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

文献类型：期刊论文

作者Yan, Jing1; Zhang, Bin2; Zhang, Shuaitong3,4,5; Cheng, Jingliang1; Liu, Xianzhi6; Wang, Weiwei7; Dong, Yuhao8; Zhang, Lu; Mo, Xiaokai2; Chen, Qiuying2
刊名NPJ PRECISION ONCOLOGY
出版日期2021-07-26
卷号5期号:1页码:9
DOI10.1038/s41698-021-00205-z
通讯作者Tian, Jie(jie.tian@ia.ac.cn) ; Zhang, Shuixing(shui7515@126.com) ; Zhang, Zhenyu(fcczhangzy1@zzu.edu.cn)
英文摘要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.
WOS关键词GRADE GLIOMAS ; MUTATIONS ; OPTIMIZATION ; REGISTRATION ; 1P/19Q ; ROBUST ; IDH
资助项目National Natural Science Foundation of China[81571664] ; National Natural Science Foundation of China[81871323] ; National Natural Science Foundation of China[81801665] ; National Natural Science Foundation of China[81702465] ; National Natural Science Foundation of China[U1804172] ; National Natural Science Foundation of Guangdong Province[2018B030311024] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201707010328] ; China Postdoctoral Science Foundation[2016M600145] ; Science and Technology Program of Henan Province[192102310123] ; Science and Technology Program of Henan Province[182102310113] ; Science and Technology Program of Henan Province[192102310050] ; Youth Innovation Fund of The First Affiliated Hospital of Zhengzhou University ; Key Research Projects of Henan Higher Education[18A320077] ; Key Program of Medical Science and Technique Foundation of Henan Province[SBGJ202002062] ; Joint Construction Program of Medical Science and Technique Foundation of Henan Province[LHGJ20190156]
WOS研究方向Oncology
语种英语
出版者NATURE RESEARCH
WOS记录号WOS:000677834000001
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of Guangdong Province ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission ; China Postdoctoral Science Foundation ; Science and Technology Program of Henan Province ; Youth Innovation Fund of The First Affiliated Hospital of Zhengzhou University ; Key Research Projects of Henan Higher Education ; Key Program of Medical Science and Technique Foundation of Henan Province ; Joint Construction Program of Medical Science and Technique Foundation of Henan Province
源URL[http://ir.ia.ac.cn/handle/173211/45566]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Zhang, Shuixing; Zhang, Zhenyu
作者单位1.Zhengzhou Univ, Affiliated Hosp 1, Dept MRI, Zhengzhou, Peoples R China
2.Jinan Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou, Guangdong, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
5.Beihang Univ, Sch Engn Med, Beijing, Peoples R China
6.Zhengzhou Univ, Affiliated Hosp 1, Dept Neurosurg, Zhengzhou, Peoples R China
7.Zhengzhou Univ, Affiliated Hosp 1, Dept Pathol, Zhengzhou, Peoples R China
8.Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Guangdong Cardiovasc Inst, Dept Catheterizat Lab,Guangdong Prov Key Lab Sout, Guangzhou, Guangdong, Peoples R China
9.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shanxi, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Jing,Zhang, Bin,Zhang, Shuaitong,et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients[J]. NPJ PRECISION ONCOLOGY,2021,5(1):9.
APA Yan, Jing.,Zhang, Bin.,Zhang, Shuaitong.,Cheng, Jingliang.,Liu, Xianzhi.,...&Zhang, Zhenyu.(2021).Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.NPJ PRECISION ONCOLOGY,5(1),9.
MLA Yan, Jing,et al."Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients".NPJ PRECISION ONCOLOGY 5.1(2021):9.

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来源：自动化研究所

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