Multimodal multiphasic pre-operative image-based deep-learning predicts hepatocellular carcinoma outcomes after curative surgery

IF 12.9 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Rex Wan-Hin Hui, Keith Wan-Hang Chiu, I-Cheng Lee, Chenlu Wang, Ho-Ming Cheng, Jianliang Lu, Xianhua Mao, Sarah Yu, Lok-Ka Lam, Lung-Yi Mak, Tan-To Cheung, Nam-Hung Chia, Chin-Cheung Cheung, Wai-Kuen Kan, Tiffany Cho-Lam Wong, Albert Chi-Yan Chan, Yi-Hsiang Huang, Man-Fung Yuen, Philip Leung-Ho Yu, Wai-Kay Seto
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引用次数: 0

Abstract

Background: Hepatocellular carcinoma (HCC) recurrence frequently occurs after curative surgery. Histological microvascular-invasion (MVI) predicts recurrence but cannot provide pre-operative prognostication, whereas clinical prediction scores have variable performances. Methods: Recurr-NET, a multimodal multiphasic residual-network random survival forest deep-learning model incorporating pre-operative CT and clinical parameters, was developed to predict HCC recurrence. Pre-operative triphasic CT scans were retrieved from patients with resected histology-confirmed HCC from four centers in Hong Kong (Internal-cohort). The internal-cohort was randomly divided in an 8:2 ratio into training and internal-validation. External-testing was performed in an independent cohort from Taiwan. Results: Among 1231 patients (Age 62.4, 83.1% male, 86.8% viral hepatitis, median follow-up 65.1 months), cumulative HCC recurrence at years 2 and 5 were 41.8% and 56.4% respectively. Recurr-NET achieved excellent accuracy in predicting recurrence from years 1-5 (Internal cohort AUROC 0.770-0.857; External AUROC 0.758-0.798), significantly out-performing MVI (Internal AUROC 0.518-0.590; External AUROC 0.557-0.615) and multiple clinical risk scores (ERASL-PRE, ERASL-POST, DFT, and Shim scores) (Internal AUROC 0.523-0.587, External AUROC: 0.524-0.620) respectively (all p<0.001). Recurr-NET was superior to MVI in stratifying recurrence risks at year 2 (Internal: 72.5% vs. 50.0% in MVI; External: 65.3% vs. 46.6% in MVI) and year 5 (Internal: 86.4% vs. 62.5% in MVI; External: 81.4% vs. 63.8% in MVI) (all p<0.001). Recurr-NET was also superior to MVI in stratifying liver-related and all-cause mortality (all p<0.001). The performance of Recurr-NET remained robust in subgroup analyses. Conclusion: Recurr-NET accurately predicted HCC recurrence, out-performing MVI and clinical prediction scores respectively, highlighting its potential in pre-operative prognostication.
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来源期刊
Hepatology
Hepatology 医学-胃肠肝病学
CiteScore
27.50
自引率
3.70%
发文量
609
审稿时长
1 months
期刊介绍: HEPATOLOGY is recognized as the leading publication in the field of liver disease. It features original, peer-reviewed articles covering various aspects of liver structure, function, and disease. The journal's distinguished Editorial Board carefully selects the best articles each month, focusing on topics including immunology, chronic hepatitis, viral hepatitis, cirrhosis, genetic and metabolic liver diseases, liver cancer, and drug metabolism.
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