Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward
  precision medicine using MRI and a data-inclusive machine learning algorithm
Lujia Wang Hairong Wang Fulvio D'Angelo Lee Curtin Christopher P. Sereduk Gustavo De Leon Kyle W. Singleton Javier Urcuyo Andrea Hawkins-Daarud Pamela R. Jackson Chandan Krishna Richard S. Zimmerman Devi P. Patra Bernard R. Bendok Kris A. Smith Peter Nakaji Kliment Donev Leslie C. Baxter Maciej M. Mrugała Michele Ceccarelli Antonio Iavarone Kristin R. Swanson Nhan L. Tran Leland S. Hu Jing Li
Glioblastoma (GBM) is one of the most aggressive and lethal human cancers.
Intra-tumoral genetic heterogeneity poses a significant challenge for
treatment. Biopsy is invasive, which motivates the development of non-invasive,
MRI-based machine learning (ML) models to quantify intra-tumoral genetic
heterogeneity for each patient. This capability holds great promise for
enabling better therapeutic selection to improve patient outcomes. We proposed
a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict
regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was
applied to a unique dataset of 318 image-localized biopsies with spatially
matched multiparametric MRI from 74 GBM patients. The model was trained to
predict the regional genetic alteration of three GBM driver genes (EGFR,
PDGFRA, and PTEN) based on features extracted from the corresponding region of
five MRI contrast images. For comparison, a variety of existing ML algorithms
were also applied. The classification accuracy of each gene was compared
between the different algorithms. The SHapley Additive exPlanations (SHAP)
method was further applied to compute contribution scores of different contrast
images. Finally, the trained WSO-SVM was used to generate prediction maps
within the tumoral area of each patient to help visualize the intra-tumoral
genetic heterogeneity. This study demonstrated the feasibility of using MRI and
WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic
alteration for each GBM patient, which can inform future adaptive therapies for
individualized oncology.