Abstract
This study aims to develop a nomogram that predicts the risk of early recurrence after R0 hepatectomy in patients with early-stage solitary HCC with MVI. This will help clinicians in postoperative adjuvant therapy (PAT) decisions.A model was established in a primary cohort of 275 patients diagnosed with early-stage solitary HCC with MVI. Three models were established through backward stepwise regression, least absolute shrinkage and selection operator, and best subset regression. The best model was used to construct the nomogram. Internal validation of the nomogram was performed via bootstrap resampling. Moreover, the high- and low-risk populations were divided using the nomogram. The effect of PAT on prognosis was separately assessed with disease-free survival (DFS). Model 2 had the smallest Akaike information criterion (333.5) and the largest Harrell C-index (0.768). Unlike the other two models (Model 1 and Model 3), the integrated discrimination improvement (IDI) of Model 2 was significantly enhanced (Model 2 vs. Model 1: 7.72%, P < 0.001; Model 2 vs. Model 3: 5.01%, P < 0.001), confirming the suitability of the nomogram obtained by Model 2. Similarly, the nomogram displayed good calibration and excellent clinical benefits and was divided into low- and high-risk groups for early recurrence, with a score of 88.07. Unlike the non-PAT, the PAT prolonged the median DFS in high-risk patients (9.3 vs. 13.5 months), and the DFS was significantly different between the two groups (log-rank test: P = 0.011).In summary, the nomogram developed can effectively predict early recurrence after R0 hepatectomy in patients with early-stage solitary HCC with MVI. Thus, the high-risk patients identified by the nomogram may benefit from the PAT.