ArticleJournal of hepatocellular carcinoma2025
Lasso-Based Nomogram for Predicting Early Recurrence Following Radical Resection in Hepatocellular Carcinoma.
Article in Journal of hepatocellular carcinoma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Antiviral therapy and hidden predictors of postoperative outcomes in hepatocellular carcinoma.BMC cancer · 2026Article
- Machine learning-based LASSO-Cox model for dementia prediction: The role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort.Annals of epidemiology · 2026Article
- Development and validation of a nomogram for predicting early recurrence in patients undergoing different courses of postoperative adjuvant transarterial chemoembolisation.BMC gastroenterology · 2025Article
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6 authors.
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Abstract
Background: 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. Methods: 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). Results: 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. Conclusion: 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.
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