A Prognostic Model To Predict Survival After Recurrence Among Patients With Recurrent Hepatocellular Carcinoma

Objective: We sought to develop and validate a preoperative model to predict survival after recurrence (SAR) in hepatocellular carcinoma (HCC). Summary Background Data: Although HCC is characterized by rates of recurrence as high as 60%, models to predict outcomes after recurrence remain relatively unexplored. Methods: Patients who developed recurrent HCC between 2000-2020 were identified from an international multi-institutional database. Clinicopathologic data on primary disease, and laboratory and radiologic imaging data on recurrent disease were collected. Multivariable cox regression analysis and internal bootstrap validation (5,000 repetitions) were used to develop and validate the SARScore. Optimal Survival Tree (OST) analysis was used to characterize SAR among patients treated with various treatment modalities. Results: Among 497 patients who developed recurrent HCC, median SAR was 41.2 months (95% CI 38.1–52.0). Presence of cirrhosis, number of primary tumors, primary macrovascular invasion, primary R1 resection margin, AFP>400 ng/mL on diagnosis of recurrent disease, radiologic extrahepatic recurrence, radiologic size and number of recurrent lesions, radiologic recurrent bilobar disease and early recurrence (≤24 months) were included in the model. The SARScore successfully stratified 1-, 3- and 5-year SAR and demonstrated strong discriminatory ability (3-year AUC: 0.75, 95% CI 0.70–0.79). While a subset of patients benefitted from resection/ablation, OST analysis revealed that patients with high SARScore disease had the worst outcomes (5-year AUC; training: 0.79 vs. testing: 0.71). The SARScore model was made available online for ease-of-use and clinical applicability (https://yutaka-endo.shinyapps.io/SARScore/). Conclusion: The SARScore demonstrated strong discriminatory ability and may be a clinically useful tool to help stratify risk and guide treatment for patients with recurrent HCC.