Using Bayesian Logistic Regression with High-Order Interactions to Model Radiation-Induced Toxicities Following Radiotherapy

Radiotherapy treatments of cancer patients are planned using dose-volume constraints. These constraints limit the volume of organs receiving a given threshold dose. We propose a new framework to predict radiation-induced toxicities and evaluate dosimetric constraints using Bayesian logistic regression with high-order interactions. The predictive power of 2 sets of rectal dose-volume constraints proposed in the recent literature was evaluated using follow-up data from the RT01 prostate radiotherapy trial. Toxicities considered were rectal bleeding and loose stools. Furthermore we derived a new type of geometrical dosimetric constraint and assessed the predictive power. % using the Bayesian logistic regression model. Bayesian logistic regression with high-order interactions using dosimetric constraints successfully predicted radiation-induced rectal bleeding and loose stools. Literature-based dose-volume constraints had less predictive power than our new type of geometrical constraint. Imposing the latter type of constraints when generating a treatment plan would be beneficial for outcome.