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A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study

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收录情况： ◇ SCIE

作者：
Deng Kexue[1] ; Wang Lu[2,3] ; Liu Yuchan[1] ; Li Xin[4] ; Hou Qiuyang[1] ; Cao Mulan[4] ; Ng Nathan Norton[5] ; Wang Huan[6] ; Chen Huanhuan[7] ; Yeom Kristen W[5] ; Zhao Mingfang[4] ; Wu Ning[8] ; Gao Peng[9] ; Shi Jingyun[10] ; Liu Zaiyi[11,12] ; Li Weimin[13] ; Tian Jie[14,15,16,17] ; Song Jiangdian[3,*1] ;
机构： [1]Department of radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, China. [2]Library of Shengjing Hospital of China Medical University, Shenyang, China. [3]School of Health Management, China Medical University, Shenyang, Liaoning, China. [4]Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China. [5]Department of Radiology, School of Medicine Stanford University, Stanford CA 94305, United States. [6]Radiation oncology department of thoracic cancer, Liaoning Cancer Hospital and Institute, Liaoning, China. [7]Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China. [8]PET-CT center, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. [9]Department of Surgical Oncology and General Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China. [10]Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. [11]Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China. [12]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China. [13]Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, Sichuan, China. [14]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China. [15]Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China. [16]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China. [17]CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Beijing, China.
出处：
DOI： 10.1016/j.eclinm.2022.101541
ISSN：

摘要：
For clinical decision making, it is crucial to identify patients with stage IV non-small cell lung cancer (NSCLC) who may benefit from tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). In this study, a deep learning-based system was designed and validated using pre-therapy computed tomography (CT) images to predict the survival benefits of EGFR-TKIs and ICIs in stage IV NSCLC patients.This retrospective study collected data from 570 patients with stage IV EGFR-mutant NSCLC treated with EGFR-TKIs at five institutions between 2010 and 2021 (data of 314 patients were from a previously registered study), and 129 patients with stage IV NSCLC treated with ICIs at three institutions between 2017 and 2021 to build the ICI test dataset. Five-fold cross-validation was applied to divide the EGFR-TKI-treated patients from four institutions into training and internal validation datasets randomly in a ratio of 80%:20%, and the data from another institution was used as an external test dataset. An EfficientNetV2-based survival benefit prognosis (ESBP) system was developed with pre-therapy CT images as the input and the probability score as the output to identify which patients would receive additional survival benefit longer than the median PFS. Its prognostic performance was validated on the ICI test dataset. For diagnosing which patient would receive additional survival benefit, the accuracy of ESBP was compared with the estimations of three radiologists and three oncologists with varying degrees of expertise (two, five, and ten years). Improvements in the clinicians' diagnostic accuracy with ESBP assistance were then quantified.ESBP achieved positive predictive values of 80·40%, 75·40%, and 77·43% for additional EGFR-TKI survival benefit prediction using the probability score of 0·2 as the threshold on the training, internal validation, and external test datasets, respectively. The higher ESBP score (>0·2) indicated a better prognosis for progression-free survival (hazard ratio: 0·36, 95% CI: 0·19-0·68, p<0·0001) in patients on the external test dataset. Patients with scores >0·2 in the ICI test dataset also showed better survival benefit (hazard ratio: 0·33, 95% CI: 0·18-0·55, p<0·0001). This suggests the potential of ESBP to identify the two subgroups of benefiting patients by decoding the commonalities from pre-therapy CT images (stage IV EGFR-mutant NSCLC patients receiving additional survival benefit from EGFR-TKIs and stage IV NSCLC patients receiving additional survival benefit from ICIs). ESBP assistance improved the diagnostic accuracy of the clinicians with two years of experience from 47·91% to 66·32%, and the clinicians with five years of experience from 53·12% to 61·41%.This study developed and externally validated a preoperative CT image-based deep learning model to predict the survival benefits of EGFR-TKI and ICI therapies in stage IV NSCLC patients, which will facilitate optimized and individualized treatment strategies.This study received funding from the National Natural Science Foundation of China (82001904, 81930053, and 62027901), and Key-Area Research and Development Program of Guangdong Province (2021B0101420005).© 2022 The Author(s).

基金：
This study received funding from the National Natural Science Foundation of China (82001904, 81930053, and 62027901), and Key-Area Research and Development Program of Guangdong Province (2021B0101420005).
语种：
外文
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PubmedID：
中科院(CAS)分区：
出版当年[2022]版：
大类 | 1 区 医学
小类 | 1 区 医学：内科
最新[2023]版：
大类 | 1 区 医学
小类 | 1 区 医学：内科
JCR分区：
出版当年[2022]版：
Q1 MEDICINE, GENERAL & INTERNAL
最新[2023]版：
Q1 MEDICINE, GENERAL & INTERNAL

影响因子： 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者：
第一作者机构： [1]Department of radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC, Hefei, Anhui, China.
共同第一作者：
通讯作者：
通讯机构： [3]School of Health Management, China Medical University, Shenyang, Liaoning, China. [11]Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China. [12]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China. [14]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China. [15]Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China. [16]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China. [17]CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Beijing, China. [*1]School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
推荐引用方式(GB/T 7714)：
Deng Kexue,Wang Lu,Liu Yuchan,et al.A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study[J].ECLINICALMEDICINE.2022,51:doi:10.1016/j.eclinm.2022.101541.
APA：
Deng Kexue,Wang Lu,Liu Yuchan,Li Xin,Hou Qiuyang...&Song Jiangdian.(2022).A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study.ECLINICALMEDICINE,51,
MLA：
Deng Kexue,et al."A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study".ECLINICALMEDICINE 51.(2022)

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