ΔSII-based nomogram for prognosis prediction after radical resection for hepatocellular carcinoma.

Background: Primary liver cancer (PLC) ranks sixth in global incidence and third in cancer mortality. Chronic inflammation drives hepatocarcinogenesis via fibrosis. Despite curative resection, hepatocellular carcinoma (HCC) patients have high recurrence rates (50% at 3 years and 70% at 5 years). In addition to pathological factors [e.g., an alpha-fetoprotein (AFP) concentration >400 ng/L and vascular invasion], systemic immune inflammation influences the prognosis. The systemic immune-inflammation index (SII) has prognostic value, but the preoperative SII does not account for surgical impact. We propose a novel postoperative/preoperative SII ratio (ΔSII) to predict survival and recurrence, integrated into a ΔSII-based prognostic nomogram. Methods: We retrospectively analyzed 244 HCC patients undergoing radical resection, randomly divided into training (n=171) and validation (n=73) cohorts at a 7:3 ratio. The optimal ΔSII cutoff for overall survival (OS) was determined by receiver operating characteristic (ROC) curve analysis. Clinicopathologic associations were assessed with Chi-squared/Fisher tests. Variable selection involved univariable Cox, least absolute shrinkage and selection operator (LASSO), and Boruta algorithms; multivariable Cox models were used to construct OS/recurrence-free survival (RFS) nomograms. Performance was evaluated via time-dependent area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: The ΔSII outperformed preoperative SII in predicting OS (AUC: 0.770 Conclusions: The ΔSII-based nomogram effectively predicts OS/RFS after resection, enabling individualized management for high-risk HCC patients.