Work

LASSO-empowered nomogram integrating nutritional-inflammatory-tumor characteristics predicts immunotherapy outcomes in advanced HCC: Large retrospective cohort

Year: 2025

Type: article

Abstract: Background & Aims: Immune 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 methods: This 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. Results: Baseline 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). Conclusions: This 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.

Source: Frontiers in Immunology

DOI: 10.3389/fimmu.2025.1626940

Authors: Shuifang Hu, Mingcong Xu, Shuping Li, Wei Chen, Zhenwei Peng

Institutions: The First Affiliated Hospital, Sun Yat-sen University, Sun Yat-sen University Cancer Center

Language: English

Topic: Hepatocellular Carcinoma Treatment and Prognosis

Subfield: Hepatology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801