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Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma

文献类型：期刊论文

作者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
刊名JOURNAL OF HEPATOCELLULAR CARCINOMA
出版日期2021
卷号8页码:1065-1076
关键词aggressive disease progression deep learning radiomics clinical factors high-risk risk prediction
DOI10.2147/JHC.S319639
通讯作者Lu, Ligong(llg0902@sina.com) ; Tian, Jie(tian@ieee.org)
英文摘要Purpose: 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.
WOS关键词SURVIVAL BENEFIT ; LIVER RESECTION ; CANCER
资助项目National 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]
WOS研究方向Oncology
语种英语
出版者DOVE MEDICAL PRESS LTD
WOS记录号WOS:000692832400001
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/45965]  
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Lu, Ligong; Tian, Jie
作者单位1.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
推荐引用方式
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.

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来源：自动化研究所

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