and known tissue architecture. Characteristic features of different areas of normal brain were identified as well. Confocal endomicroscopy provided rapid histological information precisely related to the site of microscopic imaging with imaging characteristics of cells related to the unique labeling features of the fluorophores. Although experimental with further clinical trial validation required, these data suggest that intraoperative confocal imaging can help to distinguish normal brain from tumor and tumor margin and may have application in improving

Metastatic brain tumor

MedlinePlus

... the brain, the type of tissue involved, the original location of the tumor, and other factors. In rare cases, doctors do not know the original location. This is called cancer of unknown primary ( ...

Spotlight on Brain Tumors: Do You Know the Symptoms?

MedlinePlus

... Subscribe October 2017 Print this issue Spotlight on Brain Tumors Do You Know the Symptoms? En español ... at Epilepsy Wise Choices Possible Symptoms of a Brain Tumor The symptoms of a brain tumor depend ...

Magnetic resonance spectroscopy for detection of choline kinase inhibition in the treatment of brain tumors

PubMed Central

Kumar, Manoj; Arlauckas, Sean P.; Saksena, Sona; Verma, Gaurav; Ittyerah, Ranjit; Pickup, Stephen; Popov, Anatoliy V.; Delikatny, Edward J.; Poptani, Harish

2015-01-01

Abnormal choline metabolism is a hallmark of cancer and is associated with oncogenesis and tumor progression. Increased choline is consistently observed in both pre-clinical tumor models and in human brain tumors by proton magnetic resonance spectroscopy (MRS). Thus, inhibition of choline metabolism using specific choline kinase inhibitors such as MN58b may be a promising new strategy for treatment of brain tumors. We demonstrate the efficacy of MN58b in suppressing phosphocholine production in three brain tumor cell lines. In vivo MRS studies of rats with intra-cranial F98-derived brain tumors showed a significant decrease in tumor total choline concentration after treatment with MN58b. High resolution MRS of tissue extracts confirmed that this decrease was due to a significant reduction in phosphocholine. Concomitantly, a significant increase in poly-unsaturated lipid resonances was also observed in treated tumors, indicating apoptotic cell death. Magnetic resonance imaging (MRI) based volume measurements demonstrated a significant growth arrest in the MN58b-treated tumors in comparison to saline-treated controls. Histologically, MN58b-treated tumors showed decreased cell density, as well as increased apoptotic cells. These results suggest that inhibition of choline kinase can be used as an adjuvant to chemotherapy in the treatment of brain tumors and that decreases in total choline observed by MRS can be used as an effective phamacodynamic biomarker of treatment response. PMID:25657334

Android application for determining surgical variables in brain-tumor resection procedures

PubMed Central

Vijayan, Rohan C.; Thompson, Reid C.; Chambless, Lola B.; Morone, Peter J.; He, Le; Clements, Logan W.; Griesenauer, Rebekah H.; Kang, Hakmook; Miga, Michael I.

2017-01-01

Abstract. The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient’s preoperative image volume to more closely match the intraoperative state of the patient’s brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient’s neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables. PMID:28331887

Cellular phone use and brain tumor: a meta-analysis.

PubMed

Kan, Peter; Simonsen, Sara E; Lyon, Joseph L; Kestle, John R W

2008-01-01

The dramatic increase in the use of cellular phones has generated concerns about potential adverse effects, especially the development of brain tumors. We conducted a meta-analysis to examine the effect of cellular phone use on the risk of brain tumor development. We searched the literature using MEDLINE to locate case-control studies on cellular phone use and brain tumors. Odds ratios (ORs) for overall effect and stratified ORs associated with specific brain tumors, long-term use, and analog/digital phones were calculated for each study using its original data. A pooled estimator of each OR was then calculated using a random-effects model. Nine case-control studies containing 5,259 cases of primary brain tumors and 12,074 controls were included. All studies reported ORs according to brain tumor subtypes, and five provided ORs on patients with > or =10 years of follow up. Pooled analysis showed an overall OR of 0.90 (95% confidence interval [CI] 0.81-0.99) for cellular phone use and brain tumor development. The pooled OR for long-term users of > or =10 years (5 studies) was 1.25 (95% CI 1.01-1.54). No increased risk was observed in analog or digital cellular phone users. We found no overall increased risk of brain tumors among cellular phone users. The potential elevated risk of brain tumors after long-term cellular phone use awaits confirmation by future studies.

Confronting pediatric brain tumors: parent stories.

PubMed

McMillan, Gigi

2014-01-01

This narrative symposium brings to light the extreme difficulties faced by parents of children diagnosed with brain tumors. NIB editorial staff and narrative symposium editors, Gigi McMillan and Christy A. Rentmeester, developed a call for stories that was distributed on several list serves and posted on Narrative Inquiry in Bioethics' website. The call asks parents to share their personal experience of diagnosis, treatment, long-term effects of treatment, social issues and the doctor-patient-parent dynamic that develops during this process. Thirteen stories are found in the print version of the journal and an additional six supplemental stories are published online only through Project MUSE. One change readers may notice is that the story authors are not listed in alphabetical order. The symposium editors had a vision for this issue that included leading readers through the timeline of this topic: diagnosis-treatment-acute recovery-recurrence-treatment (again)-acute recovery (again)-long-term quality of life-(possibly) end of life. Stories are arranged to help lead the reader through this timeline.Gigi McMillan is a patient and research subject advocate, co-founder of We Can, Pediatric Brain Tumor Network, as well as, the mother of a child who suffered from a pediatric brain tumor. She also authored the introduction for this symposium. Christy Rentmeester is an Associate Professor of Health Policy and Ethics in the Creighton University School of Medicine. She served as a commentator for this issue. Other commentators for this issue are Michael Barraza, a clinical psychologist and board member of We Can, Pediatric Brain Tumor Network; Lisa Stern, a pediatrician who has diagnosed six children with brain tumors in her 20 years of practice; and Katie Rose, a pediatric brain tumor patient who shares her special insights about this world.

