Abstract A062: Uncovering Social and Clinical Determinants of Baseline Distress Prior to Radiation Therapy: An Explainable Machine Learning Approach

Abstract Background: Psychological distress impacts cancer treatment adherence, outcomes, and quality of life. While predictive models can identify patients at elevated risk, understanding the mechanistic drivers of distress will be essential to design personalized psychosocial interventions. In this study, we applied explainable machine learning to predict and interpret baseline distress levels in cancer patients preparing to undergo radiation therapy. Methods: We retrospectively analyzed data from 2,103 patients treated at the University of Tennessee Medical Center (UTMC) in Knoxville. 1,953 were formally screened by an ONC Distress Depression Score instrument prior to radiation therapy, with 1,759 providing a numeric distress rating (0–10). An XGBoost regression model was trained to predict patient distress scores using a broad set of variables, including age, body mass index (BMI), ICD-coded cancer diagnosis category, marital status, insurance type, smoking and alcohol use, and neighborhood-level socioeconomic indicators such as median household income, educational attainment, and social vulnerability. Hyperparameters were optimized via Bayesian search, and model performance was evaluated with cross-validation. To ensure interpretability, we employed Shapley Additive Explanations (SHAP) to quantify each feature’s contribution to the predicted distress and to visualize the direction and non-linearity of these effects. Results: Distress levels were skewed toward the lower end of the scale, with 55.6% of patients reporting mild (<4), 20.0% moderate (4–6), and 15.9% severe (7–10) distress. The final model demonstrated moderate predictive accuracy, explaining approximately 59% of the variance in distress scores (R2 = 0.592, RMSE = 2.00, MAE = 1.67). SHAP analysis provided interpretable insights into how individual features influenced distress predictions. The top six contributors to predicted distress were younger age, lung cancer diagnosis, metastatic disease, current smoking, dual Medicare-Medicaid insurance coverage, and divorced marital status; all were associated with higher distress levels. These relationships highlighted both linear and non-linear effects, offering clinically meaningful explanations of patient-level risk. Conclusion: Our results suggest that explainable AI can contribute accurate predictions and interpretable insights into the contributors of psychological distress in cancer patients prior to radiation therapy. By combining XGBoost modeling with SHAP-based explanation, we uncovered potentially modifiable candidate drivers of distress. These findings support ongoing investigations to integrate interpretable machine learning strategies into clinical workflows to enhance preemptive, personalized supportive care interventions during radiation therapy planning. Citation Format: Soheil Hashtarkhani, Rezaur Rashid, Parnian K. Rahimabad, Fekede A. Kumsa, Brianna M. White, Lokesh Chinthala, Janet A. Zink, Christopher L. Brett, Robert L. Davis, David L. Schwartz, Arash Shaban-Nejad. Uncovering Social and Clinical Determinants of Baseline Distress Prior to Radiation Therapy: An Explainable Machine Learning Approach [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 A062.

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