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A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study

文献类型：期刊论文

作者Zhong, Lianzhen1,2; Dong, Di1,2; Fang, Xueliang3; Zhang, Fan4,5; Zhang, Ning6; Zhang, Liwen1,2; Fang, Mengjie1,2; Jiang, Wei7; Liang, Shaobo8; Li, Cong1,2
刊名EBIOMEDICINE
出版日期2021-08-01
卷号70页码:10
ISSN号2352-3964
关键词Multi-task deep learning Radiomic nomogram Survival analysis Treatment decision Advanced nasopharyngeal carcinoma
DOI10.1016/j.ebiom.2021.103522
通讯作者Hu, Zhenhua(zhenhua.hu@ia.ac.cn) ; Ma, Jun(majun2@mail.sysu.edu.cn) ; Tang, Linglong(tangll@sysucc.org.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Background: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal in clinical practice. We aimed to develop a deep learning-based model for treatment decision in NPC. Methods: A total of 1872 patients with stage T3N1M0 NPC were enrolled from four Chinese centres and received either ICT+CCRT or CCRT. A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended. Model performance was assessed by the concordance index (C-index) and the Kaplan-Meier estimator. Findings: The nomogram showed excellent prognostic ability for disease-free survival in both the CCRT (Cindex range: 0.888-0.921) and ICT+CCRT (C-index range: 0.784-0.830) groups. According to the prognostic difference between treatments using the nomogram, patients were divided into the ICT-preferred and CCRT-preferred groups. In the ICT-preferred group, patients receiving ICT+CCRT exhibited prolonged survival over those receiving CCRT in the internal and external test cohorts (hazard ratio [HR]: 0.17, p <0.001 and 0.24, p=0.02); while the trend was opposite in the CCRT-preferred group (HR: 6.24, p<0.001 and 12.08, p<0.001). Similar results for treatment decision using the nomogram were obtained in different subgroups stratified by clinical factors and MR acquisition parameters. Interpretation: Our nomogram could predict the prognosis of T3N1M0 NPC patients with different treatment regimens and accordingly recommend an optimal treatment regimen, which may serve as a potential tool for promoting personalized treatment of NPC. (C) 2021 The Authors. Published by Elsevier B.V.
WOS关键词DISTANT METASTASIS ; GASTRIC-CANCER ; PREDICT ; VALIDATION ; SIGNATURE ; SURVIVAL
资助项目National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[6202790004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81671759] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[JQ19027] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSWJSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703 Zhuhai] ; Beijing Nova Program[Z181100006218046] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向General & Internal Medicine ; Research & Experimental Medicine
语种英语
出版者ELSEVIER
WOS记录号WOS:000689248200007
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City ; Beijing Nova Program ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/45875]  
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Hu, Zhenhua; Ma, Jun; Tang, Linglong; Tian, Jie
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Beijing Key Lab Mol Imagi, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Sun Yat Sen Univ, Dept Radiat Oncol,Canc Ctr, State Key Lab Oncol South China,Collaborat Innova, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Guangzhou 510060, Guangdong, Peoples R China
4.Sun Yat Sen Univ, Affiliated Hosp 5, Dept Head & Neck Oncol, Canc Ctr, Zhuhai 519000, Guangdong, Peoples R China
5.Sun Yat Sen Univ, Affiliated Hosp 5, Guangdong Prov Key Lab Biomed Imaging, Zhuhai 519000, Guangdong, Peoples R China
6.Sun Yat Sen Univ, Dept Radiat Oncol, Peoples Hosp Foshan 1, Foshan 528000, Guangdong, Peoples R China
7.Guilin Med Univ, Dept Radiat Oncol, Affiliated Hosp, Guilin 541000, Guangxi Provinc, Peoples R China
8.Sun Yat Sen Univ, Dept Radiat Oncol, Affiliated Hosp 3, Guangzhou 510000, Guangdong, Peoples R China
9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714		 Zhong, Lianzhen,Dong, Di,Fang, Xueliang,et al. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study[J]. EBIOMEDICINE,2021,70:10.
APA Zhong, Lianzhen.,Dong, Di.,Fang, Xueliang.,Zhang, Fan.,Zhang, Ning.,...&Tian, Jie.(2021).A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study.EBIOMEDICINE,70,10.
MLA Zhong, Lianzhen,et al."A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study".EBIOMEDICINE 70(2021):10.

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来源：自动化研究所

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