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

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Multimodal multiphasic pre-operative image-based deep-learning predicts hepatocellular carcinoma outcomes after curative surgery
Hepatology ( IF 12.9 ) Pub Date : 2024-12-03 , DOI: 10.1097/hep.0000000000001180
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

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.

更新日期：2024-12-03

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