Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study | |
Zhang, Liwen1![]() ![]() ![]() ![]() | |
发表期刊 | Radiotherapy and oncology
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ISSN | 0167-8140 |
2020 | |
卷号 | 150期号:1页码:73-80 |
通讯作者 | Liu, Zaiyi(zyliu@163.com) ; Wang, Rongpin(wangrongpin@126.com) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) |
摘要 | Background and purpose: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. Materials and methods: We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n=518) and an external validation cohort (center 3, n=122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. Results: Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P-value<0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P-value<0.001, C-index:0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P-value<0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics=0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). Conclusion: The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients. |
关键词 | Gastric Cancer |
DOI | 10.1016/j.radonc.2020.06.010 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0700401] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81772006] ; National Natural Science Foundation of China[81771912] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81960314] ; National Science Fund for Distinguished Young Scholars[81925023] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175] ; Technology Foundation of Guizhou Province[QKHJC [2016]1096] |
项目资助者 | National Key R&D Program of China ; National Natural Science Foundation of China ; National Science Fund for Distinguished Young Scholars ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Technology Foundation of Guizhou Province |
WOS研究方向 | Oncology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Oncology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000629605200016 |
出版者 | ELSEVIER IRELAND LTD |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40684 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Liu, Zaiyi; Wang, Rongpin; Zhou, Junlin; Tian, Jie |
作者单位 | 1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 2.Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China 3.Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang 550002, China 4.Department of Radiology, Guangdong General Hospital, Guangzhou 510080, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang, Liwen,Dong, Di,Zhang, Wenjuan,et al. A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study[J]. Radiotherapy and oncology,2020,150(1):73-80. |
APA | Zhang, Liwen.,Dong, Di.,Zhang, Wenjuan.,Hao, Xiaohan.,Fang, Mengjie.,...&Tian, Jie.(2020).A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study.Radiotherapy and oncology,150(1),73-80. |
MLA | Zhang, Liwen,et al."A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study".Radiotherapy and oncology 150.1(2020):73-80. |
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