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Psychosocial Distress and Radiation Treatment Interruptions in Patients With Cancer Using Machine Learning and
Soheil Hashtarkhani1,2, Christopher L Brett3, Fekede Asefa Kumsa4
1Center for Biomedical Informatics, College of Medicine, University of Tennessee Health Science Center, 50 N Dunlap Street, Memphis, TN, 38103, United States, 1 9012875836.
Background:
Uninterrupted delivery of radiation therapy is critical to cancer treatment quality. Unplanned radiation treatment interruptions compromise tumor control and survival. Although previous studies have explored clinical and sociodemographic factors influencing treatment adherence, the role of psychosocial distress and related symptoms remains unclear.
Objective:
This study aims to identify patient-reported psychosocial predictors of radiation treatment interruptions from patient-reported distress screening data integrated with clinical and socioeconomic correlates via traditional and machine learning-enhanced modeling.
Methods:
This institutional cohort study included 1953 patients who underwent external beam radiation therapy between August 2020 and May 2024. Patient demographics, clinical characteristics, treatment details, and baseline distress levels were extracted from a hospital-level electronic health record data repository. Distress was assessed using the Distress Thermometer developed by the National Comprehensive Cancer Network. These data were linked with census tract-level socioeconomic indicators. Predictors of treatment interruption (measured as missed treatment days) were evaluated using negative binomial regression and an extreme gradient boosting (XGBoost) model with a Poisson objective function.
Results:
The study cohort missed a mean of 1.19 (SD 2.18) treatment days (range: 0-15). Higher distress scores, the presence of learning or communication barriers, and genitourinary malignancy were significantly associated with increased interruption risk in the negative binomial regression model. The XGBoost model confirmed these features as top contributors to prediction performance, while yielding an incrementally lower root mean squared error of 2.19 (vs 2.21 for the regression model).
Conclusions:
Patient-reported distress and health education barriers were modestly associated with treatment interruptions, underscoring the importance of incorporating psychosocial assessments into radiation oncology workflows. Both parametric and machine learning models provided stable and closely aligned predictions of missed radiation treatment days. Key predictors identified as statistically significant in the regression model were also among the top contributors in the XGBoost model, reinforcing the robustness of these findings and supporting integration of both approaches into future intervention strategies.
