Lasso-Based Nomogram for Predicting Early Recurrence Following Radical Resection in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy with a high recurrence rate following curative resection. This study aimed to identify factors contributing to early recurrence (within 2 years) and develop a Lasso-based nomogram for individualized risk assessment.

We conducted a retrospective analysis of 206 hCC patients who underwent curative resection at Taizhou Hospital, Zhejiang Province, from January 2019 to August 2022. Patients were randomly divided into training (n=144) and validation (n=62) cohorts. Lasso regression was used to identify potential recurrence risk factors among 17 candidate predictors. A Cox proportional hazards model was constructed based on variables selected by Lasso. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Five independent predictors of early HCC recurrence were identified: age, serum alanine aminotransferase (ALT) levels, cirrhosis, tumor diameter, and microvascular invasion (MVI). The nomogram demonstrated area under the curve (AUC) values for recurrence-free survival (RFS) of 0.828 (95% confidence interval [CI]: 0.753-0.904) at 1 year, 0.799 (95% CI: 0.718-0.880) at 2 years, and 0.742 (95% CI: 0.642-0.842) at 5 years in the training cohort. The corresponding AUCs in the validation cohort were 0.823 (95% CI: 0.686-0.960), 0.804 (95% CI: 0.686-0.922), and 0.857 (95% CI: 0.722-0.992) at 1, 2 and 5 years, respectively. Calibration curves and DCA confirmed the nomogram's high accuracy and clinical utility.

The Lasso-Cox regression nomogram effectively predicts HCC recurrence within two years post-hepatectomy, providing a valuable tool for personalized postoperative management to improve patient outcomes.