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
BACKGROUND AND OBJECTIVE: 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 outcome. METHODS: We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI...
2024: PloS One