Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI

PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and MethodsA t...

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Autores principales:	Bin Wang, Shan Zhang, Xubin Wu, Ying Li, Yueming Yan, Lili Liu, Jie Xiang, Dandan Li, Ting Yan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	multiparametric MRI multi-survival indicators glioblastoma machine learning radiomics analysis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/fc170b6c99a64caf8dd12230e4f11d99
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spelling	oai:doaj.org-article:fc170b6c99a64caf8dd12230e4f11d992021-11-30T23:29:09ZMultiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI2234-943X10.3389/fonc.2021.778627https://doaj.org/article/fc170b6c99a64caf8dd12230e4f11d992021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.778627/fullhttps://doaj.org/toc/2234-943XPurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and MethodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.Bin WangShan ZhangXubin WuYing LiYueming YanLili LiuJie XiangDandan LiTing YanFrontiers Media S.A.articlemultiparametric MRImulti-survival indicatorsglioblastomamachine learningradiomics analysisNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multiparametric MRI multi-survival indicators glioblastoma machine learning radiomics analysis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	multiparametric MRI multi-survival indicators glioblastoma machine learning radiomics analysis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Bin Wang Shan Zhang Xubin Wu Ying Li Yueming Yan Lili Liu Jie Xiang Dandan Li Ting Yan Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
description	PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and MethodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.
format	article
author	Bin Wang Shan Zhang Xubin Wu Ying Li Yueming Yan Lili Liu Jie Xiang Dandan Li Ting Yan
author_facet	Bin Wang Shan Zhang Xubin Wu Ying Li Yueming Yan Lili Liu Jie Xiang Dandan Li Ting Yan
author_sort	Bin Wang
title	Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
title_short	Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
title_full	Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
title_fullStr	Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
title_full_unstemmed	Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
title_sort	multiple survival outcome prediction of glioblastoma patients based on multiparametric mri
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fc170b6c99a64caf8dd12230e4f11d99
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_version_	1718406239779553280

Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI

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