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Glioblastoma (GBM) is one of the most aggressive and lethal human cancers.Intra-tumoral genetic heterogeneity poses a significant challenge fortreatment. Biopsy is invasive, which motivates the development of non-invasive,MRI-based machine learning (ML) models to quantify intra-tumoral geneticheterogeneity for each patient. This capability holds great promise forenabling better therapeutic selection to improve patient outcomes. We proposeda novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predictregional genetic alteration status within each GBM tumor using MRI. WSO-SVM wasapplied to a unique dataset of 318 image-localized biopsies with spatiallymatched multiparametric MRI from 74 GBM patients. The model was trained topredict the regional genetic alteration of three GBM driver genes (EGFR,PDGFRA, and PTEN) based on features extracted from the corresponding region offive MRI contrast images. For comparison, a variety of existing ML algorithmswere also applied. The classification accuracy of each gene was comparedbetween the different algorithms. The SHapley Additive exPlanations (SHAP)method was further applied to compute contribution scores of different contrastimages. Finally, the trained WSO-SVM was used to generate prediction mapswithin the tumoral area of each patient to help visualize the intra-tumoralgenetic heterogeneity. This study demonstrated the feasibility of using MRI andWSO-SVM to enable non-invasive prediction of intra-tumoral regional geneticalteration for each GBM patient, which can inform future adaptive therapies forindividualized oncology.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm