Radiation doses to cardiac substructures predict elevation in high-sensitivity cardiac troponin T (hs-cTnT) levels in radiotherapy for lung cancer

肺癌 肌钙蛋白复合物 内科学 放射治疗 医学 心脏病学 灵敏度(控制系统) 肌钙蛋白 核医学 肿瘤科 心肌梗塞 电子工程 工程类
作者
Xinru Chen,Xiaodong Zhang,Ting Xu,Radhe Mohan,Ruitao Lin,Mei Chen,Rachel L. Maguire,Yao Zhao,Efstratios Koutroumpakis,Nicolas L. Palaskas,Anita Deswal,Ali Ajdari,Joshua S. Niedzielski,Sanjay Shete,Laurence E. Court,Jinzhong Yang,Zhongxing Liao
出处
期刊:Cold Spring Harbor Laboratory - medRxiv
标识
DOI:10.1101/2025.09.09.25335431
摘要

ABSTRACT Purpose Cardiotoxicity is a major concern for patients undergoing thoracic radiotherapy. This study compared the predictive power of radiation dose-volume histogram (DVH) parameters and radiomic/dosiomic features of the whole heart (WH) and cardiac substructures for elevated circulating high-sensitivity cardiac troponin T (hs-cTnT), a biomarker for early detection of cardiac adverse events. Methods and Materials A retrospective cohort of 160 patients with non-small cell lung cancer (NSCLC) from a completed prospective trial and a prospective cohort of 57 patients with NSCLC enrolled in an ongoing trial were analyzed. The endpoint was hs-cTnT elevation, indicated by increase of ≥5 ng/L from baseline. An in-house auto-segmentation model delineated 19 cardiac substructures. DVH parameters, radiomic, and dosiomic features were extracted from each patient. A 100-iteration Monte Carlo cross-validation (75%/25% split) was conducted within the retrospective cohort to mitigate random split bias. Logistic regression models using different input combinations were compared, with a model using only clinical factors served as the baseline. Key predictive features were identified using permutation importance during training. Models were validated by hold-out in the prospective cohort to evaluate robustness. Model performance was assessed by area under the receiver operating characteristic curve (AUROC). Results Incidence of hs-cTnT elevation was 31.9% in the retrospective and 29.8% in the prospective cohort. The substructure DVH model achieved the highest predictive performance in cross-validation (mean AUROC 0.71, 95% CI [0.70, 0.73]) and demonstrated greater robustness in hold-out validation compared to WH-based models (AUROC 0.60 vs. ≤0.51). Feature analysis identified the left anterior descending coronary artery V20Gy as the most dominant predictor, with cut-off values ranging from 0.2%-5% using various indices. Conclusions Cardiac substructure DVH parameters have superior predictive power and robustness over WH variables for predicting hs-cTnT elevation in NSCLC radiotherapy, emphasizing the need of using cardiac substructures for cardiotoxicity risk assessment.
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