381 Glioma Three-Dimensional Shape Predicts Underlying Genetic Mutations

Abstract

INTRODUCTION: Phenotype is the detectable expression of genotype. Despite this, little is known about how the three-dimensional structure of brain tumors correlates with their genetic biomarkers. METHODS: The UCSF-PDGM dataset was filtered for histopathologically-proven gliomas. Segmentation masks of the enhancing tumor, non-enhancing/necrotic tumor, and surrounding FLAIR abnormality were obtained from each patient’s preoperative brain MRI. All tumors were evaluated for IDH mutations and 1p/19q codeletion; all grade III and IV tumors were tested for MGMT methylation status. The segmentation masks were processed to create topological features describing the tumor’s 3D shape. These features, without other clinical variables, were used in a custom machine learning pipeline to predict the presence of IDH mutations, 1p/19q codeletion, and MGMT methylation. RESULTS: After filtration, 103 of 494 gliomas tested had an IDH mutation, 29 of 494 had a 1p/19q codeletion, and 247 of 409 had methylation of MGMT. On the blinded test-subset, the machine learning model, using only features describing shape, rendered an AUROC of 0.902 (95% CI: 0.875-0.929), specificity of 90.3% (83.9%-96.6%), and sensitivity of 75.7% (68.0%-83.3%) for IDH mutation. For 1p/19q codeletion, we found an AUROC of 0.949 (0.908-0.990), specificity of 94.5% (84.3%-100%), and sensitivity of 86.6% (79.6%-93.6%). For MGMT methylation, the performance was poor with an AUROC of 0.445 (0.385-0.504). CONCLUSIONS: The three-dimensional shape of a glioma may be used to predict the presence of some key underlying genetic mutations before biopsy. Additional research is needed to validate this, improve its fidelity, and generalize it to other biomarkers.