Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making

TL;DR

This paper introduces a graph neural network pipeline that predicts intra-arterial treatment response in Hepatocellular Carcinoma patients, improving decision-making by combining diverse data types and estimating uncertainty.

Contribution

The study presents a novel GNN-based pipeline integrating heterogeneous data and uncertainty estimation for better HCC treatment response prediction.

Findings

Achieved accuracy of 0.713 in predicting treatment response.

Incorporated uncertainty estimation to identify challenging cases.

Improved therapeutic decision-making through more accurate predictions.

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.