Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Yang, Junlin, Dvornek, Nicha C., Zhang, Fan, Chapiro, Julius, Lin, MingDe, Abajian, Aaron, Duncan, James S.
Institutions: Yale University, University of Washington
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2990775917