Faculty, Staff and Student Publications
Language
English
Publication Date
8-1-2025
Journal
JCO Clinical Cancer Informatics
DOI
10.1200/CCI-25-00058
PMID
40920994
PMCID
PMC12419026
PubMedCentral® Posted Date
9-8-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Purpose: Lymphocytes play critical roles in cancer immunity and tumor surveillance. Radiation-induced lymphopenia (RIL) is a common side effect observed in patients with cancer undergoing chemoradiation therapy (CRT), leading to impaired immunity and worse clinical outcomes. Although proton beam therapy (PBT) has been suggested to reduce RIL risk compared with intensity-modulated radiation therapy (IMRT), this study used Bayesian counterfactual machine learning to identify distinct patient profiles and inform personalized radiation modality choice.
Methods: A novel Bayesian causal inferential technique is introduced and applied to a matched retrospective cohort of 510 patients with esophageal cancer undergoing CRT to identify patient profiles for which immunosuppression could have been mitigated from radiation modality selection.
Results: BMI, age, baseline absolute lymphocyte count (ALC), and planning target volume determined the extent to which reductions in ALCs varied by radiation modality. Five patient profiles were identified. Significant variation in ALC nadir between PBT and IMRT was observed in three of the patient subtypes. Notably, older patients (age >69 years) with normal weight experienced a two-fold reduction in mean ALC nadir when treated with IMRT versus PBT. Mean ALC nadir was reduced significantly for IMRT patients with lower ALC at baseline (< 1.6 k/µL) who were overweight or obese when compared with PBT, whereas overweight patients with higher baseline ALC showed clinical equipoise between modalities.
Conclusion: Individualized radiation therapy selection can be an important tool to minimize immunosuppression for high-risk patients. The Bayesian counterfactual modeling techniques presented in this article are flexible enough to capture complex, nonlinear patterns while estimating interpretable patient profiles for translation into clinical protocols.
Keywords
Machine Learning, Bayes Theorem, Immune Tolerance, Humans, Lymphopenia, Proton Therapy, Radiotherapy, Intensity-Modulated, Retrospective Studies, Esophageal Neoplasms, Chemoradiotherapy, Precision Medicine, Adult, Middle Aged, Aged, Aged, 80 and over, Male, Female
Published Open-Access
yes
Recommended Citation
Yu, Duo; Kane, Michael J; Chen, Yiqing; et al., "Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression" (2025). Faculty, Staff and Student Publications. 6166.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6166
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