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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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Autores principales: Ammari, Samy, Sallé de Chou, Raoul, Balleyguier, Corinne, Chouzenoux, Emilie, Touat, Mehdi, Quillent, Arnaud, Dumont, Sarah, Bockel, Sophie, Garcia, Gabriel C. T. E., Elhaik, Mickael, Francois, Bidault, Borget, Valentin, Lassau, Nathalie, Khettab, Mohamed, Assi, Tarek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624566/
https://www.ncbi.nlm.nih.gov/pubmed/34829395
http://dx.doi.org/10.3390/diagnostics11112043
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author Ammari, Samy
Sallé de Chou, Raoul
Balleyguier, Corinne
Chouzenoux, Emilie
Touat, Mehdi
Quillent, Arnaud
Dumont, Sarah
Bockel, Sophie
Garcia, Gabriel C. T. E.
Elhaik, Mickael
Francois, Bidault
Borget, Valentin
Lassau, Nathalie
Khettab, Mohamed
Assi, Tarek
author_facet Ammari, Samy
Sallé de Chou, Raoul
Balleyguier, Corinne
Chouzenoux, Emilie
Touat, Mehdi
Quillent, Arnaud
Dumont, Sarah
Bockel, Sophie
Garcia, Gabriel C. T. E.
Elhaik, Mickael
Francois, Bidault
Borget, Valentin
Lassau, Nathalie
Khettab, Mohamed
Assi, Tarek
author_sort Ammari, Samy
collection PubMed
description 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 (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.
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spelling pubmed-86245662021-11-27 A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI Ammari, Samy Sallé de Chou, Raoul Balleyguier, Corinne Chouzenoux, Emilie Touat, Mehdi Quillent, Arnaud Dumont, Sarah Bockel, Sophie Garcia, Gabriel C. T. E. Elhaik, Mickael Francois, Bidault Borget, Valentin Lassau, Nathalie Khettab, Mohamed Assi, Tarek Diagnostics (Basel) Article 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 (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population. MDPI 2021-11-04 /pmc/articles/PMC8624566/ /pubmed/34829395 http://dx.doi.org/10.3390/diagnostics11112043 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
Balleyguier, Corinne
Chouzenoux, Emilie
Touat, Mehdi
Quillent, Arnaud
Dumont, Sarah
Bockel, Sophie
Garcia, Gabriel C. T. E.
Elhaik, Mickael
Francois, Bidault
Borget, Valentin
Lassau, Nathalie
Khettab, Mohamed
Assi, Tarek
A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
title A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
title_full A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
title_fullStr A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
title_full_unstemmed A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
title_short A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI
title_sort predictive clinical-radiomics nomogram for survival prediction of glioblastoma using mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624566/
https://www.ncbi.nlm.nih.gov/pubmed/34829395
http://dx.doi.org/10.3390/diagnostics11112043
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