Bayesian Learning to Reduce Cardiac Risk of Patients With Locally Advanced Non-Small Cell Lung Cancer Based on Personalized Radiation Therapy Prescription

Authors

Ruitao Lin
Mei Chen
Xiaodong Zhang
Tianlin Xu
Ting Xu
Rachel C Maguire
Kelsey L Corrigan
Efstratios Koutroumpakis
Joe Y Chang
Steven H Lin
Aileen B Chen
Quynh-Nhu Nguyen
Saumil J Gandhi
Matthew S Ning
Julianna Bronk
Stephen Chun
Ali Ajdari
Joshua S Niedzielski
Jinzhong Yang
Xinru Chen
Tinsu Pan
Qing H Meng
Anne S Tsao
Anita Deswal
Radhe Mohan
Zhongxing Liao

Language

English

Publication Date

12-7-2025

Journal

International Journal of Radiation Oncology, Biology, Physics

DOI

10.1016/j.ijrobp.2025.11.061

PMID

41365476

PMCID

PMC12990097

PubMedCentral® Posted Date

3-17-2026

PubMedCentral® Full Text Version

Author MSS

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 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.

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 = .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).

Conclusions: Clinical 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.

Published Open-Access

yes

Recommended Citation

Lin, Ruitao; Chen, Mei; Zhang, Xiaodong; et al., "Bayesian Learning to Reduce Cardiac Risk of Patients With Locally Advanced Non-Small Cell Lung Cancer Based on Personalized Radiation Therapy Prescription" (2025). Faculty, Staff and Student Publications. 6280.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6280