Abstract
BACKGROUND: Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery. AIM: To develop predictive models utilizing machine learning (ML) methods to detect early-stage patients at a high risk of mortality. METHODS: Eight hundred and eight patients with HCC at Beijing Ditan Hospital were randomly allocated to training and validation cohorts in a 2:1 ratio. Prognostic models were generated using random survival forests and artificial neural networks (ANNs). These ML models were compared with other classic HCC scoring systems. A decision-tree model was established to validate the contribution of immune-inflammatory indicators to the long-term outlook of patients with early-stage HCC. RESULTS: Immune-inflammatory markers, albumin-bilirubin scores, alpha-fetoprotein, tumor size, and International Normalized Ratio were closely associated with the 5-year survival rates. Among various predictive models, the ANN model generated using these indicators through ML algorithms exhibited superior performance, with a 5-year area under the curve (AUC) of 0.85 (95%CI: 0.82-0.88). In the validation cohort, the 5-year AUC was 0.82 (95%CI: 0.74-0.85). According to the ANN model, patients were classified into high-risk and low-risk groups, with an overall survival hazard ratio of 7.98 (95%CI: 5.85-10.93, P < 0.0001) between the two cohorts. CONCLUSION: A non-invasive, cost-effective ML-based model was developed to assist clinicians in identifying high-risk early-stage HCC patients with poor postoperative prognosis following surgical resection.