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Abstract #0023

Predictive and discriminative localization of IDH genotype in high grade gliomas using deep convolutional neural nets

Adnan Ahmad1, Srinjay Sarkar1, Apurva Shah1, Santosh Vani2, Jitender Saini3, and Madhura Ingalhalikar1

1Symbiosis Centre of Medical Image Analysis, Symbiosis International University, Pune, India, 2National Institute of Mental Health and Neurosciences, Bangalore, India, 3Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India

Radiomics and state-of-art convolutional neural networks (CNNs) have demonstrated their usefulness for predicting genotype in gliomas from brain MRI. However, these techniques rely on accurate tumor segmentation and do not facilitate insights into the critical discrimative features. To mitigate this, we employ a novel technique called CNNs with discriminative localization (DL-CNN) on a clinical T2 weighted MRI dataset of IDH1 mutant and wild-type tumor patients, which is not only free of tumor segmentation with high classification accuracy of 86.7% but also demonstrates that the tumoral area is discriminative in mutants while in IDH1 wildtype the peri-tumoral edema is also involved.

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