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

Background Hepatocellular carcinoma (HCC) recurrence frequently occurs after curative surgery. Histological microvascular-invasion (MVI) predicts recurrence but cannot provide preoperative 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.