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.

Expression of hypoxia-inducible carbonic anhydrases in brain tumors

PubMed Central

Proescholdt, Martin A.; Mayer, Christina; Kubitza, Marion; Schubert, Thomas; Liao, Shu-Yuan; Stanbridge, Eric J.; Ivanov, Sergey; Oldfield, Edward H.; Brawanski, Alexander; Merrill, Marsha J.

2005-01-01

Malignant brain tumors exhibit distinct metabolic characteristics. Despite high levels of lactate, the intracellular pH of brain tumors is more alkaline than normal brain. Additionally, with increasing malignancy, brain tumors display intratumoral hypoxia. Carbonic anhydrase (CA) IX and XII are transmembrane isoenzymes that are induced by tissue hypoxia. They participate in regulation of pH homeostasis by catalyzing the reversible hydration of carbon dioxide. The aim of our study was to investigate whether brain tumors of different histology and grade of malignancy express elevated levels of CA IX and XII as compared to normal brain. We analyzed 120 tissue specimens from brain tumors (primary and metastatic) and normal brain for CA IX and XII expression by immunohistochemistry, Western blot, and in situ hybridization. Whereas normal brain tissue showed minimal levels of CA IX and XII expression, expression in tumors was found to be upregulated with increased level of malignancy. Hemangioblastomas, from patients with von Hippel–Lindau disease, also displayed high levels of CA IX and XII expression. Comparison of CA IX and XII staining with HIF-1α staining revealed a similar microanatomical distribution, indicating hypoxia as a major, but not the only, induction factor. The extent of CA IX and XII staining correlated with cell proliferation, as indicated by Ki67 labeling. The results demonstrate that CA IX and XII are upregulated in intrinsic and metastatic brain tumors as compared to normal brain tissue. This may contribute to the management of tumor-specific acid load and provide a therapeutic target. PMID:16212811

Lassa-Vesicular Stomatitis Chimeric Virus Safely Destroys Brain Tumors

PubMed Central

Wollmann, Guido; Drokhlyansky, Eugene; Davis, John N.; Cepko, Connie

2015-01-01

ABSTRACT High-grade tumors in the brain are among the deadliest of cancers. Here, we took a promising oncolytic virus, vesicular stomatitis virus (VSV), and tested the hypothesis that the neurotoxicity associated with the virus could be eliminated without blocking its oncolytic potential in the brain by replacing the neurotropic VSV glycoprotein with the glycoprotein from one of five different viruses, including Ebola virus, Marburg virus, lymphocytic choriomeningitis virus (LCMV), rabies virus, and Lassa virus. Based on in vitro infections of normal and tumor cells, we selected two viruses to test in vivo. Wild-type VSV was lethal when injected directly into the brain. In contrast, a novel chimeric virus (VSV-LASV-GPC) containing genes from both the Lassa virus glycoprotein precursor (GPC) and VSV showed no adverse actions within or outside the brain and targeted and completely destroyed brain cancer, including high-grade glioblastoma and melanoma, even in metastatic cancer models. When mice had two brain tumors, intratumoral VSV-LASV-GPC injection in one tumor (glioma or melanoma) led to complete tumor destruction; importantly, the virus moved contralaterally within the brain to selectively infect the second noninjected tumor. A chimeric virus combining VSV genes with the gene coding for the Ebola virus glycoprotein was safe in the brain and also selectively targeted brain tumors but was substantially less effective in destroying brain tumors and prolonging survival of tumor-bearing mice. A tropism for multiple cancer types combined with an exquisite tumor specificity opens a new door to widespread application of VSV-LASV-GPC as a safe and efficacious oncolytic chimeric virus within the brain. IMPORTANCE Many viruses have been tested for their ability to target and kill cancer cells. Vesicular stomatitis virus (VSV) has shown substantial promise, but a key problem is that if it enters the brain, it can generate adverse neurologic consequences, including death. We

Evaluation of D-isomers of 4-borono-2-18F-fluoro-phenylalanine and O-11C-methyl-tyrosine as brain tumor imaging agents: a comparative PET study with their L-isomers in rat brain glioma.

