A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning...

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Auteurs principaux: Samy Ammari, Raoul Sallé de Chou, Corinne Balleyguier, Emilie Chouzenoux, Mehdi Touat, Arnaud Quillent, Sarah Dumont, Sophie Bockel, Gabriel C. T. E. Garcia, Mickael Elhaik, Bidault Francois, Valentin Borget, Nathalie Lassau, Mohamed Khettab, Tarek Assi
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
glioblastoma
biomarker
radiomics
machine learning
Medicine (General)
R5-920
Accès en ligne:https://doaj.org/article/3428d18c49c14d4880c655f172cab863
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https://doaj.org/article/3428d18c49c14d4880c655f172cab863

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