Abstract
PURPOSE: This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE. PATIENTS AND METHODS: The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan-Meier (K-M) analysis and the Log rank test. RESULTS: The median OS was 439 days (95% CI: 215.795-662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172-932.828), 395 days (95% CI: 309.415-480.585), respectively (P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285-3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310-2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496-1.000) and combined model (C-index: 0.770, 95% CI: 0.581-0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508-0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort. CONCLUSION: Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.