@article{TLCR120040,
author = {Jung Chun Kim and Seonhwa Kim and Jun Hyeong Park and Jaesung Heo},
title = {Computed tomography-based deep learning prediction of radiation-induced functional decline after lung cancer radiotherapy},
journal = {Translational Lung Cancer Research},
volume = {15},
number = {6},
year = {2026},
keywords = {},
abstract = {Background: Radiation therapy is a key treatment modality for lung cancer; however, post-treatment pulmonary function decline remains a clinically significant concern. Conventional dose-volume parameters provide limited individualized prediction of functional deterioration and fail to account for the heterogeneous baseline lung status visible on computed tomography (CT). In this study, we aimed to develop and validate a deep learning model to predict post-radiation pulmonary function and identify patients transitioning to a high-risk functional state [forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) ratio, FEV1/FVC },
issn = {2226-4477}, url = {https://tlcr.amegroups.org/article/view/120040}
}