Abstract A061: Machine Learning and Causal Inference-Based Predictive Risk Modeling of Unplanned Radiation Treatment Interruption

Abstract

Background: Adherence to scheduled radiation therapy (RT) is a key determinant of cancer treatment quality and outcomes. For this study, we developed an interpretable AI model to identify 1) patients at risk for multiple unplanned RT interruptions and 2) modifiable factors contributing to an elevated risk of RT interruption. Methods: We retrospectively analyzed clinical, socioeconomic, demographic, and behavioral data from 2,525 RT patients treated at the University of Tennessee Medical Center (UTMC) in Knoxville. The study cohort was dichotomized into patients with 0-1 unplanned RT interruptions (Class 0; n≈2000) and those missing >2 sessions (Class 1; n≈500). The dataset was partitioned into training, validation, and test sets (70:15:15 ratio), with class imbalance addressed in the training set by synthetic data generation via Tabular Variational Autoencoder. Twenty-seven candidate features were initially evaluated for multicollinearity using correlation matrices, heatmap visualization, and Variance Inflation Factor analysis. We applied feature selection methods (correlation-based techniques and causality-based approaches) to limit further modeling to the most predictive 15 core features. We compared XGBoost and Neural Networks-based classifiers, with each model undergoing hyperparameter optimization using Bayesian optimization methods. SHapley Additive exPlanations (SHAP) analysis was used to identify influential predictors. Results: The final optimized XGBoost model provided an overall accuracy of 82% and AUC-ROC of 63% on the independent test set. All tested models yielded similar performance, confirming the consistent predictive value of our selected features despite class imbalance. SHAP analyses identified dominant predictive contributions from treatment factors (prescribed radiation dose per session), patient resources (insurance coverage, marital status, social vulnerability indices), and travel distance to the radiotherapy facility. Supplementary causal analysis employing total causal effect methods further corroborated the direct influence of all these features. Conclusions: Our results suggest that causal inference and explainable AI modeling can provide useful interrogative strategies to identify modifiable predictors of RT adherence. Further refinement of predictive decision-support tools may lead to automated approaches to match high-risk patients with personalized interventions (e.g. community-based care navigation and/or patient psychosocial support) in real-world clinical settings to overcome social barriers to RT access. Citation Format: Rezaur Rashid, Soheil Hashtarkhani, Parnian K. Rahimabad, Brianna M. White, Fekede A. Kumsa, Lokesh Chinthala, Janet A. Zink, Christopher L. Brett, Robert L. Davis, David L. Schwartz, Arash Shaban-Nejad. Machine Learning and Causal Inference-Based Predictive Risk Modeling of Unplanned Radiation Treatment Interruption [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A061.