ArticleInternational journal of radiation oncology, biology, physics2026
Bayesian Learning to Reduce Cardiac Risk of Patients With Locally Advanced Non-Small Cell Lung Cancer Based on Personalized Radiation Therapy Prescription.
Article in International journal of radiation oncology, biology, physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeRadiation-induced heart damage is a significant concern in the treatment of non-small cell lung cancer (NSCLC) that can have debilitating or life-threatening consequences. Current strategies focus on minimizing heart exposure, but individual susceptibility varies. Existing evidence also suggests that a uniform "one-size-fits-all" dosimetric constraint for the heart may not be optimal for all patients. METHODS AND MATERIALS: We developed a prospective study using Bayesian continuous learning and adaptation to develop a framework for personalized adaptive radiation treatment (PART) to reduce cardiovascular adverse events among patients with locally advanced NSCLC. The trial includes a Bayesian personalized risk prediction model to guide heart dose constraints; sequential learning to refine the model and the PART; continuous adaptation of the target risk level; and go/no-go monitoring of PART effectiveness in clinical implementation. Elevation of high-sensitivity cardiac troponin T (hs-cTnT) after radiation was used as a surrogate biomarker for grade ≥2 cardiovascular adverse events to allow real-time decision-making.
resultsAs of July 31, 2025, 100 patients have been enrolled and completed radiation treatment. Standard radiation plans were implemented for cohort 1 (50 patients), and PART for cohort 2 (50 patients). The first model incorporated patient- and disease-related factors and mean heart dose (MHD) as risk factors. The average treated MHDs were 7.84 ± 6.30 Gy in cohort 1 and 6.36 ± 6.01 Gy in cohort 2 (P = .20). The incidence of hs-cTnT elevation was 20.5% in cohort 2 compared with 31.9% in cohort 1. Within cohort 2, patients who satisfied the PART dose constraint had a markedly lower incidence of hs-cTnT elevation (9.7%) compared with those who exceeded the PART dose constraint (46.2%, P = .012).
conclusionsClinical implementation of PART model to guide treatment decision within a prospective trial is feasible. The recommended MHD constraints generated by the first version of PART appear reasonable and clinically relevant. PART was associated with lower incidence of hs-cTnT elevation.
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