Abstract
PURPOSE: There is a scarcity of predictive models currently accessible for prognosticating proliferative hepatocellular carcinoma (HCC), an integrated class of subtype, characterized by a dismal prognosis. Consequently, this study aimed to develop and validate a novel prognostic model capable of accurately predicting the prognosis of proliferative HCC after curative resection. PATIENTS AND METHODS: This retrospective multicenter study included patients with solitary HCC who underwent curative liver resection from August 2014 to December 2020 (n = 816). Patients were stratified into either the proliferative HCC cohort (n = 259) or the nonproliferative HCC cohort (n = 557) based on histological criteria. Disease-free survival (DFS) was compared between the two groups before and after one-to-one propensity score matching (PSM). Of all the proliferative HCC patients, 203 patients were assigned to training cohort, and 56 patients were assigned to validation cohort. Univariate and multivariate analyses were performed in training cohort to identify risk factors associated with worse DFS. Thereafter, a predictive model was constructed, subsequently validated in the validation cohort. RESULTS: The DFS of proliferative HCC was significantly worse than nonproliferative HCC before and after PSM. Meanwhile, multivariate regression analysis revealed that liver cirrhosis (P = 0.032) and larger tumor size (P = 0.000) were independent risk factors of worse DFS. Lastly, the discriminative abilities of the predictive model for 1, 3, 5-year DFS rates, as determined by receiver operating characteristic (ROC) curves, were 0.702, 0.720, and 0.809 in the training cohort and 0.752, 0.776, and 0.851 in the validation cohort, respectively. CONCLUSION: This study developed a predictive model with satisfactory accuracy to predict the worse DFS in proliferative HCCs after liver resection. Moreover, this predictive model may serve as a valuable tool for clinicians to predict postoperative HCC recurrence, thereby enabling them to implement early preventative strategies.