Individualized Risk Stratification for Early Recurrence of Hepatocellular Carcinoma: A Clinical Tool Derived from a Large-Scale (n=3084) Cohort Study
Introduction Early recurrence (typically within 2 years) following curative liver resection remains the primary obstacle to long-term survival in patients with hepatocellular carcinoma (HCC). Traditional linear staging systems frequently fail to capture the non-linear clinical and biological interactions driving early relapse. This study aimed to develop and validate a methodologically rigorous machine learning framework for precision post-hepatectomy risk stratification, and to deploy an interactive clinical tool. Patients and Methods In this large-scale retrospective cohort study, we analyzed 3084 HCC patients who underwent curative resection. To strictly prevent data leakage and ensure generalizability, the cohort was partitioned into training (60%), validation (20%), and independent test (20%) sets prior to any preprocessing. Six ML algorithms were evaluated, and SHapley Additive exPlanations (SHAP) were employed to decode model transparency. A web-based calculator was subsequently deployed for clinical use. Results XGBoost emerged as the optimal model, achieving an area under the curve (AUC) of 0.891 (95% CI: 0.865–0.915) on the independent test set, significantly outperforming traditional logistic regression (P < 0.0001). Using a rigorously optimized probability threshold of 0.310, the model yielded a sensitivity of 87.6% (95% CI: 83.5%–91.4%) and a specificity of 70.3% (95% CI: 65.4%–75.2%), with all confidence intervals derived from 2,000 bootstrap resamples. SHAP analysis identified tumor capsule integrity, neutrophil-to-eosinophil ratio (NER), and alpha-fetoprotein (AFP) as the most critical predictors of recurrence. Conclusion This methodologically rigorous ML framework provides a highly sensitive, data-driven tool for individual risk stratification. By identifying high-risk patients, the web-based calculator can guide clinicians in implementing intensified postoperative surveillance and selecting candidates for targeted adjuvant interventions, ultimately improving oncological outcomes in HCC.