PubMed

Kanazawa, Masakatsu; Nishiyama, Shingo; Hashimoto, Fumio; Kakiuchi, Takeharu; Tsukada, Hideo

2018-06-13

The potential of the D-isomerization of 4-borono-2- 18 F-fluoro-phenylalanine ( 18 F-FBPA) to improve its target tumor to non-target normal brain tissue ratio (TBR) was evaluated in rat brain glioma and compared with those of L- and D- 11 C-methyl-tyrosine ( 11 C-CMT). The L- or D-isomer of 18 F-FBPA was injected into rats through the tail vein, and their whole body kinetics and distributions were assessed using the tissue dissection method up to 90 min after the injection. The kinetics of L- and D- 18 F-FBPA or L- and D- 11 C-CMT in the C-6 glioma-inoculated rat brain were measured for 90 or 60 min, respectively, using high-resolution animal PET, and their TBRs were assessed. Tissue dissection analyses showed that D- 18 F-FBPA uptake was significantly lower than that of L- 18 F-FBPA in the brain and abdominal organs, except for the kidney and bladder, reflecting the faster elimination rate of D- 18 F-FBPA than L- 18 F-FBPA from the blood to the urinary tract. PET imaging using 18 F-FBPA revealed that although the brain uptake of D- 18 F-FBPA was significantly lower than that of L- 18 F-FBPA, the TBR of the D-isomer improved to 6.93 from 1.45 for the L-isomer. Similar results were obtained with PET imaging using 11 C-CMT with a smaller improvement in TBR to 1.75 for D- 11 C-CMT from 1.33 for L- 11 C-CMT. The present results indicate that D- 18 F-FBPA is a better brain tumor imaging agent with higher TBR than its original L-isomer and previously reported tyrosine-based PET imaging agents. This improved TBR of D- 18 F-FBPA without any pre-treatments, such as tentative blood-brain barrier disruption using hyperosmotic agents or sonication, suggests that the D-isomerization of BPA results in the more selective accumulation of 10 B in tumor cells that is more effective and less toxic than conventional L-BPA.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

[RSF model optimization and its application to brain tumor segmentation in MRI].

PubMed

Cheng, Zhaoning; Song, Zhijian

2013-04-01

Magnetic resonance imaging (MRI) is usually obscure and non-uniform in gray, and the tumors inside are poorly circumscribed, hence the automatic tumor segmentation in MRI is very difficult. Region-scalable fitting (RSF) energy model is a new segmentation approach for some uneven grayscale images. However, the level set formulation (LSF) of RSF model is not suitable for the environment with different grey level distribution inside and outside the intial contour, and the complex intensity environment of MRI always makes it hard to get ideal segmentation results. Therefore, we improved the model by a new LSF and combined it with the mean shift method, which can be helpful for tumor segmentation and has better convergence and target direction. The proposed method has been utilized in a series of studies for real MRI images, and the results showed that it could realize fast, accurate and robust segmentations for brain tumors in MRI, which has great clinical significance.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

NASA Astrophysics Data System (ADS)

Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2016-03-01

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.

PubMed

Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2016-03-22

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

PubMed Central

Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2016-01-01

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83–0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments. PMID:27001047

Recruited brain tumor-derived mesenchymal stem cells contribute to brain tumor progression.

PubMed

Behnan, Jinan; Isakson, Pauline; Joel, Mrinal; Cilio, Corrado; Langmoen, Iver A; Vik-Mo, Einar O; Badn, Wiaam

2014-05-01

The identity of the cells that contribute to brain tumor structure and progression remains unclear. Mesenchymal stem cells (MSCs) have recently been isolated from normal mouse brain. Here, we report the infiltration of MSC-like cells into the GL261 murine glioma model. These brain tumor-derived mesenchymal stem cells (BT-MSCs) are defined with the phenotype (Lin-Sca-1+CD9+CD44+CD166+/-) and have multipotent differentiation capacity. We show that the infiltration of BT-MSCs correlates to tumor progression; furthermore, BT-MSCs increased the proliferation rate of GL261 cells in vitro. For the first time, we report that the majority of GL261 cells expressed mesenchymal phenotype under both adherent and sphere culture conditions in vitro and that the non-MSC population is nontumorigenic in vivo. Although the GL261 cell line expressed mesenchymal phenotype markers in vitro, most BT-MSCs are recruited cells from host origin in both wild-type GL261 inoculated into green fluorescent protein (GFP)-transgenic mice and GL261-GFP cells inoculated into wild-type mice. We show the expression of chemokine receptors CXCR4 and CXCR6 on different recruited cell populations. In vivo, the GL261 cells change marker profile and acquire a phenotype that is more similar to cells growing in sphere culture conditions. Finally, we identify a BT-MSC population in human glioblastoma that is CD44+CD9+CD166+ both in freshly isolated and culture-expanded cells. Our data indicate that cells with MSC-like phenotype infiltrate into the tumor stroma and play an important role in tumor cell growth in vitro and in vivo. Thus, we suggest that targeting BT-MSCs could be a possible strategy for treating glioblastoma patients. © 2013 AlphaMed Press.

Stereotactic Radiosurgery in Treating Patients With Brain Tumors

ClinicalTrials.gov

2012-03-21

Adult Central Nervous System Germ Cell Tumor; Adult Malignant Meningioma; Adult Medulloblastoma; Adult Noninfiltrating Astrocytoma; Adult Oligodendroglioma; Adult Craniopharyngioma; Adult Meningioma; Brain Metastases; Adult Ependymoma; Adult Pineal Parenchymal Tumor; Adult Brain Stem Glioma; Adult Infiltrating Astrocytoma; Mixed Gliomas; Stage IV Peripheral Primitive Neuroectodermal Tumor

Coexistence of Multiple Sclerosis and Brain Tumor: An Uncommon Diagnostic Challenge.

