Tumor burden with AFP improves survival prediction for TACE-treated patients with HCC: An international observational study

Abstract

Background & aimsCurrent prognostic models for patients with HCC undergoing transarterial chemoembolization (TACE) are not extensively validated and widely accepted. We aimed to develop and validate a continuous model incorporating tumor burden and biology for individual survival prediction and risk stratification. MethodsOverall, 4,377 treatment-naïve recommended TACE candidates from 39 centres in 5 countries were enrolled and divided into a training, internal validation, and two external validation datasets. The novel model was developed using a Cox multivariable regression analysis and compared with our original 6-and-12 model (the largest tumor size [ts, centimetres] + tumor number [tn]) and other available models in terms of predictive accuracy. ResultsThe proposed model named the ‘6-and-12 model 2.0’ was generated as ‘ts + tn + 1.5 × log10 alpha-fetoprotein [AFP])’, showed good discrimination (C-index 0.674) and calibration (Hosmer-Lemeshow test p=0.147), and outperformed current existing models. An easy-to-use stratification was proposed according to the different AFP levels (≤100, 100–400, 400–2,000, 2,000–10,000, 10,000–40,000, and >40,000 ng/ml) along with the corresponding tumor burden cut-offs (8/14, 7/13, 6/12, 5/11, 4/10, and any tumor burden), i.e. if the AFP level was 400–2,000 ng/ml, the stratification should be low-(≤6)/intermediate-(6-12)/high-risk (>12) strata. Hence, it could divide the patients into three distinct risk categories with a median overall survival of 45.0 (95% confidence interval [CI], 40.1–49.9), 30.0 (95% CI, 26.1–33.9), and 15.4 (95% CI, 13.4–17.4) months (p<0.001) from low-risk to high-risk strata, respectively. These findings were confirmed in validation and subgroup analyses. ConclusionsThe 6-and-12 model 2.0 significantly improved individual outcome prediction and better stratified the recommended TACE candidates, which could be used in clinical practice, as well as trial design.