Abstract
OBJECTIVE: To develop a model for predicting the overall survival (OS) of hepatocellular carcinoma (HCC) patients after transarterial chemoembolization (TACE) on the basis of multisequence MRI radiomic features and clinical variables. METHODS: The DCE-MRI and clinical data of 116 HCC patients treated with TACE for the first time were retrospectively analyzed. The included patients were randomly divided into training and validation cohorts at a ratio of 7:3. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors that affect the OS of patients with HCC after TACE. Radiomic features were extracted from the sequences of FS-T2W images and arterial-phase (A) and portal venous-phase (P) axial DCE-MR images. The LASSO method was used to select the best radiomic features. Logistic regression was used to establish a radiomic model of each sequence, a joint model of MRI features (M model) combined the radiomic features of all the sequences, and a radiomic-clinical model (M-C model) that integrated the radiomic signatures and clinically independent predictors. The diagnostic performance of each model was evaluated as the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: The Child-Turcotte-Pugh (CTP) score and neutrophil-to-lymphocyte ratio (NLR) -platelet-to-lymphocyte ratio (PLR) were found to be independent risk factors that affect the OS of patients with HCC treated with TACE. The AUCs of the FS-T(2)WI, A, P, M, and M-C models for predicting the OS of HCC patients after TACE treatment were 0.779, 0.803, 0.745, 0.858 and 0.893, respectively, in the training group and 0.635, 0.651, 0.644, 0.778 and 0.803, respectively, in the validation group. The M-C model had the best predictive performance. CONCLUSION: Multiparameter MRI-based radiomic features may be helpful for predicting OS after TACE treatment in HCC patients. The inclusion of clinical indicators such as inflammation scores can improve the predictive performance.