CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma
Fu, Sirui1,2; Pan, Meiqing2,3; Zhang, Jie4; Zhang, Hui2,3,5; Tang, Zhenchao2,3,5; Li, Yong1; Mu, Wei2,3,5; Huang, Jianwen1; Dong, Di3; Duan, Chongyang6; Li, Xiaoqun7; Wang, Shuo2,3; Chen, Xudong8; He, Xiaofeng9; Yan, Jianfeng10; Lu, Ligong1; Tian, Jie2,3,5,11
Source PublicationJOURNAL OF HEPATOCELLULAR CARCINOMA
2021
Volume8Pages:1065-1076
Corresponding AuthorLu, Ligong(llg0902@sina.com) ; Tian, Jie(tian@ieee.org)
AbstractPurpose: For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed. Patients and Methods: After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (Model(CS)), deep learning radiomics (Model(D)), and both (Model(CSD)), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram. Furthermore, we performed analyses of subgroups who received different treatments or those in different disease stages and compared time to aggressive PD and overall survival in the high- and low-risk subgroups. Results: Among the constructed models, Model(CSD), combining clinical/semantic factors and deep learning radiomics, outperformed Model(CS) and Model(D) (areas under the curve [AUCs] for the training dataset: 0.741, 0.815, and 0.856; validation dataset: 0.780, 0.836, and 0.862), with statistical difference per the net reclassification improvement, the integrated discrimination improvement, and/or the DeLong test in both datasets. Besides, Model(CSD) had the best calibration and decision curves. The performance of Model(CSD) was not affected by treatment types (AUC: resection = 0.839; transarterial chemoembolization = 0.895; p = 0.183) or disease stages (AUC: BCLC [Barcelona Clinic Liver Cancer] stage 0 and A = 0.827; BCLC stage AB &B = 0.861; p = 0.537). Moreover, the high-risk group had a significantly shorter median time to aggressive PD than the low-risk group (training dataset hazard ratio [HR] = 0.108, p < 0.001; validation dataset HR = 0.058, p < 0.001) and poorer overall survival (training dataset HR = 0.357, p < 0.001; validation dataset HR = 0.204, p < 0.001). Conclusion: Our deep learning-based model successfully stratified the risks of aggressive PD. In the high-risk population, current guideline indicates that first-line treatments are insufficient to prevent extrahepatic metastasis and macrovascular invasion and ensure survival benefits, so more therapies may be explored for these patients.
Keywordaggressive disease progression deep learning radiomics clinical factors high-risk risk prediction
DOI10.2147/JHC.S319639
WOS KeywordSURVIVAL BENEFIT ; LIVER RESECTION ; CANCER
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[82001914] ; National Natural Science Foundation of China[81871511] ; Project of High-Level Talents Team Introduction in Zhuhai City[Zhuhai HLHPTP201703] ; Nurture Programme of Zhuhai People's Hospital[2019-PY-07] ; Nurture Programme of Zhuhai People's Hospital[2020XSYC-09]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Project of High-Level Talents Team Introduction in Zhuhai City ; Nurture Programme of Zhuhai People's Hospital
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000692832400001
PublisherDOVE MEDICAL PRESS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45966
Collection中国科学院分子影像重点实验室
Corresponding AuthorLu, Ligong; Tian, Jie
Affiliation1.Jinan Univ, Zhuhai Hosp, Zhuhai Peoples Hosp, Zhuhai Intervent Med Ctr, 79 Kangning Rd, Zhuhai 519000, Guangdong, Peoples R China
2.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Beijing Key Lab Mol Imag, Beijing, Peoples R China
4.Jinan Univ, Zhuhai Hosp, Zhuhai Peoples Hosp, Dept Radiol, Zhuhai, Peoples R China
5.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
6.Southern Med Univ, Sch Publ Hlth, Dept Biostat, Guangzhou, Peoples R China
7.Zhongshan City Peoples Hosp, Dept Intervent Treatment, Zhongshan, Peoples R China
8.Shenzhen Peoples Hosp, Dept Radiol, Shenzhen, Peoples R China
9.Southern Med Univ, Nanfang Hosp, Intervent Diag & Treatment Dept, Guangzhou, Peoples R China
10.Yangjiang Peoples Hosp, Dept Radiol, Yangjiang, Peoples R China
11.Univ Chinese Acad Sci, Beijing, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Fu, Sirui,Pan, Meiqing,Zhang, Jie,et al. Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma[J]. JOURNAL OF HEPATOCELLULAR CARCINOMA,2021,8:1065-1076.
APA Fu, Sirui.,Pan, Meiqing.,Zhang, Jie.,Zhang, Hui.,Tang, Zhenchao.,...&Tian, Jie.(2021).Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma.JOURNAL OF HEPATOCELLULAR CARCINOMA,8,1065-1076.
MLA Fu, Sirui,et al."Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma".JOURNAL OF HEPATOCELLULAR CARCINOMA 8(2021):1065-1076.
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
[Fu, Sirui]'s Articles
[Pan, Meiqing]'s Articles
[Zhang, Jie]'s Articles
Baidu academic
Similar articles in Baidu academic
[Fu, Sirui]'s Articles
[Pan, Meiqing]'s Articles
[Zhang, Jie]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Fu, Sirui]'s Articles
[Pan, Meiqing]'s Articles
[Zhang, Jie]'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.