MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

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

doi:10.1093/noajnl/vdad023

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Neurooncol Adv. 2023 Mar 5;5(1):vdad023. doi: 10.1093/noajnl/vdad023. eCollection 2023 Jan-Dec.

Authors

Satrajit Chakrabarty¹, Pamela LaMontagne², Joshua Shimony², Daniel S Marcus², Aristeidis Sotiras³

Affiliations

¹ Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.
² Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
³ Mallinckrodt Institute of Radiology and Institute for Informatics, Washington University School of Medicine, St. Louis, USA.

Abstract

Background: IDH mutation and 1p/19q codeletion status are important prognostic markers for glioma that are currently determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to noninvasively determine molecular alterations from MRI.

Methods: Pre-operative MRI scans of 2648 glioma patients were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774) datasets. A 2.5D hybrid convolutional neural network was proposed to simultaneously localize glioma and classify its molecular status by leveraging MRI imaging features and prior knowledge features from clinical records and tumor location. The models were trained on 223 and 348 cases for IDH and 1p/19q tasks, respectively, and tested on one internal (TCGA) and two external (WUSM and EGD) test sets.

Results: For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively.

Conclusions: The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform "virtual biopsy" for tailoring treatment planning and overall clinical management of gliomas.

Keywords: 1p/19q codeletion; deep learning; glioma; isocitrate dehydrogenase; overall survival.