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Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis m...

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Autores principales: Ammari, Samy, Sallé de Chou, Raoul, Assi, Tarek, Touat, Mehdi, Chouzenoux, Emilie, Quillent, Arnaud, Limkin, Elaine, Dercle, Laurent, Hadchiti, Joya, Elhaik, Mickael, Moalla, Salma, Khettab, Mohamed, Balleyguier, Corinne, Lassau, Nathalie, Dumont, Sarah, Smolenschi, Cristina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305059/
https://www.ncbi.nlm.nih.gov/pubmed/34359346
http://dx.doi.org/10.3390/diagnostics11071263
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author Ammari, Samy
Sallé de Chou, Raoul
Assi, Tarek
Touat, Mehdi
Chouzenoux, Emilie
Quillent, Arnaud
Limkin, Elaine
Dercle, Laurent
Hadchiti, Joya
Elhaik, Mickael
Moalla, Salma
Khettab, Mohamed
Balleyguier, Corinne
Lassau, Nathalie
Dumont, Sarah
Smolenschi, Cristina
author_facet Ammari, Samy
Sallé de Chou, Raoul
Assi, Tarek
Touat, Mehdi
Chouzenoux, Emilie
Quillent, Arnaud
Limkin, Elaine
Dercle, Laurent
Hadchiti, Joya
Elhaik, Mickael
Moalla, Salma
Khettab, Mohamed
Balleyguier, Corinne
Lassau, Nathalie
Dumont, Sarah
Smolenschi, Cristina
author_sort Ammari, Samy
collection PubMed
description Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.
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spelling pubmed-83050592021-07-25 Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab Ammari, Samy Sallé de Chou, Raoul Assi, Tarek Touat, Mehdi Chouzenoux, Emilie Quillent, Arnaud Limkin, Elaine Dercle, Laurent Hadchiti, Joya Elhaik, Mickael Moalla, Salma Khettab, Mohamed Balleyguier, Corinne Lassau, Nathalie Dumont, Sarah Smolenschi, Cristina Diagnostics (Basel) Article Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients. MDPI 2021-07-14 /pmc/articles/PMC8305059/ /pubmed/34359346 http://dx.doi.org/10.3390/diagnostics11071263 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ammari, Samy
Sallé de Chou, Raoul
Assi, Tarek
Touat, Mehdi
Chouzenoux, Emilie
Quillent, Arnaud
Limkin, Elaine
Dercle, Laurent
Hadchiti, Joya
Elhaik, Mickael
Moalla, Salma
Khettab, Mohamed
Balleyguier, Corinne
Lassau, Nathalie
Dumont, Sarah
Smolenschi, Cristina
Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab
title Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab
title_full Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab
title_fullStr Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab
title_full_unstemmed Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab
title_short Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab
title_sort machine-learning-based radiomics mri model for survival prediction of recurrent glioblastomas treated with bevacizumab
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305059/
https://www.ncbi.nlm.nih.gov/pubmed/34359346
http://dx.doi.org/10.3390/diagnostics11071263
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