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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MDPI AG
2021
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oai:doaj.org-article:3428d18c49c14d4880c655f172cab8632021-11-25T17:21:09ZA Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI10.3390/diagnostics111120432075-4418https://doaj.org/article/3428d18c49c14d4880c655f172cab8632021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2043https://doaj.org/toc/2075-4418Glioblastoma (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 <i>p</i> = 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.Samy AmmariRaoul Sallé de ChouCorinne BalleyguierEmilie ChouzenouxMehdi TouatArnaud QuillentSarah DumontSophie BockelGabriel C. T. E. GarciaMickael ElhaikBidault FrancoisValentin BorgetNathalie LassauMohamed KhettabTarek AssiMDPI AGarticleglioblastomabiomarkerradiomicsmachine learningMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2043, p 2043 (2021) |
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EN |
| topic |
glioblastoma biomarker radiomics machine learning Medicine (General) R5-920 |
| spellingShingle |
glioblastoma biomarker radiomics machine learning Medicine (General) R5-920 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 A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI |
| 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 <i>p</i> = 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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Samy Ammari |
| title |
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_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_sort |
predictive clinical-radiomics nomogram for survival prediction of glioblastoma using mri |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/3428d18c49c14d4880c655f172cab863 |
| work_keys_str_mv |
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| _version_ |
1718412462016954368 |