Frontiers in Medicine (May 2023)
FAIL-T (AFP, AST, tumor sIze, ALT, and Tumor number): a model to predict intermediate-stage HCC patients who are not good candidates for TACE
Abstract
BackgroundPatients with un-resectable hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) are a diverse group with varying overall survival (OS). Despite the availability of several scoring systems for predicting OS, one of the unsolved problems is identifying patients who might not benefit from TACE. We aim to develop and validate a model for identifying HCC patients who would survive <6 months after their first TACE.MethodsPatients with un-resectable HCC, BCLC stage 0-B, who received TACE as their first and only treatment between 2007 and 2020 were included in this study. Before the first TACE, demographic data, laboratory data, and tumor characteristics were obtained. Eligible patients were randomly allocated in a 2:1 ratio to training and validation sets. The former was used for model development using stepwise multivariate logistic regression, and the model was validated in the latter set.ResultsA total of 317 patients were included in the study (210 for the training set and 107 for the validation set). The baseline characteristics of the two sets were comparable. The final model (FAIL-T) included AFP, AST, tumor sIze, ALT, and Tumor number. The FAIL-T model yielded AUROCs of 0.855 and 0.806 for predicting 6-month mortality after TACE in the training and validation sets, respectively, while the “six-and-twelve” score showed AUROCs of 0.751 (P < 0.001) in the training set and 0.729 (P = 0.099) in the validation sets for the same purpose.ConclusionThe final model is useful for predicting 6-month mortality in naive HCC patients undergoing TACE. HCC patients with high FAIL-T scores may not benefit from TACE, and other treatment options, if available, should be considered.
Keywords
