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Abstract #1912

Validating multimodal MRI based stratification of IDH genotype using radiomics and CNNs

Madhura Ingalhalikar1, Tanay Chougule1, Sumeet Shinde1, Vani Santosh2, and Jitender Saini3
1Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, India, 2Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore, India, 3Department of Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India

Radiomics based multi-variate models and state-of-art convolutional neural networks (CNNs) have demonstrated their usefulness for predicting IDH genotype in gliomas from MRI images. However, adaptability and clinical explanability of these models on unseen multi-center datasets has not been investigated. Our work trains radiomics and CNN based classifiers on a large dataset (TCIA) and tests multiple local datasets. Results demonstrate higher adaptability of radiomics than standard CNNs, except for transfer learned CNNs. Better interpretability was obtained from feature ranking (in case of radiomics) and high resolution class activation maps (in case of CNNs).

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