Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network

Satrajit Chakrabarty; Pamela LaMontagne; Joshua Shimony; Daniel S. Marcus; Aristeidis Sotiras

doi:10.1117/12.2651391

7 April 2023 Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network

Satrajit Chakrabarty, Pamela LaMontagne, Joshua Shimony, Daniel S. Marcus, Aristeidis Sotiras

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650W (2023) https://doi.org/10.1117/12.2651391
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Glioma is the most common form of brain tumor with a high degree of heterogeneity in imaging characteristics, treatment-response, and survival rate. An important factor causing this heterogeneity is the mutation of isocitrate dehydrogenase (IDH) enzyme. The current clinical gold-standard for identifying IDH mutation status involves invasive procedures that involve risk, may fail to capture intra-tumoral spatial heterogeneity or can be inaccessible in low-resource settings. In this study, we propose a deep learning-based method to non-invasively and preoperatively determine IDH status of high- and low-grade gliomas by leveraging their phenotypical characteristics from volumetric MRI scans. For this purpose, we propose a 3D Mask R-CNN-based approach to simultaneously detect and segment glioma as well as classify its IDH status - thus obviating the requirement of any separate tumor segmentation step. The network can operate on routinely acquired MRI sequences and is agnostic to glioma grade. It was trained on patient-cases from publicly available datasets (n = 223) and tested on two hold-out datasets acquired from The Cancer Genome Atlas (TCGA; n = 62) and Washington University School of Medicine (WUSM; n = 261). The model achieved areas under the receiver operating characteristic of 0.83 and 0.87, and areas under the precision-recall curves of 0.78 and 0.79, on the TCGA and WUSM sets, respectively. The model can be used to perform a pre-operative ‘virtual biopsy’ of gliomas, thus facilitating treatment planning, potentially leading to better overall survival.

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Citation Download Citation

Satrajit Chakrabarty, Pamela LaMontagne, Joshua Shimony, Daniel S. Marcus, and Aristeidis Sotiras "Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650W (7 April 2023); https://doi.org/10.1117/12.2651391

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