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

doi:10.1007/s00259-020-04986-6

HFCAS OpenIR

	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
	Song, Zhengbo 1; Liu, Tianchi 2,3; Shi, Lei2,3 ; Yu, Zongyang 4; Shen, Qing 2,3; Xu, Mengdi 2,3; Huang, Zhangzhou 5; Cai, Zhijian 6; Wang, Wenxian 1; Xu, Chunwei 7; Sun, Jingjing 8; Chen, Ming 9
	2020-08-13
发表期刊	EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
ISSN	1619-7070
通讯作者	Chen, Ming(chenming@zjcc.org.cn)
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.
关键词	Deep learning model Anaplastic lymphoma kinase Computed tomography Non-small cell lung cancer
DOI	10.1007/s00259-020-04986-6
关键词[WOS]	IDENTIFICATION ; CLASSIFICATION ; CRIZOTINIB ; MUTATIONS ; GENE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81802276]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000559444500003
出版者	SPRINGER
引用统计	被引频次：26[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	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 Clin Trial, Canc Hosp, Zhejiang Canc Hosp, Hangzhou 310022, Zhejiang, Peoples R China 2.Shanghai Key Lab Artificial Intelligence Med Imag, Shanghai 200336, Peoples R China 3.YITU AI Res Inst Healthcare, Hangzhou 310000, Zhejiang, Peoples R China 4.900th Hosp, Dept Med Oncol, Fuzhou 350000, Fujian, Peoples R China 5.Fujian Canc Hosp, Dept Med Oncol, Fuzhou 350001, Peoples R China 6.Zhejiang Univ, Inst Immunol, Sch Med, Hangzhou 310009, Zhejiang, Peoples R China 7.Fujian Canc Hosp, Dept Pathol, Fuzhou 350001, Peoples R China 8.Univ Chinese Acad Sci, Dept Radiol, Canc Hosp, Thejiang Canc Hosp, Hangzhou 310022, Zhejiang, 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).