LASSO-empowered nomogram integrating nutritional-inflammatory-tumor characteristics predicts immunotherapy outcomes in advanced HCC: Large retrospective cohort - Hu, Shuifang; Xu, Mingcong; Li, Shuping; Chen, Wei; Peng, Zhenwei (Frontiers in Immunology, 2025)
Background & AimsImmune checkpoint inhibitors (ICIs) show heterogeneous efficacy in advanced hepatocellular carcinoma (HCC), but existing biomarkers are invasive and costly. We aimed to develop a noninvasive prognostic model using routine clinical parameters.Materials and methodsThis retrospective study included 537 advanced HCC patients treated with PD-1/PD-L1 inhibitors, randomly divided into training (n=322) and validation (n=215) cohorts. Continuous variables were dichotomized using R packages. Univariate Cox regression followed by LASSO regression with 10-fold cross-validation selected predictive features for nomogram construction. Model performance was assessed via time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Cox proportional hazards models identified independent prognostic factors.ResultsBaseline characteristics were balanced between training and validation cohorts (P>0.05). The LASSO-derived nomogram incorporated 13 risk factors, which encompass multiple dimensions such as tumor characteristics, nutritional status, and inflammation. The model demonstrated robust discrimination, with the area under the curve (AUC) values exceeding 0.75 for 3-, 6-, 12-, and 24-month overall survival (OS). Calibration curves demonstrated a strong concordance between the predicted survival probabilities and the actual observations, and DCA revealed that the nomogram could increase net benefit. Additionally, the nomogram successfully stratified patients into low-risk and high-risk groups based on OS risk, with significant survival differences observed between the two groups in both the training and validation cohorts (all p < 0.001).ConclusionsThis validated nomogram integrating inflammatory, nutritional, and tumor characteristics provides a cost-effective tool for prognostic stratification in advanced HCC patients undergoing immunotherapy, potentially guiding personalized therapeutic strategies.