CT-based deep learning prediction of complete response in intermediate-stage hepatocellular carcinoma treated with drug-eluting beads transarterial chemoembolization.

Objectives: To develop a CT-based deep learning (DL) model to predict complete response (CR) to drug-eluting beads-transarterial chemoembolization (TACE) in patients with naive Barcelona Clinic Liver Cancer (BCLC) B hepatocellular carcinoma (HCC). Methods: This dual-centre retrospective study included 93 patients with BCLC B HCC treated with drug-eluting beads-TACE (median size of 40 mm and 37 mm at Institutions 1 and 2). Complete response was defined as per modified Response Evaluation Criteria in Solid Tumours on liver contrast-enhanced CT within 2 months of treatment. A twin-network DL model with spatio-temporal Video Vision Transformer (ViViT) architecture was developed to predict CR using baseline dedicated liver CT. The model was extensively trained/tested based on an 8-fold cross-validation approach with an ensemble technique, a model vote system where the outcome is the average of multiple model predictions. Results: The CR rate was 36% (18/50) and 22% (11/49) at Institutions 1 and 2. The model showed high specificity and AUC, as well as moderate sensitivity and balanced accuracy when using either the late arterial phase (0.91 ± 0.12, 0.86 ± 0.16, 0.43 ± 0.23, and 0.67 ± 0.13, respectively) or the portal venous phase (0.90 ± 0.15, 0.85 ± 0.17, 0.57 ± 0.30, and 0.74 ± 0.16, respectively). Conclusion: The developed CT-based DL model predicted CR to drug-eluting beads-TACE in patients with naive BCLC B HCC with high specificity. It should be refined to improve sensitivity. Advances in knowledge: The study underscores the potential of artificial intelligence in precision medicine in patients with HCC. While the model shows promise, further research with larger datasets and prospective studies is needed to enhance its predictive power and clinical applicability.