Individualized Risk Stratification for Early Recurrence of Hepatocellular Carcinoma: A Clinical Tool Derived from a Large-Scale (n=3084) Cohort Study
TL;DR
This methodologically rigorous ML framework provides a highly sensitive, data-driven tool for individual risk stratification and can guide clinicians in implementing intensified postoperative surveillance and selecting candidates for targeted adjuvant interventions, ultimately improving oncological outcomes in HCC.
Abstract
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.