Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression Article Swipe
Duo Yu
,
Michael J. Kane
,
Yiqing Chen
,
Steven H. Lin
,
Radhe Mohan
,
Brian P. Hobbs
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1200/cci-25-00058
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1200/cci-25-00058
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.
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- Type
- article
- Language
- en
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- https://doi.org/10.1200/cci-25-00058
- OA Status
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- References
- 43
- Related Works
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- OpenAlex ID
- https://openalex.org/W4414051635
All OpenAlex metadata
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https://openalex.org/W4414051635Canonical identifier for this work in OpenAlex
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https://doi.org/10.1200/cci-25-00058Digital Object Identifier
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Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate ImmunosuppressionWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-01Full publication date if available
- Authors
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Duo Yu, Michael J. Kane, Yiqing Chen, Steven H. Lin, Radhe Mohan, Brian P. HobbsList of authors in order
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https://doi.org/10.1200/cci-25-00058Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1200/cci-25-00058Direct OA link when available
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0Total citation count in OpenAlex
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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