PubMed

Abrishamchi, Fatemeh; Khorvash, Fariborz

2017-01-01

Nonneoplastic demyelinating processes of the brain with mass effect on magnetic resonance imaging can cause diagnostic difficulties. It requires differential diagnosis between the tumefactive demyelinating lesion and the coexistence of neoplasm. We document the case of 41-year-old woman with clinical and radiological findings suggestive of multiple sclerosis. Additional investigations confirmed the coexistence of astrocytoma. This report emphasizes the importance of considering brain tumors in the differential diagnosis of primary demyelinating disease presenting with a cerebral mass lesion.

Efficacy, safety and outcome of frameless image-guided robotic radiosurgery for brain metastases after whole brain radiotherapy.

PubMed

Lohkamp, Laura-Nanna; Vajkoczy, Peter; Budach, Volker; Kufeld, Markus

2018-05-01

Estimating efficacy, safety and outcome of frameless image-guided robotic radiosurgery for the treatment of recurrent brain metastases after whole brain radiotherapy (WBRT). We performed a retrospective single-center analysis including patients with recurrent brain metastases after WBRT, who have been treated with single session radiosurgery, using the CyberKnife® Radiosurgery System (CKRS) (Accuray Inc., CA) between 2011 and 2016. The primary end point was local tumor control, whereas secondary end points were distant tumor control, treatment-related toxicity and overall survival. 36 patients with 140 recurrent brain metastases underwent 46 single session CKRS treatments. Twenty one patients had multiple brain metastases (58%). The mean interval between WBRT and CKRS accounted for 2 years (range 0.2-7 years). The median number of treated metastases per treatment session was five (range 1-12) with a tumor volume of 1.26 ccm (mean) and a median tumor dose of 18 Gy prescribed to the 70% isodose line. Two patients experienced local tumor recurrence within the 1st year after treatment and 13 patients (36%) developed novel brain metastases. Nine of these patients underwent additional one to three CKRS treatments. Eight patients (22.2%) showed treatment-related radiation reactions on MRI, three with clinical symptoms. Median overall survival was 19 months after CKRS. The actuarial 1-year local control rate was 94.2%. CKRS has proven to be locally effective and safe due to high local tumor control rates and low toxicity. Thus CKRS offers a reliable salvage treatment option for recurrent brain metastases after WBRT.

Dye-Enhanced Multimodal Confocal Imaging of Brain Cancers

NASA Astrophysics Data System (ADS)

Wirth, Dennis; Snuderl, Matija; Sheth, Sameer; Curry, William; Yaroslavsky, Anna

2011-04-01

Background and Significance: Accurate high resolution intraoperative detection of brain tumors may result in improved patient survival and better quality of life. The goal of this study was to evaluate dye enhanced multimodal confocal imaging for discriminating normal and cancerous brain tissue. Materials and Methods: Fresh thick brain specimens were obtained from the surgeries. Normal and cancer tissues were investigated. Samples were stained in methylene blue and imaged. Reflectance and fluorescence signals were excited at 658nm. Fluorescence emission and polarization were registered from 670 nm to 710 nm. The system provided lateral resolution of 0.6 μm and axial resolution of 7 μm. Normal and cancer specimens exhibited distinctively different characteristics. H&E histopathology was processed from each imaged sample. Results and Conclusions: The analysis of normal and cancerous tissues indicated clear differences in appearance in both the reflectance and fluorescence responses. These results confirm the feasibility of multimodal confocal imaging for intraoperative detection of small cancer nests and cells.

Patients With Brain Tumors: Who Receives Postacute Occupational Therapy Services?

PubMed

Chan, Vincy; Xiong, Chen; Colantonio, Angela

2015-01-01

Data on the utilization of occupational therapy among patients with brain tumors have been limited to those with malignant tumors and small samples of patients outside North America in specialized palliative care settings. We built on this research by examining the characteristics of patients with brain tumors who received postacute occupational therapy services in Ontario, Canada, using health care administrative data. Between fiscal years 2004-2005 and 2008-2009, 3,199 patients with brain tumors received occupational therapy services in the home care setting after hospital discharge; 12.4% had benign brain tumors, 78.2% had malignant brain tumors, and 9.4% had unspecified brain tumors. However, patients with benign brain tumors were older (mean age=63.3 yr), and a higher percentage were female (65.2%). More than 90% of patients received in-home occupational therapy services. Additional research is needed to examine the significance of these differences and to identify factors that influence access to occupational therapy services in the home care setting. Copyright © 2015 by the American Occupational Therapy Association, Inc.

Targeted Doxorubicin Delivery to Brain Tumors via Minicells: Proof of Principle Using Dogs with Spontaneously Occurring Tumors as a Model.

PubMed

MacDiarmid, Jennifer A; Langova, Veronika; Bailey, Dale; Pattison, Scott T; Pattison, Stacey L; Christensen, Neil; Armstrong, Luke R; Brahmbhatt, Vatsala N; Smolarczyk, Katarzyna; Harrison, Matthew T; Costa, Marylia; Mugridge, Nancy B; Sedliarou, Ilya; Grimes, Nicholas A; Kiss, Debra L; Stillman, Bruce; Hann, Christine L; Gallia, Gary L; Graham, Robert M; Brahmbhatt, Himanshu

