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

• Published in: PloS one Vol. 19; no. 4; p. e0299267
• Main Authors: Wang, Lujia, Wang, Hairong, D’Angelo, Fulvio, Curtin, Lee, Sereduk, Christopher P., Leon, Gustavo De, Singleton, Kyle W., Urcuyo, Javier, Hawkins-Daarud, Andrea, Jackson, Pamela R., Krishna, Chandan, Zimmerman, Richard S., Patra, Devi P., Bendok, Bernard R., Smith, Kris A., Nakaji, Peter, Donev, Kliment, Baxter, Leslie C., Mrugała, Maciej M., Ceccarelli, Michele, Iavarone, Antonio, Swanson, Kristin R., Tran, Nhan L., Hu, Leland S., Li, Jing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.04.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Care and treatment; Data mining; Diagnosis; ErbB Receptors - genetics; Genetic aspects; Genetic Heterogeneity; Genetic research; Glioblastoma - diagnostic imaging; Glioblastoma - genetics; Glioblastoma - pathology; Glioblastoma multiforme; Health aspects; Humans; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine and Health Sciences; Physical Sciences; Platelet-derived growth factor; Precision Medicine; Research and Analysis Methods; Support Vector Machine; United States

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 he...