Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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![]() ![]() | |
发表期刊 | TRANSLATIONAL LUNG CANCER RESEARCH
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ISSN | 2218-6751 |
2022-04-15 | |
页码 | 23 |
摘要 | 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. |
关键词 | Tumor biomarkers immunotherapy lung neoplasms programmed cell death 1 receptor (PD-1 receptor) biostatistics |
DOI | 10.21037/tlcr-22-244 |
关键词[WOS] | GASTRIC-CANCER ; RADIOMICS ; BLOCKADE ; DOCETAXEL ; NIVOLUMAB ; ANTIBODY ; PD-1 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National 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] |
项目资助者 | 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 ; Shanghai Municipal Health Commission ; Tongji University AI Program ; Youth Innovation Promotion Association CAS ; China Postdoctoral Science Foundation |
WOS研究方向 | Oncology ; Respiratory System |
WOS类目 | Oncology ; Respiratory System |
WOS记录号 | WOS:000790593000001 |
出版者 | AME PUBL CO |
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/173211/48417 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Dong, Di; Tian, Jie; Xie, Dong |
作者单位 | 1.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 |
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 | 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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