2016-01-01

Cytotoxic chemotherapy can be very effective for the treatment of cancer but toxicity on normal tissues often limits patient tolerance and often causes long-term adverse effects. The objective of this study was to assist in the preclinical development of using modified, non-living bacterially-derived minicells to deliver the potent chemotherapeutic doxorubicin via epidermal growth factor receptor (EGFR) targeting. Specifically, this study sought to evaluate the safety and efficacy of EGFR targeted, doxorubicin loaded minicells (designated EGFRminicellsDox) to deliver doxorubicin to spontaneous brain tumors in 17 companion dogs; a comparative oncology model of human brain cancers. EGFRminicellsDox were administered weekly via intravenous injection to 17 dogs with late-stage brain cancers. Biodistribution was assessed using single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI). Anti-tumor response was determined using MRI, and blood samples were subject to toxicology (hematology, biochemistry) and inflammatory marker analysis. Targeted, doxorubicin-loaded minicells rapidly localized to the core of brain tumors. Complete resolution or marked tumor regression (>90% reduction in tumor volume) were observed in 23.53% of the cohort, with lasting anti-tumor responses characterized by remission in three dogs for more than two years. The median overall survival was 264 days (range 49 to 973). No adverse clinical, hematological or biochemical effects were observed with repeated administration of EGFRminicellsDox (30 to 98 doses administered in 10 of the 17 dogs). Targeted minicells loaded with doxorubicin were safely administered to dogs with late stage brain cancer and clinical activity was observed. These findings demonstrate the strong potential for clinical applications of targeted, doxorubicin-loaded minicells for the effective treatment of patients with brain cancer. On this basis, we have designed a Phase 1 clinical study of EGFR

Targeted Doxorubicin Delivery to Brain Tumors via Minicells: Proof of Principle Using Dogs with Spontaneously Occurring Tumors as a Model

PubMed Central

MacDiarmid, Jennifer A.; Langova, Veronika; Bailey, Dale; Pattison, Scott T.; Pattison, Stacey L.; Christensen, Neil; Armstrong, Luke R.; Brahmbhatt, Vatsala N.; Smolarczyk, Katarzyna; Harrison, Matthew T.; Costa, Marylia; Mugridge, Nancy B.; Sedliarou, Ilya; Grimes, Nicholas A.; Kiss, Debra L.; Stillman, Bruce; Hann, Christine L.; Gallia, Gary L.; Graham, Robert M.; Brahmbhatt, Himanshu

2016-01-01

Background Cytotoxic chemotherapy can be very effective for the treatment of cancer but toxicity on normal tissues often limits patient tolerance and often causes long-term adverse effects. The objective of this study was to assist in the preclinical development of using modified, non-living bacterially-derived minicells to deliver the potent chemotherapeutic doxorubicin via epidermal growth factor receptor (EGFR) targeting. Specifically, this study sought to evaluate the safety and efficacy of EGFR targeted, doxorubicin loaded minicells (designated EGFRminicellsDox) to deliver doxorubicin to spontaneous brain tumors in 17 companion dogs; a comparative oncology model of human brain cancers. Methodology/Principle Findings EGFRminicellsDox were administered weekly via intravenous injection to 17 dogs with late-stage brain cancers. Biodistribution was assessed using single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI). Anti-tumor response was determined using MRI, and blood samples were subject to toxicology (hematology, biochemistry) and inflammatory marker analysis. Targeted, doxorubicin-loaded minicells rapidly localized to the core of brain tumors. Complete resolution or marked tumor regression (>90% reduction in tumor volume) were observed in 23.53% of the cohort, with lasting anti-tumor responses characterized by remission in three dogs for more than two years. The median overall survival was 264 days (range 49 to 973). No adverse clinical, hematological or biochemical effects were observed with repeated administration of EGFRminicellsDox (30 to 98 doses administered in 10 of the 17 dogs). Conclusions/Significance Targeted minicells loaded with doxorubicin were safely administered to dogs with late stage brain cancer and clinical activity was observed. These findings demonstrate the strong potential for clinical applications of targeted, doxorubicin-loaded minicells for the effective treatment of patients with brain cancer. On

Pediatric Brain Tumors: Genomics and Epigenomics Pave the Way.

PubMed

Fontebasso, Adam M; Jabado, Nada

2015-01-01

Primary malignant brain tumors remain a disproportionate cause of morbidity and mortality in humans. A number of studies exploring the cancer genome of brain tumors across ages using integrated genetics and epigenetics and next-generation sequencing technologies have recently emerged. This has led to considerable advances in the understanding of the basic biology and pathogenesis of brain tumors, including the most malignant and common variants in children: gliomas and medulloblastoma. Notably, studies of pediatric brain tumors have identified unexpected oncogenic pathways implicated in tumorigenesis. These range from a single pathway/molecule defect such as abnormalities of the mitogen-activated protein kinase pathway, considered to be a hallmark of pilocytic astrocytomas, to alterations in the epigenome as a critical component altered in many subgroups of high-grade brain tumors. Importantly, the type, timing, and spatial clustering of these molecular alterations provide a better understanding of the pathogenesis of the respective brain tumor they target and critical markers for therapy that will help refine pathological grading. We summarize these novel findings in pediatric brain tumors, which also are put in the context of the evolving notion of molecular pathology, now a mandated tool for proper classification and therapy assignment in the clinical setting.

