Bayesian Learning to Reduce Cardiac Risk for Locally Advanced NSCLC Patients Based on Personalized Radiotherapy Prescription

Ruitao Lin¹, Mei Chen², Xiaodong Zhang³, Tianlin Xu⁴, Ting Xu⁵, Rachel C Maguire⁵, Kelsey L Corrigan⁵, Efstratios Koutroumpakis⁶, Joe Y Chang², Steven H Lin², Aileen B Chen², Quynh-Nhu Nguyen², Saumil J Gandhi², Matthew S Ning², Julianna Bronk², Stephen Chun⁵, Ali Ajdari⁷, Joshua S Niedzielski³, Jinzhong Yang³, Xinru Chen⁸, Tinsu Pan⁹, Qing H Meng¹⁰, Anne S Tsao¹¹, Anita Deswal⁶, Radhe Mohan³, Zhongxing Liao¹²

Affiliations

¹ Department of Biostatistics, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. Electronic address: rlin@mdanderson.org.
² Department of Thoracic Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
³ Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
⁴ Edwards Lifesciences, Irvine, CA, USA.
⁵ Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
⁶ Department of Cardiology, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
⁷ Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
⁸ Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas, USA.
⁹ Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹⁰ Department of Laboratory Medicine, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹¹ Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹² Department of Thoracic Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. Electronic address: zliao@mdanderson.org.

PMID: 41365476
DOI: 10.1016/j.ijrobp.2025.11.061

Bayesian Learning to Reduce Cardiac Risk for Locally Advanced NSCLC Patients Based on Personalized Radiotherapy Prescription

Ruitao Lin et al. Int J Radiat Oncol Biol Phys. 2025.

. 2025 Dec 7:S0360-3016(25)06579-4.

doi: 10.1016/j.ijrobp.2025.11.061. Online ahead of print.

Authors

Affiliations

¹ Department of Biostatistics, Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. Electronic address: rlin@mdanderson.org.
² Department of Thoracic Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
³ Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
⁴ Edwards Lifesciences, Irvine, CA, USA.
⁵ Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
⁶ Department of Cardiology, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
⁷ Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
⁸ Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas, USA.
⁹ Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹⁰ Department of Laboratory Medicine, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹¹ Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹² Department of Thoracic Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. Electronic address: zliao@mdanderson.org.

PMID: 41365476
DOI: 10.1016/j.ijrobp.2025.11.061

Abstract

Purpose: Radiation-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: 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 (CAEs) 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 CAEs to allow real-time decision-making.

Results: As 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 = 0.20). The incidence of hs-cTnT elevation was 20.5% in cohort 2 compared to 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 = 0.012).

Conclusion: Clinical implementation of PART model to guide treatment decision within a prospective trial is feasible. The recommended mean heart dose constraints generated by the first version of PART appear reasonable and clinically relevant. PART was associated with lower incidence of hs-cTnT elevation.

Keywords: Bayesian learning; Cardiac risk; non-small-cell lung cancer; personalized radiotherapy.

PubMed Disclaimer

Conflict of interest statement

Declaration of competing interest The authors declare no potential conflicts of interest.

LinkOut - more resources

Full Text Sources
- ClinicalKey
- Elsevier Science
Research Materials
- NCI CPTC Antibody Characterization Program

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Bayesian Learning to Reduce Cardiac Risk for Locally Advanced NSCLC Patients Based on Personalized Radiotherapy Prescription

Affiliations

Bayesian Learning to Reduce Cardiac Risk for Locally Advanced NSCLC Patients Based on Personalized Radiotherapy Prescription

Authors

Affiliations

Abstract

Conflict of interest statement

LinkOut - more resources

Full Text Sources

Research Materials