Prediction of Hepatic Toxicity after Radiotherapy Using a Neural Network Including Blood Biomarkers and Liver Dose Distributions

Abstract

<h3>Purpose/Objective(s)</h3> ASTRO recently published practical guidelines for treating unresectable hepatocellular carcinoma with radiotherapy, recommending hypofractionated dose escalated regimens when tolerated by the liver. This highlights the need for patient-specific prediction models of radiation-induced hepatic toxicities, which necessitates inclusion not only of radiation dose, but also baseline liver function biomarkers. <h3>Materials/Methods</h3> We developed a shallow neural network to predict declines in Child-Pugh score (CP) of 2 or more, with a focus on robustness, interpretability and ability to include both proton and photon radiation dose distributions. The network uses 1 convolution layer to extract 8 dosimetric features that are representative of the differential DVH (dDVH) and interactions of different dose levels within, which are then combined in a fully connected layer with baseline platelet counts and liver function biomarkers—CP and albumin-bilirubin grade. After registering a study analysis plan, we trained the model on 117 patients from a single institution and independently validated it on 88 patients from another institution, where 47%/36% of the training/validation cohorts received proton radiotherapy. We compared the model to logistic regression to evaluate the importance of feature interactions on predictive power, measured by the area under the receiver operating characteristics (AUC). <h3>Results</h3> The model prediction of CP decline had a cross-validation AUC = 0.86; 95%CI: 0.69-0.97 and independent validation AUC = 0.74 with 89% accuracy in 10% high-risk group. In contrast, logistic regression showed cross-validation AUC 0.73; 95%CI: 0.56-0.88 and validation AUC 0.56, demonstrating a significant importance of feature interactions. Sensitivity analysis of the neural network revealed that patients with low baseline platelet counts were most sensitive to high doses to the normal liver compared to other patient groups, indicating a potential benefit of liver-sparing therapies in these patients. <h3>Conclusion</h3> We developed a patient-specific hepatic toxicity model that can take into account both proton and photon dose distributions and the interaction of radiation dose to normal liver with baseline liver function. Robust model performance across institutions and particularly in high-risk patients could allow stratification of patients to standard, hypofractionated regimens, or proton therapy based on a patient's risk score.