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

文献类型：期刊论文

作者Li, Jing3,4; Dong, Di1,2; Fang, Mengjie1,2; Wang, Rui3; Tian, Jie2,5,6; Li, Hailiang4; Gao, Jianbo3
刊名EUROPEAN RADIOLOGY
出版日期2020-01-17
页码10
ISSN号0938-7994
关键词Gastric cancer Tomography X-ray computed Lymph node Radiomics Deep learning
DOI10.1007/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.
WOS关键词CLASSIFICATION ; ACCURACY ; RATIO
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
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.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Zhengzhou Univ, Affiliated Hosp 1, Dept Radiol, 1 East Jianshe Rd, Zhengzhou 450052, Henan, Peoples R China
4.Zhengzhou Univ, Henan Canc Hosp, Affiliated Canc Hosp, Dept Radiol, Zhengzhou 450008, Henan, 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.

入库方式： OAI收割

来源：自动化研究所

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