TH-EF-207A-06: High-Resolution Optical-CT/ECT Imaging of Unstained Mice Femur, Brain, Spleen, and Tumor

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

Yoon, S; Dewhirst, M; Oldham, M

2016-06-15

Purpose: Optical transmission and emission computed tomography (optical-CT/ECT) provides high-resolution 3D attenuation and emission maps in unsectioned large (∼1cm{sup 3}) ex vivo tissue samples at a resolution of 12.9µm{sup 3} per voxel. Here we apply optical-CT/ECT to investigate high-resolution structure and auto-fluorescence in a range of optically cleared mice organs, including, for the first time, mouse bone (femur), opening the potential for study of bone metastasis and bone-mediated immune response. Methods: Three BALBc mice containing 4T1 flank tumors were sacrificed to obtain spleen, brain, tumor, and femur. Tissues were washed in 4% PFA, fixed in EtOH solution (for 5, 10,more » 10, and 2 days respectively), and then optically cleared for 3 days in BABBs. The femur was also placed in 0.25M aqueous EDTA for 15–30 days to remove calcium. Optical-CT/ECT attenuation and emission maps at 633nm (the latter using 530nm excitation light) were obtained for all samples. Bi-telecentric optical-CT was compared side-by-side with conventional optical projection tomography (OPT) imaging to evaluate imaging capability of these two rival techniques. Results: Auto-fluorescence mapping of femurs reveals vasculatures and fluorescence heterogeneity. High signals (A.U.=10) are reported in the medullary cavity but not in the cortical bone (A.U.=1). The brain strongly and uniform auto-fluoresces (A.U.=5). Thick, optically dense organs such as the spleen and the tumor (0.12, 0.46OD/mm) are reconstructed at depth without significant loss of resolution, which we attribute to the bi-telecentric optics of optical-CT. The attenuation map of tumor reveals vasculature, attenuation heterogeneity, and possibly necrotic tissue. Conclusion: We demonstrate the feasibility of optical-CT/ECT imaging of un-sectioned mice bones (femurs) and spleen with high resolution. This result, and the characterization of unstained organs, are important steps enabling future studies involving optical

A novel pre-clinical in vivo mouse model for malignant brain tumor growth and invasion.

PubMed

Shelton, Laura M; Mukherjee, Purna; Huysentruyt, Leanne C; Urits, Ivan; Rosenberg, Joshua A; Seyfried, Thomas N

2010-09-01

Glioblastoma multiforme (GBM) is a rapidly progressive disease of morbidity and mortality and is the most common form of primary brain cancer in adults. Lack of appropriate in vivo models has been a major roadblock to developing effective therapies for GBM. A new highly invasive in vivo GBM model is described that was derived from a spontaneous brain tumor (VM-M3) in the VM mouse strain. Highly invasive tumor cells could be identified histologically on the hemisphere contralateral to the hemisphere implanted with tumor cells or tissue. Tumor cells were highly expressive for the chemokine receptor CXCR4 and the proliferation marker Ki-67 and could be identified invading through the pia mater, the vascular system, the ventricular system, around neurons, and over white matter tracts including the corpus callosum. In addition, the brain tumor cells were labeled with the firefly luciferase gene, allowing for non-invasive detection and quantitation through bioluminescent imaging. The VM-M3 tumor has a short incubation time with mortality occurring in 100% of the animals within approximately 15 days. The VM-M3 brain tumor model therefore can be used in a pre-clinical setting for the rapid evaluation of novel anti-invasive therapies.

Spontaneous delayed brain herniation through a subdural membrane after tumor surgery.

PubMed

Van Dycke, Annelies; Okito, Jean-Pierre Kalala; Acou, Marjan; Deblaere, Karel; Hemelsoet, Dimitri; Van Roost, Dirk

2013-12-01

We report on a rare case of spontaneous cerebral herniation through a subdural membrane in a 54-year-old patient. Brain herniation in adults as a complication of chronic subdural hematomas shortly after a neurosurgical intervention is rare. We are the first to report a case of delayed local herniation in an adult patient more than 1 year after a neurosurgical procedure. The patient suffered from a low-grade oligodendroglioma since 1993. Radiotherapy was then applied, followed by resective surgery and chemotherapy in 2008 because of tumor progression. Subsequently, he developed a symptomatic subdural hygroma treated with a subduro-atrial cerebrospinal fluid shunt. In January 2010, the shunt was occluded. Follow-up brain imaging showed a stable situation after tumor resection, with a cyst in the temporal resection cavity and a stable subdural hygroma. In February 2011, the patient visited the emergency department because of an acute right hemiparesis and progressive motor aphasia. Urgent magnetic resonance imaging was suspicious of a herniation of brain parenchyma in the left middle cranial fossa. Explorative surgery showed a locally incarcerated brain herniation through a membrane with a ring-like aperture. Resection of this membrane led to normalization of the position of the brain tissue and to clinical improvement. Brain herniation through a subdural membrane is an extremely rare complication, but must be a differential diagnosis in patients with a known chronic subdural hematoma or hygroma and clinical deterioration, even in the absence of recent surgery. Urgent surgical intervention of the herniated brain is recommended to reduce the risk of permanent neurological damage. Georg Thieme Verlag KG Stuttgart · New York.

Real-time simulation and visualization of volumetric brain deformation for image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Ferrant, Matthieu; Nabavi, Arya; Macq, Benoit M. M.; Kikinis, Ron; Warfield, Simon K.

