CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer

doi:10.1016/j.ejrad.2020.109277

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

	CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer
	Sun, Rui-Jia 2; Fang, Meng-Jie 4,5; Tang, Lei 2; Li, Xiao-Ting 2; Lu, Qiao-Yuan 2; Dong, Di 4,5; Tian, Jie 1,3,4; Sun, Ying-Shi 2
刊名	EUROPEAN JOURNAL OF RADIOLOGY
	2020-11-01
卷号	132 页码:8
关键词	Stomach neoplasms Multi-detector computed tomography Radiomics Deep learning
ISSN号	0720-048X
DOI	10.1016/j.ejrad.2020.109277
通讯作者	Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Sun, Ying-Shi(sys27@163.com)
英文摘要	Purpose: This work aimed to develop and validate a deep learning radiomics model for evaluating serosa invasion in gastric cancer. Materials and Methods: A total of 572 gastric cancer patients were included in this study. Firstly, we retrospectively enrolled 428 consecutive patients (252 in the training set and 176 in the test set I) with pathological confirmed T3 or T4a. Subsequently, 144 patients who were clinically diagnosed cT3 or cT4a were prospectively allocated to the test set II. Histological verification was based on the surgical specimens. CT findings were determined by a panel of three radiologists. Conventional hand-crafted features and deep learning features were extracted from three phases CT images and were utilized to build radiomics signatures via machine learning methods. Incorporating the radiomics signatures and CT findings, a radiomics nomogram was developed via multivariable logistic regression. Its diagnostic ability was measured using receiver operating characteristiccurve analysis. Results: The radiomics signatures, built with support vector machine or artificial neural network, showed good performance for discriminating T4a in the test I and II sets with area under curves (AUCs) of 0.76-0.78 and 0.79-0.84. The nomogram had powerful diagnostic ability in all training, test I and II sets with AUCs of 0.90 (95 % CI, 0.86-0.94), 0.87 (95 % CI, 0.82-0.92) and 0.90 (95 % CI, 0.85-0.96) respectively. The net reclassification index revealed that the radiomics nomogram had significantly better performance than the clinical model (p-values < 0.05). Conclusions: The deep learning radiomics model based on CT images is effective at discriminating serosa invasion in gastric cancer.
资助项目	Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support[ZYLX201803] ; 'Beijing Hospitals Authority' Ascent Plan[DFL20191103] ; National Natural Science Foundation of China[81971584] ; National Natural Science Foundation of China[91959116] ; 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[81227901] ; National Natural Science Foundation of China[82022036] ; National Key R&D Program of China[2019YFC0117705] ; National Key R&D Program of China[2017YFC1309101] ; National Key R&D Program of China[2017YFC1309104] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Youth Innovation Promotion Association CAS[2017175]
WOS关键词	COMPUTED-TOMOGRAPHY ; PHASE-II ; CHEMOTHERAPY ; STOMACH
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER IRELAND LTD
WOS记录号	WOS:000585834300015
资助机构	Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support ; 'Beijing Hospitals Authority' Ascent Plan ; National Natural Science Foundation of China ; National Key R&D Program of China ; Bureau of International Cooperation of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/41673]
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	Dong, Di; Tian, Jie; Sun, Ying-Shi
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China 2.Peking Univ, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing, Dept Radiol,Canc Hosp & Inst, Beijing 100142, Peoples R China 3.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Sch Life Sci & Technol, Xian 710126, Shaanxi, Peoples R China 4.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Sun, Rui-Jia,Fang, Meng-Jie,Tang, Lei,et al. CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer[J]. EUROPEAN JOURNAL OF RADIOLOGY,2020,132:8.
APA	Sun, Rui-Jia.,Fang, Meng-Jie.,Tang, Lei.,Li, Xiao-Ting.,Lu, Qiao-Yuan.,...&Sun, Ying-Shi.(2020).CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer.EUROPEAN JOURNAL OF RADIOLOGY,132,8.
MLA	Sun, Rui-Jia,et al."CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer".EUROPEAN JOURNAL OF RADIOLOGY 132(2020):8.