Related Experiment Video
Updated: Sep 7, 2026
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Development of a PNI-Based Prognostic Model for Patients with Unresectable Hepatocellular Carcinoma Treated with
Weifu Liu1, Bohua Yu2, Fuqun Wei3,4
1Department of Oncology and Vascular Interventional Therapy, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China.
Background:
Prognostic stratification remains suboptimal in unresectable hepatocellular carcinoma (uHCC) treated with locoregional therapy (LRT) plus lenvatinib and PD-1 blockade. Existing staging systems insufficiently reflect host nutritional-immune status.
Methods:
We included a multicenter retrospective study including 347 patients with uHCC treated with LRT, lenvatinib, and a PD-1 inhibitor. Nine prognostic models were developed using Cox regression Model performance was evaluated using time-dependent AUC (tdAUC), calibration, and decision curve analysis (DCA). A nomogram was constructed for individualized survival prediction.
Results:
Median overall survival (OS) was 33.1 months (95% CI, 30.5-not reached) and median progression-free survival (PFS) was 10.2 months (95% CI, 9.0-12.6) in the overall cohort. The integrated model (incorporating tumor burden, ALBI grade, AFP, PNI) showed superior discrimination, with C-indices of 0.780 (training cohort) and 0.785 (validation cohort). The tdAUCs for OS and PFS were 0.892 and 0.807, respectively, in the training cohort and 0.861 and 0.819, respectively, in the validation cohort. Calibration plots demonstrated good agreement between predicted and observed 2-year OS in both cohorts. Risk stratification based on tertiles yielded significantly separated survival curves in both cohorts (all P<0.001). The nomogram achieved strong predictive accuracy, with 1-, 2-, and 3-year AUCs of 0.879, 0.808, and 0.787, respectively.
Conclusion:
PNI-integrated prognostic model provides robust risk stratification for uHCC patients receiving LRT combined with lenvatinib and PD-1 inhibitors, supporting individualized pre-treatment risk assessment.
