International journal of general medicine · 2026
Prognostic Models for Small Hepatocellular Carcinoma Using Inflammatory Indices and Machine Learning: A Propensity Score-Matched Study.
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Cong Yun, Gou Yi, Ma Ziwei, Huang Wei, Zhang Lifeng, Guo Qiang, Aji Tuerganaili, Shao Yingmei
Аннотация
BACKGROUND: The prognosis for patients with small hepatocellular carcinoma (HCC) after curative resection is variable. Early recurrence (within 2 years) remains a significant clinical challenge, closely associated with poor long-term outcomes. Although inflammatory biomarkers have shown prognostic value, integrated models for predicting both early recurrence and long-term survival are lacking. This study developed and validated prognostic models for overall survival (OS) and recurrence-free survival (RFS) in HCC patients post-resection, employing propensity score matching (PSM) to control for confounders between small (≤3 cm) and non-small HCC, with an emphasis on assessing model performance within the small HCC subgroup.
METHODS: A retrospective analysis of HCC patients who underwent hepatectomy was performed. PSM (1:1 nearest-neighbor with replacement) was applied to balance baseline characteristics between small and non-small HCC groups. After matching, a balanced cohort was used for survival analysis. Independent prognostic factors were identified through Cox regression. A traditional nomogram and risk score models were developed and compared against three machine learning models (LASSO-Cox, Random Forest, XGBoost) using time-dependent AUC.
RESULTS: Following PSM, 165 patients (90 with small HCC and 75 with non-small HCC) were included. Small HCC patients demonstrated significantly better OS and RFS (both p < 0.01). Multivariate analysis identified tumor size, SII, and AAR as independent predictors for OS, and tumor size, PAR, NLR, and GPR for RFS. The LASSO-Cox model exhibited the best overall performance, achieving the highest accuracy for early recurrence (2-year RFS AUC = 0.727) and competitive accuracy for long-term survival (5-year OS AUC = 0.698). The nomogram retained good interpretability (5-year OS AUC = 0.691).
CONCLUSION: This study establishes a comprehensive prognostic framework for small HCC, integrating tumor size with systemic inflammation (SII, NLR), liver function (AAR, GPR), and nutritional-coagulation status (PAR). The LASSO-Cox model is recommended for predicting both early recurrence and long-term survival, offering refined risk stratification to guide postoperative management.
METHODS: A retrospective analysis of HCC patients who underwent hepatectomy was performed. PSM (1:1 nearest-neighbor with replacement) was applied to balance baseline characteristics between small and non-small HCC groups. After matching, a balanced cohort was used for survival analysis. Independent prognostic factors were identified through Cox regression. A traditional nomogram and risk score models were developed and compared against three machine learning models (LASSO-Cox, Random Forest, XGBoost) using time-dependent AUC.
RESULTS: Following PSM, 165 patients (90 with small HCC and 75 with non-small HCC) were included. Small HCC patients demonstrated significantly better OS and RFS (both p < 0.01). Multivariate analysis identified tumor size, SII, and AAR as independent predictors for OS, and tumor size, PAR, NLR, and GPR for RFS. The LASSO-Cox model exhibited the best overall performance, achieving the highest accuracy for early recurrence (2-year RFS AUC = 0.727) and competitive accuracy for long-term survival (5-year OS AUC = 0.698). The nomogram retained good interpretability (5-year OS AUC = 0.691).
CONCLUSION: This study establishes a comprehensive prognostic framework for small HCC, integrating tumor size with systemic inflammation (SII, NLR), liver function (AAR, GPR), and nutritional-coagulation status (PAR). The LASSO-Cox model is recommended for predicting both early recurrence and long-term survival, offering refined risk stratification to guide postoperative management.