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Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk
He, Bing-Xi1,2,3; Zhong, Yi-Fan4; Zhu, Yong-Bei1,2,3; Deng, Jia-Jun4; Fang, Meng-Jie3; She, Yun-Lang4; Wang, Ting-Ting5; Yang, Yang5; Sun, Xi-Wen5; Belluomini, Lorenzo6,7; Watanabe, Satoshi8; Dong, Di3,9; Tian, Jie1,2,3; Xie, Dong4
Source PublicationTRANSLATIONAL LUNG CANCER RESEARCH
ISSN2218-6751
2022-04-15
Pages23
Abstract

Background: Radiomics based on computed tomography (CT) images is potential in promoting individualized treatment of non-small cell lung cancer (NSCLC), however, its role in immunotherapy needs further exploration. The aim of this study was to develop a CT-based radiomics score to predict the efficacy of immune checkpoint inhibitor (ICI) monotherapy in patients with advanced NSCLC. Methods: Two hundred and thirty-six ICI-treated patients were retrospectively included and divided into a training cohort (n=188) and testing cohort (n=48) at a ratio of 8 to 2. The efficacy outcomes of ICI were evaluated based on overall survival (OS) and progression-free survival (PFS). We designed a survival network and combined it with a Cox regression model to obtain patients' OS risk score (OSRS) and PFS risk score (PFSRS). Results: Based on OSRS and PFSRS, patients were divided into high-and low-risk groups in the training cohort and the test cohort with distinctly different [training cohort, log-rank P<0.001, hazard ratio (HR): 4.14; test cohort, log-rank P=0.014, HR: 4.54] and PFS (training cohort, log-rank P<0.001, HR: 4.52; test cohort, log-rank P<0.001, HR: 6.64). Further joint evaluation of OSRS and PFSRS showed that both were significant in the Cox regression model (P<0.001), and multi-overall survival risk score (MOSRS) displayed more outstanding stratification capabilities than OSRS in both the training (P<0.001) and test cohorts (P=0.002). None of the clinical characteristics were significant in the Cox regression model, and the score that predicted the best immune response was not as good as the risk score from follow-up information in the performance of prognostic stratification. Conclusions: We developed a CT imaging-based score with the potential to become an independent prognostic factor to screen patients who would benefit from ICI treatment, which suggested that CT radiomics could be applied for individualized immunotherapy of NSCLC. Our findings should be further validated by future larger multicenter study.

KeywordTumor biomarkers immunotherapy lung neoplasms programmed cell death 1 receptor (PD-1 receptor) biostatistics
DOI10.21037/tlcr-22-244
WOS KeywordGASTRIC-CANCER ; RADIOMICS ; BLOCKADE ; DOCETAXEL ; NIVOLUMAB ; ANTIBODY ; PD-1
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[91959126] ; 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[9195910169] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Shanghai Municipal Health Commission[2018ZHYL0102] ; Tongji University AI Program[22120190216] ; Youth Innovation Promotion Association CAS[2017175] ; China Postdoctoral Science Foundation[2021M700341]
Funding OrganizationNational 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 ; Shanghai Municipal Health Commission ; Tongji University AI Program ; Youth Innovation Promotion Association CAS ; China Postdoctoral Science Foundation
WOS Research AreaOncology ; Respiratory System
WOS SubjectOncology ; Respiratory System
WOS IDWOS:000790593000001
PublisherAME PUBL CO
Sub direction classification医学影像处理与分析
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48417
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong, Di; Tian, Jie; Xie, Dong
Affiliation1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
2.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
4.Tongji Univ, Dept Thorac Surg, Shanghai Pulm Hosp, Sch Med, Shanghai, Peoples R China
5.Tongji Univ, Shanghai Pulm Hosp, Dept Radiol, Sch Med, Shanghai, Peoples R China
6.Univ Verona, Dept Med, Sect Oncol, Sch Med, Verona, Italy
7.Verona Univ Hosp Trust, Verona, Italy
8.Niigata Univ, Dept Resp Med & Infect Dis, Grad Sch Med & Dent Sci, Niigata, Japan
9.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
He, Bing-Xi,Zhong, Yi-Fan,Zhu, Yong-Bei,et al. Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk[J]. TRANSLATIONAL LUNG CANCER RESEARCH,2022:23.
APA He, Bing-Xi.,Zhong, Yi-Fan.,Zhu, Yong-Bei.,Deng, Jia-Jun.,Fang, Meng-Jie.,...&Xie, Dong.(2022).Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk.TRANSLATIONAL LUNG CANCER RESEARCH,23.
MLA He, Bing-Xi,et al."Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk".TRANSLATIONAL LUNG CANCER RESEARCH (2022):23.
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