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

<h4>Background and objective</h4>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...

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Autors principals:	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
Format:	Artigo
Idioma:	Inglês
Publicat:	Public Library of Science (PLoS) 2024-01-01
Col·lecció:	PLoS ONE
Accés en línia:	https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0299267&type=printable
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