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

We sought to develop and validate a preoperative model to predict survival after recurrence (SAR) in hepatocellular carcinoma (HCC).

Although HCC is characterized by recurrence as high as 60%, models to predict outcomes after recurrence remain relatively unexplored.

Patients who developed recurrent HCC between 2000 and 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 (5000 repetitions) were used to develop and validate the SARScore. Optimal Survival Tree analysis was used to characterize SAR among patients treated with various treatment modalities.

Among 497 patients who developed recurrent HCC, median SAR was 41.2 months (95% CI 38.1-52.0). The presence of cirrhosis, number of primary tumors, primary macrovascular invasion, primary R1 resection margin, AFP>400 ng/mL on the 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, Optimal Survival Tree 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/ ).

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.