Case study brain tumor

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ScienceSoft has developed a solution for the healthcare industry aimed at improving automated brain cancer diagnostics by applying convolutional neural network CNN algorithms. The automated medical diagnostics application was to analyze uploaded brain MRI scans and mark the scanned area with the segmented tumor, including each tissue type defined — normal tissue, edema, nonenhancing core, necrotic core and enhancing core. The project stages included building a specific CNN structure; preparing training and testing datasets; training and testing the CNN, and evaluating accuracy. To evaluate CNN performance, the team compared acquired results with the ground truth. The ground truth was taken from BRATS imaging datasets, which have been segmented and annotated manually by one to four raters as well as approved by neuro-radiologists.
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The patient was progression-free for 23 mo. Fresh primary and recurrent tumor samples were collected and processed for patient-derived model development. Whole-genome sequencing WGS was performed concurrently with additional standard of care diagnostics. WGS revealed a hypermutated genotype in the germline tissue and in both the primary and recurrent tumor samples.
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Oligodendrogliomas are a rare primary central nervous system tumor with an estimated 1, people diagnosed each year. To improve treatment for patients with oligodendrogliomas, Elizabeth B. Claus, M.
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This information allows for cells to distinguish each other apart, as well as form a connection with synapses. However, with too much Glutamate it can be toxic to the brain causing over excitation of the cell and causing them to die. As this disease progresses, symptoms change and vary in degrees. In the late stages, patients will need extensive help with activities of daily living and round the clock supervision.
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