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

doi:10.1016/j.ebiom.2022.104364

	Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study
	She, Yunlang 1; He, Bingxi 2,3,4; Wang, Fang 5; Zhong, Yifan 1; Wang, Tingting 6; Liu, Zhenchuan 7; Yang, Minglei 8; Yu, Bentong 9; Deng, Jiajun 1; Sun, Xiwen 6; Wu, Chunyan 10; Hou, Likun 10; Zhu, Yuming 6; Yang, Yang6 ; Hu, Hongjie 5; Dong, Di4,11 ; Chen, Chang 1; Tian, Jie 2,3,4,12
发表期刊	EBIOMEDICINE
ISSN	2352-3964
	2022-12-01
卷号	86 页码:13
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)
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/).
关键词	Deep learning Neoadjuvant chemoimmunotherapy Major pathological response Non-small cell lung cancer
DOI	10.1016/j.ebiom.2022.104364
关键词[WOS]	GASTRIC-CANCER ; NSCLC PATIENTS ; SINGLE-ARM ; OPEN-LABEL ; CHEMOTHERAPY ; RADIOMICS
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	General & Internal Medicine ; Research & Experimental Medicine
WOS类目	Medicine, General & Internal ; Medicine, Research & Experimental
WOS记录号	WOS:000904359700005
出版者	ELSEVIER
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51091
专题	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.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Thorac Surg, Shanghai, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China 3.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 5.Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Radiol, Hangzhou, Peoples R China 6.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Radiol, Shanghai, Peoples R China 7.Tongji Univ, Shanghai Tongji Hosp, Sch Med, Dept Thorac Surg, Shanghai, Peoples R China 8.Chinese Acad Sci, Hwa Mei Hosp, Dept Thorac Surg, Ningbo, Zhejiang, Peoples R China 9.Nanchang Univ, Affiliated Hosp 1, Dept Thorac Surg, Nanchang, Jiangxi, Peoples R China 10.Tongji Univ, Shanghai Pulm Hosp, Sch Med, Dept Pathol, Shanghai, Peoples R China 11.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 12.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China
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	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.