Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker | |
He, Bingxi1,2; Dong, Di2,3![]() ![]() | |
发表期刊 | JOURNAL FOR IMMUNOTHERAPY OF CANCER
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2020 | |
卷号 | 8期号:2页码:10 |
摘要 | Background Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC). Methods CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)). Results TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB. Conclusion By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC. |
关键词 | immunotherapy lung neoplasms tumor microenvironment biomarkers tumor biostatistics |
DOI | 10.1136/jitc-2020-000550 |
关键词[WOS] | PD-1 ; BLOCKADE ; SENSITIVITY ; THERAPY |
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[91959126] ; 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[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority CAS Project[XDB38040200] ; 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] ; Shanghai Municipal Health Commission[2018ZHYL0102] ; Shanghai Municipal Health Commission[2019SY072] ; Clinical Research Foundation of Shanghai Pulmonary Hospital[FK1943] |
项目资助者 | National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority CAS Project ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Shanghai Municipal Health Commission ; Clinical Research Foundation of Shanghai Pulmonary Hospital |
WOS研究方向 | Oncology ; Immunology |
WOS类目 | Oncology ; Immunology |
WOS记录号 | WOS:000552677200003 |
出版者 | BMJ PUBLISHING GROUP |
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/173211/40199 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Huang, Zhipei; Jiang, Tao; Tian, Jie; Chen, Chang |
作者单位 | 1.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Tongji Univ, Dept Thorac Surg, Shanghai Pulm Hosp, Shanghai, Peoples R China 5.Tongji Univ, Dept Med Oncol, Shanghai Pulm Hosp, Shanghai, Peoples R China 6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China 7.Beijing Genecast Biotechnol Co, Dept Med, Beijing, Peoples R China 8.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Peoples R China 9.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, 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, Bingxi,Dong, Di,She, Yunlang,et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker[J]. JOURNAL FOR IMMUNOTHERAPY OF CANCER,2020,8(2):10. |
APA | He, Bingxi.,Dong, Di.,She, Yunlang.,Zhou, Caicun.,Fang, Mengjie.,...&Chen, Chang.(2020).Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker.JOURNAL FOR IMMUNOTHERAPY OF CANCER,8(2),10. |
MLA | He, Bingxi,et al."Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker".JOURNAL FOR IMMUNOTHERAPY OF CANCER 8.2(2020):10. |
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