CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer
Liu, Shengyuan1,2; Deng, Jingyu3; Dong, Di1,2; Fang, Mengjie4; Ye, Zhaoxiang3; Hu, Yanfeng5; Li, Hailin4; Zhong, Lianzhen1,2; Cao, Runnan1,2; Zhao, Xun1,2; Shang, Wenting1,2; Li, Guoxin5; Liang, Han3; Tian, Jie1,2,4,6,7
Source PublicationMEDICAL PHYSICS
ISSN0094-2405
2023-08-13
Pages11
Corresponding AuthorShang, Wenting(wenting.shang@ia.ac.cn) ; Li, Guoxin(gzliguoxin@163.com) ; Liang, Han(tjlianghan@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractBackgroundThe potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PurposeThis multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. MethodsA total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). ResultsThe combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p ). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p ) in the internal survival cohort and 0.715 (0.650-0.779, p ) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p ) that was missed by preoperative diagnosis. ConclusionsThe model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.
Keyworddeep learning extranodal soft tissue metastasis gastric cancer radiomics
DOI10.1002/mp.16647
WOS KeywordLYMPH-NODE METASTASIS ; INDICATOR ; SURVIVAL ; IMAGES ; DECODE ; BRIDGE
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81971580] ; National Natural Science Foundation of China[81971619] ; National Natural Science Foundation of China[91959205] ; National Key Ramp;D Program of China[2017YFA0205200] ; Beijing Natural Science Foundation[Z20J00105] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703] ; Youth Innovation Promotion Association CAS[Y2021049]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key Ramp;D Program of China ; Beijing Natural Science Foundation ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City ; Youth Innovation Promotion Association CAS
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:001047332400001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/54042
Collection中国科学院分子影像重点实验室
Corresponding AuthorShang, Wenting; Li, Guoxin; Liang, Han; Tian, Jie
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Tianjin Med Univ Canc Inst & Hosp, Natl Clin Res Ctr Canc, Tianjins Clin Res Ctr Canc, Dept Gastrointestinal Surg,Key Lab Canc Prevent &, Tianjin, Peoples R China
4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
5.Southern Med Univ, Nanfang Hosp, Guangzhou, Guangdong, Peoples R China
6.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu, Shengyuan,Deng, Jingyu,Dong, Di,et al. Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer[J]. MEDICAL PHYSICS,2023:11.
APA Liu, Shengyuan.,Deng, Jingyu.,Dong, Di.,Fang, Mengjie.,Ye, Zhaoxiang.,...&Tian, Jie.(2023).Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.MEDICAL PHYSICS,11.
MLA Liu, Shengyuan,et al."Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer".MEDICAL PHYSICS (2023):11.
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
[Liu, Shengyuan]'s Articles
[Deng, Jingyu]'s Articles
[Dong, Di]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Shengyuan]'s Articles
[Deng, Jingyu]'s Articles
[Dong, Di]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Shengyuan]'s Articles
[Deng, Jingyu]'s Articles
[Dong, Di]'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.