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The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients

文献类型：期刊论文

作者Song, Zhengbo2; Liu, Tianchi3,4; Shi, Lei3,4; Yu, Zongyang5; Shen, Qing3,4; Xu, Mengdi3,4; Huang, Zhangzhou6; Cai, Zhijian7; Wang, Wenxian2; Xu, Chunwei8
刊名EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
出版日期2020-08-13
关键词Deep learning model Anaplastic lymphoma kinase Computed tomography Non-small cell lung cancer
ISSN号1619-7070
DOI10.1007/s00259-020-04986-6
通讯作者Chen, Ming(chenming@zjcc.org.cn)
英文摘要Purpose This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information for predicting anaplastic lymphoma kinase (ALK) fusion status in non-small cell lung cancer (NSCLC) patients. Materials and methods Preoperative CT images, clinicopathological information as well as the ALK fusion status from 937 patients in three hospitals were retrospectively collected to train and validate the DLM for the prediction of ALK fusion status in tumors. Another cohort of patients (n = 91) received ALK tyrosine kinase inhibitor (TKI) treatment was also included to evaluate the value of the DLM in predicting the clinical outcomes of the patients. Results The performances of the DLM trained only by CT images in the primary and validation cohorts were AUC = 0.8046 (95% CI 0.7715-0.8378) and AUC = 0.7754 (95% CI 0.7199-0.8310), respectively, while the DLM trained by both CT images and clinicopathological information exhibited better performance for the prediction of ALK fusion status (AUC = 0.8540, 95% CI 0.8257-0.8823 in the primary cohort,p < 0.001; AUC = 0.8481, 95% CI 0.8036-0.8926 in the validation cohort,p < 0.001). In addition, the deep learning scores of the DLMs showed significant differences between the wild-type and ALK infusion tumors. In the ALK-target therapy cohort (n = 91), the patients predicted as ALK-positive by the DLM showed better performance of progression-free survival than the patients predicted as ALK-negative (16.8 vs. 7.5 months,p = 0.010). Conclusion Our findings showed that the DLM trained by both CT images and clinicopathological information could effectively predict the ALK fusion status and treatment responses of patients. For the small size of the ALK-target therapy cohort, larger data sets would be collected to further validate the performance of the model for predicting the response to ALK-TKI treatment.
WOS关键词IDENTIFICATION ; CLASSIFICATION ; CRIZOTINIB ; MUTATIONS ; GENE
资助项目National Natural Science Foundation of China[81802276]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000559444500003
出版者SPRINGER
资助机构National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/70807]  
专题中国科学院合肥物质科学研究院
通讯作者Chen, Ming
作者单位1.Univ Chinese Acad Sci, Dept Radiol, Canc Hosp, Thejiang Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China
2.Univ Chinese Acad Sci, Dept Clin Trial, Canc Hosp, Zhejiang Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China
3.Shanghai Key Lab Artificial Intelligence Med Imag, Shanghai 200336, Peoples R China
4.YITU AI Res Inst Healthcare, Hangzhou 310000, Zhejiang, Peoples R China
5.900th Hosp, Dept Med Oncol, Fuzhou 350000, Fujian, Peoples R China
6.Fujian Canc Hosp, Dept Med Oncol, Fuzhou 350001, Peoples R China
7.Zhejiang Univ, Inst Immunol, Sch Med, Hangzhou 310009, Zhejiang, Peoples R China
8.Fujian Canc Hosp, Dept Pathol, Fuzhou 350001, Peoples R China
9.Univ Chinese Acad Sci, Dept Radiotherapy, Canc Hosp, Zhejiang Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714		 Song, Zhengbo,Liu, Tianchi,Shi, Lei,et al. The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients[J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING,2020.
APA Song, Zhengbo.,Liu, Tianchi.,Shi, Lei.,Yu, Zongyang.,Shen, Qing.,...&Chen, Ming.(2020).The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING.
MLA Song, Zhengbo,et al."The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients".EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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