2001-05-01

During neurosurgery, the challenge for the neurosurgeon is to remove as much as possible of a tumor without destroying healthy tissue. This can be difficult because healthy and diseased tissue can have the same visual appearance. To this aim, and because the surgeon cannot see underneath the brain surface, image-guided neurosurgery systems are being increasingly used. However, during surgery, deformation of the brain occurs (due to brain shift and tumor resection), therefore causing errors in the surgical planning with respect to preoperative imaging. In our previous work, we developed software for capturing the deformation of the brain during neurosurgery. The software also allows preoperative data to be updated according to the intraoperative imaging so as to reflect the shape changes of the brain during surgery. Our goal in this paper was to rapidly visualize and characterize this deformation over the course of surgery with appropriate tools. Therefore, we developed tools allowing the doctor to visualize (in 2D and 3D) deformations, as well as the stress tensors characterizing the deformation along with the updated preoperative and intraoperative imaging during the course of surgery. Such tools significantly add to the value of intraoperative imaging and hence could improve surgical outcomes.

First noninvasive thermal ablation of a brain tumor with MR-guided focused ultrasound

PubMed Central

2014-01-01

Magnetic resonance-guided focused ultrasound surgery (MRgFUS) allows for precise thermal ablation of target tissues. While this emerging modality is increasingly used for the treatment of various types of extracranial soft tissue tumors, it has only recently been acknowledged as a modality for noninvasive neurosurgery. MRgFUS has been particularly successful for functional neurosurgery, whereas its clinical application for tumor neurosurgery has been delayed for various technical and procedural reasons. Here, we report the case of a 63-year-old patient presenting with a centrally located recurrent glioblastoma who was included in our ongoing clinical phase I study aimed at evaluating the feasibility and safety of transcranial MRgFUS for brain tumor ablation. Applying 25 high-power sonications under MR imaging guidance, partial tumor ablation could be achieved without provoking neurological deficits or other adverse effects in the patient. This proves, for the first time, the feasibility of using transcranial MR-guided focused ultrasound to safely ablate substantial volumes of brain tumor tissue. PMID:25671132

General Information about Childhood Brain and Spinal Cord Tumors

MedlinePlus

... Cord Tumors Treatment Overview (PDQ®)–Patient Version General Information About Childhood Brain and Spinal Cord Tumors Go ... types of brain and spinal cord tumors. The information from tests and procedures done to detect (find) ...

Nano to micro delivery systems: targeting angiogenesis in brain tumors.

PubMed

Gilert, Ariel; Machluf, Marcelle

2010-10-08

Treating brain tumors using inhibitors of angiogenesis is extensively researched and tested in clinical trials. Although anti-angiogenic treatment holds a great potential for treating primary and secondary brain tumors, no clinical treatment is currently approved for brain tumor patients. One of the main hurdles in treating brain tumors is the blood brain barrier - a protective barrier of the brain, which prevents drugs from entering the brain parenchyma. As most therapeutics are excluded from the brain there is an urgent need to develop delivery platforms which will bypass such hurdles and enable the delivery of anti-angiogenic drugs into the tumor bed. Such delivery systems should be able to control release the drug or a combination of drugs at a therapeutic level for the desired time. In this mini-review we will discuss the latest improvements in nano and micro drug delivery platforms that were designed to deliver inhibitors of angiogenesis to the brain.

Nano to micro delivery systems: targeting angiogenesis in brain tumors

PubMed Central

2010-01-01

Treating brain tumors using inhibitors of angiogenesis is extensively researched and tested in clinical trials. Although anti-angiogenic treatment holds a great potential for treating primary and secondary brain tumors, no clinical treatment is currently approved for brain tumor patients. One of the main hurdles in treating brain tumors is the blood brain barrier - a protective barrier of the brain, which prevents drugs from entering the brain parenchyma. As most therapeutics are excluded from the brain there is an urgent need to develop delivery platforms which will bypass such hurdles and enable the delivery of anti-angiogenic drugs into the tumor bed. Such delivery systems should be able to control release the drug or a combination of drugs at a therapeutic level for the desired time. In this mini-review we will discuss the latest improvements in nano and micro drug delivery platforms that were designed to deliver inhibitors of angiogenesis to the brain. PMID:20932320

Recurrent medulloblastoma: Frequency of tumor enhancement on Gd-DTPA MR imaging

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

Rollins, N.; Mendelsohn, D.; Mulne, A.

1990-05-01

Thirty-two children with medulloblastoma were evaluated postoperatively with conventional and gadolinium-enhanced MR imaging. Eleven patients had abnormal cranial MR studies; nine of these had recurrent tumor. In six patients recurrent tumor enhanced with Gd, while in the other three patients recurrent tumor did not enhance. The remaining two patients had areas of abnormal Gd enhancement that were caused by radiation-induced breakdown of the blood-brain barrier rather than by recurrent tumor. This study shows that not all recurrent medulloblastoma enhances and that the absence of Gd enhancement does not necessarily indicate the absence of recurrent tumor.

Recurrent medulloblastoma: Frequency of tumor enhancement on Gd-DTPA MR imaging

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

Rollins, N.; Mendelsohn, D.; Mulne, A.

1990-07-01

Thirty-two children with medulloblastoma were evaluated postoperatively with conventional and gadolinium-enhanced MR imaging. Eleven patients had abnormal cranial MR studies; nine of these had recurrent tumor. In six patients recurrent tumor enhanced with Gd, while in the other three patients recurrent tumor did not enhance. The remaining two patients had areas of abnormal Gd enhancement that were caused by radiation-induced breakdown of the blood-brain barrier rather than by recurrent tumor. This study shows that not all recurrent medulloblastoma enhances and that the absence of Gd enhancement does not necessarily indicate the absence of recurrent tumor.

