Dataset Information

Delayed contrast extravasation MRI: a new paradigm in neuro-oncology.

ABSTRACT: Conventional magnetic resonance imaging (MRI) is unable to differentiate tumor/nontumor enhancing tissues. We have applied delayed-contrast MRI for calculating high resolution treatment response assessment maps (TRAMs) clearly differentiating tumor/nontumor tissues in brain tumor patients.One hundred and fifty patients with primary/metastatic tumors were recruited and scanned by delayed-contrast MRI and perfusion MRI. Of those, 47 patients underwent resection during their participation in the study. Region of interest/threshold analysis was performed on the TRAMs and on relative cerebral blood volume maps, and correlation with histology was studied. Relative cerebral blood volume was also assessed by the study neuroradiologist.Histological validation confirmed that regions of contrast agent clearance in the TRAMs >1 h post contrast injection represent active tumor, while regions of contrast accumulation represent nontumor tissues with 100% sensitivity and 92% positive predictive value to active tumor. Significant correlation was found between tumor burden in the TRAMs and histology in a subgroup of lesions resected en bloc (r(2) = 0.90, P < .0001). Relative cerebral blood volume yielded sensitivity/positive predictive values of 51%/96% and there was no correlation with tumor burden. The feasibility of applying the TRAMs for differentiating progression from treatment effects, depicting tumor within hemorrhages, and detecting residual tumor postsurgery is demonstrated.The TRAMs present a novel model-independent approach providing efficient separation between tumor/nontumor tissues by adding a short MRI scan >1 h post contrast injection. The methodology uses robust acquisition sequences, providing high resolution and easy to interpret maps with minimal sensitivity to susceptibility artifacts. The presented results provide histological validation of the TRAMs and demonstrate their potential contribution to the management of brain tumor patients.

SUBMITTER: Zach L

PROVIDER: S-EPMC4483101 | biostudies-other | 2015 Mar

REPOSITORIES: biostudies-other

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Delayed contrast extravasation MRI: a new paradigm in neuro-oncology.

Zach Leor L Guez David D Last David D Daniels Dianne D Grober Yuval Y Nissim Ouzi O Hoffmann Chen C Nass Dvora D Talianski Alisa A Spiegelmann Roberto R Tsarfaty Galia G Salomon Sharona S Hadani Moshe M Kanner Andrew A Blumenthal Deborah T DT Bukstein Felix F Yalon Michal M Zauberman Jacob J Roth Jonathan J Shoshan Yigal Y Fridman Evgeniya E Wygoda Marc M Limon Dror D Tzuk Tzahala T Cohen Zvi R ZR Mardor Yael Y

Neuro-oncology 20141130 3

<h4>Background</h4>Conventional magnetic resonance imaging (MRI) is unable to differentiate tumor/nontumor enhancing tissues. We have applied delayed-contrast MRI for calculating high resolution treatment response assessment maps (TRAMs) clearly differentiating tumor/nontumor tissues in brain tumor patients.<h4>Methods</h4>One hundred and fifty patients with primary/metastatic tumors were recruited and scanned by delayed-contrast MRI and perfusion MRI. Of those, 47 patients underwent resection d ...[more]

PMID: 25452395

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Project description:Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.

Project description:To date, realistic models of how the central nervous system governs behavior have been restricted in scope to the brain, brainstem or spinal column, as if these existed as disembodied organs. Further, the model is often exercised in relation to an in vivo physiological experiment with input comprising an impulse, a periodic signal or constant activation, and output as a pattern of neural activity in one or more neural populations. Any link to behavior is inferred only indirectly via these activity patterns. We argue that to discover the principles of operation of neural systems, it is necessary to express their behavior in terms of physical movements of a realistic motor system, and to supply inputs that mimic sensory experience. To do this with confidence, we must connect our brain models to neuro-muscular models and provide relevant visual and proprioceptive feedback signals, thereby closing the loop of the simulation. This paper describes an effort to develop just such an integrated brain and biomechanical system using a number of pre-existing models. It describes a model of the saccadic oculomotor system incorporating a neuromuscular model of the eye and its six extraocular muscles. The position of the eye determines how illumination of a retinotopic input population projects information about the location of a saccade target into the system. A pre-existing saccadic burst generator model was incorporated into the system, which generated motoneuron activity patterns suitable for driving the biomechanical eye. The model was demonstrated to make accurate saccades to a target luminance under a set of environmental constraints. Challenges encountered in the development of this model showed the importance of this integrated modeling approach. Thus, we exposed shortcomings in individual model components which were only apparent when these were supplied with the more plausible inputs available in a closed loop design. Consequently we were able to suggest missing functionality which the system would require to reproduce more realistic behavior. The construction of such closed-loop animal models constitutes a new paradigm of computational neurobehavior and promises a more thoroughgoing approach to our understanding of the brain's function as a controller for movement and behavior.

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Delayed contrast extravasation MRI: a new paradigm in neuro-oncology.

Publications

Delayed contrast extravasation MRI: a new paradigm in neuro-oncology.

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