AI models based on gadoxetic acid-enhanced MRI to predict treatment response and prognosis after TACE in...

To develop and validate radiomics and deep learning models based on the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI (EOB-MRI) for the noninvasive prediction of treatment response and prognosis following transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC).

From April 2018 to September 2024, 160 patients with Barcelona Clinic Liver Cancer (BCLC) stage A or B HCC (>3 cm) were retrospectively enrolled and randomly divided into training (n = 112) and test (n = 48) sets. An independent cohort of 38 HCC patients was used for external validation. Twenty-six radiomic features were extracted using LASSO to construct a machine learning model using eXtreme gradient boosting (XGBoost). A deep convolutional neural network (DCNN) based on the ResNet50 architecture was used to develop a deep learning model. A clinical model was built via multivariate logistic regression. Model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Kaplan-Meier analysis of combined deep learning and radiomics(DLR) scores was used to estimate overall survival in the follow-up cohort (n = 117).

BCLC stage (P = 0.035), tumor number (P = 0.015), and tumor size (P = 0.013) were independent clinical predictors. In the training set, the AUCs (95% CI) for the clinical, radiomics (XGBoost), and deep learning (DCNN) models were 0.79, 0.84, and 0.96, respectively. In the test set, the AUCs were 0.70, 0.80, and 0.92, respectively. In the external validation set, the AUCs were 0.77, 0.80, and 0.86, respectively. The DCNN model showed superior calibration and the highest net clinical benefit in DCA. In addition, multivariable Cox regression revealed that DLR model output was an independent risk factor for the overall survival (hazard ratio: 15.9, 95% CI: 4.49-56.33; p < 0.001).

HBP-based AI models effectively predicted TACE response and prognosis in HCC patients, with the DCNN model showing the best performance. The integrated DLR model demonstrated high predictive accuracy and may serve as a reliable tool for individualized treatment planning in precision oncology.