A CECT ‐Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual‐Validation Study

This study aimed to develop and validate machine learning (ML) models integrating clinical parameters and the 2PI system (Pathology and Prognosis-Informed Imaging System) for predicting postoperative recurrence risk in hepatocellular carcinoma (HCC). The multicenter retrospective study included 496 patients with solitary HCC (≤ 5 cm). Surgical resection (SR) patients from the primary center constituted the training set; radiofrequency ablation (RFA) patients from the same center formed the internal test set; and SR patients from other centers served as the external test set. In the training set, multivariable logistic regression identified seven imaging features associated with pathological markers, with odds ratios calculated as exp(β). Kendall's tau-b coefficient was used to indicate the strength of association between pathology and recurrence risk. A dual-information pathway 2PI system was constructed based on these coefficients, and the performance of ML models integrating clinical parameters and the 2PI system was evaluated. A total of 496 patients (mean age 58 ± 10 years, 376 men) were enrolled. Patients were stratified into high- and low-risk groups using a 2PI system threshold of ≥ 19. The Random Survival Forest (RSF) model incorporating clinical parameters and the 2PI system demonstrated favorable predictive performance across all sets (training: C-index 0.76 [95% CI: 0.72-0.80]; internal test: C-index 0.69 [95% CI: 0.63-0.75]; external test: C-index 0.68 [95% CI: 0.57-0.79]). The 2PI system applies a joint weighting strategy to create a straightforward image scoring system that enhances postoperative recurrence risk prediction for solitary HCC, demonstrating preliminary generalizability across both SR and RFA cohorts.