Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

doi:10.1007/s00330-019-06621-x

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

	Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer
	Li, Jing 2,3; Dong, Di 1,4; Fang, Mengjie 1,4; Wang, Rui 3; Tian, Jie 4,5,6; Li, Hailiang 2; Gao, Jianbo 3
刊名	EUROPEAN RADIOLOGY
	2020-01-17
页码	10
关键词	Gastric cancer Tomography X-ray computed Lymph node Radiomics Deep learning
ISSN号	0938-7994
DOI	10.1007/s00330-019-06621-x
通讯作者	Gao, Jianbo(gaojianbo_cancer@163.com)
英文摘要	Objectives To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer. Materials and methods Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28-81 years; 157 men [mean age, 60 years; range, 28-81 years] and 47 women [mean age, 54 years; range, 28-79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell's concordance index (C-index) based on patients' outcomes. Results The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002). Conclusion The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients' prognosis.
资助项目	National Natural Science Foundation of China[81271573] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924]
WOS关键词	CLASSIFICATION ; ACCURACY ; RATIO
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000507798400010
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29523]
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	Gao, Jianbo
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Zhengzhou Univ, Henan Canc Hosp, Affiliated Canc Hosp, Dept Radiol, Zhengzhou 450008, Henan, Peoples R China 3.Zhengzhou Univ, Affiliated Hosp 1, Dept Radiol, 1 East Jianshe Rd, Zhengzhou 450052, Henan, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 5.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shaanxi, Peoples R China 6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Li, Jing,Dong, Di,Fang, Mengjie,et al. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer[J]. EUROPEAN RADIOLOGY,2020:10.
APA	Li, Jing.,Dong, Di.,Fang, Mengjie.,Wang, Rui.,Tian, Jie.,...&Gao, Jianbo.(2020).Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.EUROPEAN RADIOLOGY,10.
MLA	Li, Jing,et al."Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer".EUROPEAN RADIOLOGY (2020):10.