Background: The Scottish Inflammatory Prognostic Score (SIPS), an innovative scoring system, has emerged as a promising biomarker for predicting patient outcomes following cancer therapy. This study aimed to evaluate the value of SIPS as a prognostic indicator following hepatectomy in patients with hepatocellular carcinoma (HCC).
Methods: This retrospective study included 693 HCC patients who underwent hepatectomy. Survival outcomes were compared between propensity score-matched groups. Independent prognostic factors were identified through Cox regression analysis. Additionally, both traditional Cox proportional hazards models and machine learning models based on the SIPS were developed and validated.
Results: A total of 693 HCC patients who underwent hepatectomy were included, with 102 in the high SIPS group and 591 in the low SIPS group. Following propensity score matching (1:3 ratio), both groups achieved balance, with 82 patients in the high SIPS group and 240 patients in the low SIPS group. The low SIPS group demonstrated significantly superior recurrence-free survival (RFS) (25 months vs. 21 months; P < 0.001) and overall survival (OS) (69 months vs. 58 months; P < 0.001) compared to the high SIPS group. Multivariable analysis identified SIPS as an independent adverse factor affecting both RFS and OS. The calibration curve for overall patient survival diagnosis displayed excellent predictive accuracy. Traditional COX prognostic models and machine learning models incorporating SIPS demonstrated excellent performance both the training and validation set.
Conclusion: This study confirms the prognostic significance of SIPS in post-hepatectomy HCC patients, providing a practical tool for risk stratification and clinical decision-making. Further research and validation are needed to consolidate its role in prognostic assessment.
Keywords: Hepatectomy; Hepatocellular carcinoma; Machine learning; Prognostic biomarkers; Propensity score matching (PSM); Scottish inflammatory prognostic score (SIPS).
© 2024. The Author(s).