Pre- and postoperative predictors of extrahepatic recurrence after curative resection for hepatocellular carcinoma

The factors associated with extrahepatic recurrence (EHR) after curative resection for hepatocellular carcinoma (HCC) have rarely been investigated. This study examined the pre- and postoperative predictors of EHR after curative resection in HCC patients over a ten-year follow-up period.

A retrospective review was conducted on treatment-naïve HCC patients who underwent curative resection between 2004 and 2019 at four tertiary hospitals in South Korea. The cohort of 1,069 patients was divided into a derivation cohort (n = 683) and a validation cohort (n = 386) based on participating institutions.

In the derivation cohort, the mean age was 59.8 years, and 85.7% were male. The majority of patients (98.7%) had compensated liver cirrhosis, and chronic hepatitis B was the prevalent etiology (72.9%). EHR developed in 107 patients (15.7%) and was associated with younger age, advanced tumor stages, and histological features including larger tumor size, a higher number of tumors, the presence of microvascular invasion, serosal nvasion, intrahepatic metastasis, and necrosis. According to multivariable Cox regression analyses, younger age, a higher modified Union for International Cancer Control (UICC) stage, exceeding the Milan criteria, and an albumin-bilirubin (ALBI) grade ≥ 2 were independently significant preoperative factors associated with EHR. Similarly, age, tumor number, the presence of microvascular invasion, necrosis, exceeding the Milan criteria, and an ALBI grade ≥ 2 were independently significant postoperative factors. Kaplan-Meier plots clearly differentiated EHR-free survival among the risk groups stratified by our EHR-preop and EHR-postop models. The EHR-preop and EHR-postop models, developed in the derivation cohort, were applied to the validation cohort and showed clear separation among risk groups.

Our study developed and validated predictive models (EHR-preop and EHR-postop) to identify the risk of EHR after curative HCC resection. The models could potentially enhance clinical decision-making by identifying patients at elevated EHR risk thus advancing personalized HCC care.