Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients

Abstract Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess...

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Autores principales: Jing Yan, Bin Zhang, Shuaitong Zhang, Jingliang Cheng, Xianzhi Liu, Weiwei Wang, Yuhao Dong, Lu Zhang, Xiaokai Mo, Qiuying Chen, Jin Fang, Fei Wang, Jie Tian, Shuixing Zhang, Zhenyu Zhang
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Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/8adfc13b7d7e4796ad5cfc0874c1ed41
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spelling oai:doaj.org-article:8adfc13b7d7e4796ad5cfc0874c1ed412021-12-02T16:24:22ZQuantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients10.1038/s41698-021-00205-z2397-768Xhttps://doaj.org/article/8adfc13b7d7e4796ad5cfc0874c1ed412021-07-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00205-zhttps://doaj.org/toc/2397-768XAbstract Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.Jing YanBin ZhangShuaitong ZhangJingliang ChengXianzhi LiuWeiwei WangYuhao DongLu ZhangXiaokai MoQiuying ChenJin FangFei WangJie TianShuixing ZhangZhenyu ZhangNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
spellingShingle Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Jing Yan
Bin Zhang
Shuaitong Zhang
Jingliang Cheng
Xianzhi Liu
Weiwei Wang
Yuhao Dong
Lu Zhang
Xiaokai Mo
Qiuying Chen
Jin Fang
Fei Wang
Jie Tian
Shuixing Zhang
Zhenyu Zhang
Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
description Abstract Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.
format article
author Jing Yan
Bin Zhang
Shuaitong Zhang
Jingliang Cheng
Xianzhi Liu
Weiwei Wang
Yuhao Dong
Lu Zhang
Xiaokai Mo
Qiuying Chen
Jin Fang
Fei Wang
Jie Tian
Shuixing Zhang
Zhenyu Zhang
author_facet Jing Yan
Bin Zhang
Shuaitong Zhang
Jingliang Cheng
Xianzhi Liu
Weiwei Wang
Yuhao Dong
Lu Zhang
Xiaokai Mo
Qiuying Chen
Jin Fang
Fei Wang
Jie Tian
Shuixing Zhang
Zhenyu Zhang
author_sort Jing Yan
title Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
title_short Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
title_full Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
title_fullStr Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
title_full_unstemmed Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
title_sort quantitative mri-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/8adfc13b7d7e4796ad5cfc0874c1ed41
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