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Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images | |
Tian, Panwen1; He, Bingxi2; Dong, Di3![]() | |
发表期刊 | Theranostics
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ISSN | 1838-7640 |
2020 | |
卷号 | 0期号:0页码:0 |
通讯作者 | Huang, Zhipei(zhphuang@ucas.ac.cn) ; Dong, Di(di.dong@ia.ac.cn) ; Li, Weimin(weimin0034@163.com) |
文章类型 | article |
摘要 | Purpose: This study aimed to use computed tomography (CT) images to assess PD-L1 expression in nonsmall cell lung cancer (NSCLC) and predict response to immunotherapy. Methods: We retrospectively analyzed a PD-L1 expression dataset that consisted of 939 consecutive stage IIIB-IV NSCLC patients with pretreatment CT images. A deep convolutional neural network was trained and optimized with CT images from the training cohort (n=750) and validation cohort (n=96) to obtain a PD-L1 expression signature (PDL1ES), which was evaluated using the test cohort (n=93). Finally, a separate immunotherapy cohort (n=94) was used to assess the prognostic value of PDL1ES with respect to clinical outcome. Results: PDL1ES was able to predict high PD-L1 expression (PD-L1 ≥ 50%) with areas under the receiver operating characteristic curve (AUC) of 0.78 (95% confidence interval (CI): 0.75~0.80), 0.71 (95% CI: 0.60~0.81), and 0.76 (95% CI: 0.67~0.85) in the training, validation, and test cohorts, respectively. In patients treated with anti-PD-1 antibody, low PDL1ES was associated with improved progression-free survival (PFS) (median PFS 363 days in low score group vs 183 days in high score group; hazard ratio [HR]: 2.57, 95% CI: 1.22~5.44; P = 0.010). Additionally, when PDL1ES was combined with a clinical model that was trained using age, sex, smoking history and family history of malignancy, the response to immunotherapy (P<0.001) could be better predicted compared to either PDL1ES or the clinical model alone. Conclusions: The deep learning model provides a noninvasive method to predict high PD-L1 expression of NSCLC and to infer clinical outcomes in response to immunotherapy. Additionally, this deep learning model combined with clinical models demonstrated improved stratification capabilities. |
关键词 | PD-L1 expression deep learning computed tomography immunotherapy non-small cell lung cancer. |
DOI | accepted |
关键词[WOS] | DIAGNOSIS ; BLOCKADE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[91859203] ; National Natural Science Foundation of China[82072598] ; National Natural Science Foundation of China[81871890] ; 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[81930053] ; National Key R&D Program of China[2017YFC0910004] ; 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[2017ZX10103004012] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Project of High-Level Talents Team Introduction in Zhuhai City[Zhuhai HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175] ; International scientific and technological innovation cooperation of Sichuan Province[2018HH0161] |
项目资助者 | National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City ; Youth Innovation Promotion Association CAS ; International scientific and technological innovation cooperation of Sichuan Province |
WOS研究方向 | Research & Experimental Medicine |
WOS类目 | Medicine, Research & Experimental |
WOS记录号 | WOS:000600556000008 |
出版者 | IVYSPRING INT PUBL |
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/173211/40688 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Huang, Zhipei; Li, Weimin; Tian, Jie |
作者单位 | 1.Department of Respiratory and Critical Care Medicine, Lung Cancer Treatment Centre, West China Hospital, West China Hospital, Sichuan University, Sichuan, China 2.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China 3.CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China 4.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 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 | Tian, Panwen,He, Bingxi,Dong, Di,et al. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images[J]. Theranostics,2020,0(0):0. |
APA | Tian, Panwen.,He, Bingxi.,Dong, Di.,Mu, Wei.,Liu, Kunqin.,...&Tian, Jie.(2020).Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.Theranostics,0(0),0. |
MLA | Tian, Panwen,et al."Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images".Theranostics 0.0(2020):0. |
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