Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
This addresses the need for improved therapeutic decision-making for HCC patients undergoing transarterial chemoembolization, but it is incremental as it builds on existing prediction methods with a new pipeline.
This work tackles the problem of predicting treatment response to intra-arterial therapy for Hepatocellular Carcinoma (HCC) patients, achieving an accuracy of 0.713 ± 0.075, F1 of 0.702 ± 0.082, and AUC of 0.710 ± 0.108.
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our graph neural network model seamlessly combines heterogeneous inputs of baseline MR scans, pre-treatment clinical information, and planned treatment characteristics and has been validated on patients with HCC treated by transarterial chemoembolization (TACE). It achieves Accuracy of $0.713 \pm 0.075$, F1 of $0.702 \pm 0.082$ and AUC of $0.710 \pm 0.108$. In addition, the pipeline incorporates uncertainty estimation to select hard cases and most align with the misclassified cases. The proposed pipeline arrives at more informed intra-arterial therapeutic decisions for patients with HCC via improving model accuracy and incorporating uncertainty estimation.