CASIA OpenIR
Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer
Li, Jing1,2; Dong, Di3,4; Fang, Mengjie3,4; Wang, Rui2; Tian, Jie3,5,6; Li, Hailiang1; Gao, Jianbo2
Source PublicationEUROPEAN RADIOLOGY
ISSN0938-7994
2020-01-17
Pages10
Corresponding AuthorGao, Jianbo(gaojianbo_cancer@163.com)
AbstractObjectives 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.
KeywordGastric cancer Tomography X-ray computed Lymph node Radiomics Deep learning
DOI10.1007/s00330-019-06621-x
WOS KeywordCLASSIFICATION ; ACCURACY ; RATIO
Indexed BySCI
Language英语
Funding ProjectNational 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] ; 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]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000507798400010
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29524
Collection中国科学院自动化研究所
Corresponding AuthorGao, Jianbo
Affiliation1.Zhengzhou Univ, Henan Canc Hosp, Affiliated Canc Hosp, Dept Radiol, Zhengzhou 450008, Henan, Peoples R China
2.Zhengzhou Univ, Affiliated Hosp 1, Dept Radiol, 1 East Jianshe Rd, Zhengzhou 450052, Henan, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, 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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Jing]'s Articles
[Dong, Di]'s Articles
[Fang, Mengjie]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Jing]'s Articles
[Dong, Di]'s Articles
[Fang, Mengjie]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Jing]'s Articles
[Dong, Di]'s Articles
[Fang, Mengjie]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.