Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study

doi:10.1002/mp.14350

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

	Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study
	Wang, Siwen 5,6; Feng, Caizhen 3; Dong, Di 5,6; Li, Hailin 5,6; Zhou, Jing 2; Ye, Yingjiang 2; Liu, Zaiyi 4; Tian, Jie 1,6; Wang, Yi 3
刊名	MEDICAL PHYSICS
	2020-08-05
页码	10
关键词	disease-free survival gastric cancer multidetector-row computed tomography risk stratification radiomics
ISSN号	0094-2405
DOI	10.1002/mp.14350
通讯作者	Liu, Zaiyi(zyliu@163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Yi(wangyi@pkuph.edu.cn)
英文摘要	Purpose Preoperative and noninvasive prognosis evaluation remains challenging for gastric cancer. Novel preoperative prognostic biomarkers should be investigated. This study aimed to develop multidetector-row computed tomography (MDCT)-guided prognostic models to direct follow-up strategy and improve prognosis. Methods A retrospective dataset of 353 gastric cancer patients were enrolled from two centers and allocated to three cohorts: training cohort (n = 166), internal validation cohort (n = 83), and external validation cohort (n = 104). Quantitative radiomic features were extracted from MDCT images. The least absolute shrinkage and selection operator penalized Cox regression was adopted to construct a radiomic signature. A radiomic nomogram was established by integrating the radiomic signature and significant clinical risk factors. We also built a preoperative tumor-node-metastasis staging model for comparison. All models were evaluated considering the abilities of risk stratification, discrimination, calibration, and clinical use. Results In the two validation cohorts, the established four-feature radiomic signature showed robust risk stratification power (P = 0.0260 and 0.0003, log-rank test). The radiomic nomogram incorporated radiomic signature, extramural vessel invasion, clinical T stage, and clinical N stage, outperforming all the other models (concordance index = 0.720 and 0.727) with good calibration and decision benefits. Also, the 2-yr disease-free survival (DFS) prediction was most effective (time-dependent area under curve = 0.771 and 0.765). Moreover, subgroup analysis indicated that the radiomic signature was more sensitive in risk stratifying patients with advanced clinical T/N stage. Conclusions The proposed MDCT-guided radiomic signature was verified as a prognostic factor for gastric cancer. The radiomic nomogram was a noninvasive auxiliary model for preoperative individualized DFS prediction, holding potential in promoting treatment strategy and clinical prognosis.
资助项目	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[2017YFC0908300] ; National Natural Science Foundation of China[81901819] ; 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 Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81771912] ; Natural Science Foundation of Beijing Municipality[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Strategic Priority CAS Project[XDB38040200] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	EXTRAMURAL VENOUS INVASION ; PROGNOSTIC VALUE ; CURATIVE RESECTION ; VASCULAR INVASION ; NOMOGRAM ; METASTASIS ; SIGNATURE ; MRI ; CARCINOMA ; DIAGNOSIS
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:000555757600001
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Beijing Municipality ; Bureau of International Cooperation of Chinese Academy of Sciences ; Strategic Priority CAS Project ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40364]
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	Liu, Zaiyi; Tian, Jie; Wang, Yi
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China 2.Peking Univ, Dept Gastrointestinal Surg, Peoples Hosp, Beijing 100044, Peoples R China 3.Peking Univ, Dept Radiol, Peoples Hosp, Beijing 100044, Peoples R China 4.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou 510080, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Siwen,Feng, Caizhen,Dong, Di,et al. Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study[J]. MEDICAL PHYSICS,2020:10.
APA	Wang, Siwen.,Feng, Caizhen.,Dong, Di.,Li, Hailin.,Zhou, Jing.,...&Wang, Yi.(2020).Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study.MEDICAL PHYSICS,10.
MLA	Wang, Siwen,et al."Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study".MEDICAL PHYSICS (2020):10.