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Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study

文献类型：期刊论文

作者She, Yunlang12; He, Bingxi9,10,11; Wang, Fang8; Zhong, Yifan12; Wang, Tingting7; Liu, Zhenchuan6; Yang, Minglei5; Yu, Bentong4; Deng, Jiajun12; Sun, Xiwen7
刊名EBIOMEDICINE
出版日期2022-12-01
卷号86页码:13
ISSN号2352-3964
关键词Deep learning Neoadjuvant chemoimmunotherapy Major pathological response Non-small cell lung cancer
DOI10.1016/j.ebiom.2022.104364
通讯作者Hu, Hongjie(hongjiehu@zju.edu.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Chen, Chang(chenthoracic@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Background This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction. Methods 274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data. Findings MPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58-0.86) and 0.72 (95% CI: 0.58-0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64-0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62-0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment. Interpretation The proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy. Copyright (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
WOS关键词GASTRIC-CANCER ; NSCLC PATIENTS ; SINGLE-ARM ; OPEN-LABEL ; CHEMOTHERAPY ; RADIOMICS
资助项目National Key Research and Development Program of China, China[2017YFA0205200] ; National Natural Science Foundation of China, China[91959126] ; National Natural Science Foundation of China, China[82022036] ; National Natural Science Foundation of China, China[91959130] ; National Natural Science Foundation of China, China[81971776] ; National Natural Science Foundation of China, China[81771924] ; National Natural Science Foundation of China, China[6202790004] ; National Natural Science Foundation of China, China[81930053] ; National Natural Science Foundation of China, China[9195910169] ; National Natural Science Foundation of China, China[62176013] ; National Natural Science Foundation of China, China[8210071009] ; Beijing Natural Science Foundation, China[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences, China[XDB38040200] ; Chinese Academy of Sciences, China[GJJSTD20170004] ; Chinese Academy of Sciences, China[QYZDJ-SSW-JSC005] ; Shanghai Hospital Development Center, China[SHDC2020CR3047B] ; Science and Technology Commission of Shanghai Municipality, China[21YF1438200]
WOS研究方向General & Internal Medicine ; Research & Experimental Medicine
语种英语
出版者ELSEVIER
WOS记录号WOS:000904359700005
资助机构National Key Research and Development Program of China, China ; National Natural Science Foundation of China, China ; Beijing Natural Science Foundation, China ; Strategic Priority Research Program of Chinese Academy of Sciences, China ; Chinese Academy of Sciences, China ; Shanghai Hospital Development Center, China ; Science and Technology Commission of Shanghai Municipality, China
源URL[http://ir.ia.ac.cn/handle/173211/51090]  
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, Hongjie; Dong, Di; Chen, Chang; Tian, Jie
作者单位1.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Pathol, Shanghai, Peoples R China
4.Nanchang Univ, Affiliated Hosp 1, Dept Thorac Surg, Nanchang, Jiangxi, Peoples R China
5.Chinese Acad Sci, Hwa Mei Hosp, Dept Thorac Surg, Ningbo, Zhejiang, Peoples R China
6.Tongji Univ, Shanghai Tongji Hosp, Sch Med, Dept Thorac Surg, Shanghai, Peoples R China
7.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Radiol, Shanghai, Peoples R China
8.Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Radiol, Hangzhou, Peoples R China
9.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
10.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 She, Yunlang,He, Bingxi,Wang, Fang,et al. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study[J]. EBIOMEDICINE,2022,86:13.
APA She, Yunlang.,He, Bingxi.,Wang, Fang.,Zhong, Yifan.,Wang, Tingting.,...&Tian, Jie.(2022).Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study.EBIOMEDICINE,86,13.
MLA She, Yunlang,et al."Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study".EBIOMEDICINE 86(2022):13.

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来源：自动化研究所

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