Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

doi:10.1002/mp.16647

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer
	Liu, Shengyuan 4,5; Deng, Jingyu 7; Dong, Di 4,5; Fang, Mengjie 1; Ye, Zhaoxiang 7; Hu, Yanfeng 2; Li, Hailin 1; Zhong, Lianzhen 4,5; Cao, Runnan 4,5; Zhao, Xun 4,5
刊名	MEDICAL PHYSICS
	2023-08-13
页码	11
关键词	deep learning extranodal soft tissue metastasis gastric cancer radiomics
ISSN号	0094-2405
DOI	10.1002/mp.16647
通讯作者	Shang, Wenting(wenting.shang@ia.ac.cn) ; Li, Guoxin(gzliguoxin@163.com) ; Liang, Han(tjlianghan@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	BackgroundThe potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PurposeThis multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. MethodsA total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). ResultsThe combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p ). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p ) in the internal survival cohort and 0.715 (0.650-0.779, p ) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p ) that was missed by preoperative diagnosis. ConclusionsThe model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; 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[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81971580] ; National Natural Science Foundation of China[81971619] ; National Natural Science Foundation of China[91959205] ; National Key Ramp;D Program of China[2017YFA0205200] ; Beijing Natural Science Foundation[Z20J00105] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703] ; Youth Innovation Promotion Association CAS[Y2021049]
WOS关键词	LYMPH-NODE METASTASIS ; INDICATOR ; SURVIVAL ; IMAGES ; DECODE ; BRIDGE
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:001047332400001
资助机构	Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key Ramp;D Program of China ; Beijing Natural Science Foundation ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54041]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Shang, Wenting; Li, Guoxin; Liang, Han; Tian, Jie
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China 2.Southern Med Univ, Nanfang Hosp, Guangzhou, Guangdong, Peoples R China 3.Beijing Key Lab Mol Imaging, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 6.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 7.Tianjin Med Univ Canc Inst & Hosp, Natl Clin Res Ctr Canc, Tianjins Clin Res Ctr Canc, Dept Gastrointestinal Surg,Key Lab Canc Prevent &, Tianjin, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shengyuan,Deng, Jingyu,Dong, Di,et al. Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer[J]. MEDICAL PHYSICS,2023:11.
APA	Liu, Shengyuan.,Deng, Jingyu.,Dong, Di.,Fang, Mengjie.,Ye, Zhaoxiang.,...&Tian, Jie.(2023).Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.MEDICAL PHYSICS,11.
MLA	Liu, Shengyuan,et al."Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer".MEDICAL PHYSICS (2023):11.