A CART-based prognostic model for risk stratification of postoperative early recurrence in hepatocellular carcinoma with microvascular invasion - Zeng, Jie; Lu, Ri-Jin; Tao, Zheng; Zeng, Can; Mo, Kai-Xiang; Cen, Weijie; Lin, Yan; Liang, Rong; Li, Le-Qun; Wu, Guo-Bin; Ye, Jia-Zhou; Mai, Rong-Yun (Frontiers in Oncology, 2025)
BackgroundPostoperative early recurrence (ER) poses a major threat to long-term survival in hepatocellular carcinoma (HCC), especially in patients with microvascular invasion (MVI). Although conventional staging systems provide prognostic guidance, they are often inadequate for capturing recurrence risk in this high-risk subgroup. To develop and validate a CART-based prognostic model tailored to ER risk stratification and assessment of long-term outcomes following curative hepatectomy in MVI-positive HCC.MethodsA retrospective cohort of 440 patients with histologically confirmed HCC and MVI who underwent curative resection was analyzed. ER-associated predictors were identified via multivariable Cox regression and used to construct a classification and regression tree (CART) algorithm. Model discrimination, calibration, and clinical utility were evaluated using time-dependent ROC curves and decision curve analysis. Predictive performance for recurrence-free survival (RFS) and overall survival (OS) was compared against established staging systems.ResultsEight independent factors predictive of ER were identified: HBV-DNA load, tumor size, Edmondson-Steiner grade, tumor capsule integrity, MVI classification, satellite nodules, Ki-67 index, and CK19 expression. The CART model demonstrated robust discriminative ability (C-statistic: 0.773 in training; 0.764 in validation), and consistently outperformed conventional staging systems. Furthermore, CART-defined risk strata were significantly associated with both RFS and OS (P < 0.001).ConclusionsThis CART-based framework provides a transparent and clinically implementable tool for ER risk stratification in MVI-positive HCC. By outperforming existing staging algorithms, it offers a basis for individualized surveillance and postoperative management.