Pretreatment prediction of brain tumors' response to radiation therapy using high b-value diffusion-weighted MRI.

PubMed

Mardor, Yael; Roth, Yiftach; Ochershvilli, Aharon; Spiegelmann, Roberto; Tichler, Thomas; Daniels, Dianne; Maier, Stephan E; Nissim, Ouzi; Ram, Zvi; Baram, Jacob; Orenstein, Arie; Pfeffer, Raphael

2004-01-01

Diffusion-weighted magnetic resonance imaging (DWMRI) is sensitive to tissues' biophysical characteristics, including apparent diffusion coefficients (ADCs) and volume fractions of water in different populations. In this work, we evaluate the clinical efficacy of DWMRI and high diffusion-weighted magnetic resonance imaging (HDWMRI), acquired up to b = 4000 sec/mm(2) to amplify sensitivity to water diffusion properties, in pretreatment prediction of brain tumors' response to radiotherapy. Twelve patients with 20 brain lesions were studied. Six ring-enhancing lesions were excluded due to their distinct diffusion characteristics. Conventional and DWMRI were acquired on a 0.5-T MRI. Response to therapy was determined from relative changes in tumor volumes calculated from contrast-enhanced T1-weighted MRI, acquired before and a mean of 46 days after beginning therapy. ADCs and a diffusion index, R(D), reflecting tissue viability based on water diffusion were calculated from DWMRIs. Pretreatment values of ADC and R(D) were found to correlate significantly with later tumor response/nonresponse (r = 0.76, P <.002 and r = 0.77, P <.001). This correlation implies that tumors with low pretreatment diffusion values, indicating high viability, will respond better to radiotherapy than tumors with high diffusion values, indicating necrosis. These results demonstrate the feasibility of using DWMRI for pretreatment prediction of response to therapy in patients with brain tumors undergoing radiotherapy.

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

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

Brain Tumors - Multiple Languages

MedlinePlus

... FAQs Customer Support Health Topics Drugs & Supplements Videos & Tools You Are Here: Home → Multiple Languages → All Health Topics → Brain Tumors URL of this page: https://medlineplus.gov/ ...

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.

Efficacy of NGR peptide-modified PEGylated quantum dots for crossing the blood-brain barrier and targeted fluorescence imaging of glioma and tumor vasculature.

PubMed

Huang, Ning; Cheng, Si; Zhang, Xiang; Tian, Qi; Pi, Jiangli; Tang, Jun; Huang, Qing; Wang, Feng; Chen, Jin; Xie, Zongyi; Xu, Zhongye; Chen, Weifu; Zheng, Huzhi; Cheng, Yuan

2017-01-01

Delivery of imaging agents to brain glioma is challenging because the blood-brain barrier (BBB) functions as a physiological checkpoint guarding the central nervous system from circulating large molecules. Moreover, the ability of existing probes to target glioma has been insufficient and needs to be improved. In present study, PEG-based long circulation, CdSe/ZnS quantum dots (QDs)-based nanoscale and fluorescence, asparagines-glycine-arginine peptides (NGR)-based specific CD13 recognition were integrated to design and synthesize a novel nanoprobe by conjugating biotinylated NGR peptides to avidin-PEG-coated QDs. Our data showed that the NGR-PEG-QDs were nanoscale with less than 100 nm and were stable in various pH (4.0~8.0). These nanomaterials with non-toxic concentrations could cross the BBB and target CD13-overexpressing glioma and tumor vasculature in vitro and in vivo, contributing to fluorescence imaging of this brain malignancy. These achievements allowed groundbreaking technological advances in targeted fluorescence imaging for the diagnosis and surgical removal of glioma, facilitating potential transformation toward clinical nanomedicine. Copyright © 2016 Elsevier Inc. All rights reserved.

Novel treatment strategies for brain tumors and metastases

PubMed Central

El-Habashy, Salma E.; Nazief, Alaa M.; Adkins, Chris E.; Wen, Ming Ming; El-Kamel, Amal H.; Hamdan, Ahmed M.; Hanafy, Amira S.; Terrell, Tori O.; Mohammad, Afroz S.; Lockman, Paul R.; Nounou, Mohamed Ismail

2015-01-01

This review summarizes patent applications in the past 5 years for the management of brain tumors and metastases. Most of the recent patents discuss one of the following strategies: the development of new drug entities that specifically target the brain cells, the blood–brain barrier and the tumor cells, tailor-designing a novel carrier system that is able to perform multitasks and multifunction as a drug carrier, targeting vehicle and even as a diagnostic tool, direct conjugation of a US FDA approved drug with a targeting moiety, diagnostic moiety or PK modifying moiety, or the use of innovative nontraditional approaches such as genetic engineering, stem cells and vaccinations. Until now, there has been no optimal strategy to deliver therapeutic agents to the CNS for the treatment of brain tumors and metastases. Intensive research efforts are actively ongoing to take brain tumor targeting, and novel and targeted CNS delivery systems to potential clinical application. PMID:24998288

A noninvasive multimodal technique to monitor brain tumor vascularization

NASA Astrophysics Data System (ADS)