Validated model for prediction of recurrent hepatocellular carcinoma after liver transplantation in Asian population

Abstract

BACKGROUND: Liver transplantation (LT) is regarded as the best treatment for both primary and recurrent hepatocellular carcinoma (HCC). Post-transplant HCC recurrence rate is relatively low but significant, ranging from 10%-30% according to different series. When recurrence happens, it is usually extrahepatic and associated with poor prognosis. A predictive model that allows patient stratification according to recurrence risk can help to individualize post-transplant surveillance protocol and guidance of the use of anti-tumor immunosuppressive agents.

AIM: To develop a scoring system to predict HCC recurrence after LT in an Asian population.

METHODS: Consecutive patients having LT for HCC from 1995 to 2016 at our hospital were recruited. They were randomized into the training set and the validation set in a 60:40 ratio. Multivariable Cox regression model was used to identity factors associated with HCC recurrence. A risk score was assigned to each factor according to the odds ratio. Accuracy of the score was assessed by the area under the receiver operating characteristic curve.

RESULTS: In total, 330 patients were eligible for analysis (183 in training and 147 in validation). Recurrent HCC developed in 14.2% of them. The median follow-up duration was 65.6 mo. The 5-year disease-free and overall survival rates were 78% and 80%, respectively. On multivariate analysis, alpha-fetoprotein > 400 ng/mL [P = 0.012, hazard ratio (HR) 2.92], sum of maximum tumor size and number (P = 0.013, HR 1.15), and salvage LT (P = 0.033, HR 2.08) were found to be independent factors for disease-free survival. A risk score was calculated for each patient with good discriminatory power (c-stat 0.748 and 0.85, respectively, in the training and validation sets). With the derived scores, patients were classified into low- (0–9), moderate- (> 9–14), and high-risk groups (> 14), and the risk of HCC recurrence in the training and validation sets was 10%, 20%, 54% (c-stat 0.67) and 4%, 22%, 62% (c-stat 0.811), accordingly. The risk stratification model was validated with chi-squared goodness-of-fit test (P = 0.425).

CONCLUSION; A validated predictive model featuring alpha-fetoprotein, salvage LT, and the sum of largest tumor diameter and total number of tumor nodule provides simple and reliable guidance for individualizing postoperative surveillance strategy.