방사선의학 웹진

바로가기  >

Abstract
Background and purpose: Radiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac adverse events, including atrial fibrillation (AF), despite advanced RT. This study integrates patient-specific information to develop learning-based models to predict the incidence of AF following NSCLC chemoradiotherapy (CRT) and evaluates these models using institutional and external datasets.

Materials and methods: Institutional and external patient cohorts consisted of 321 and 187 NSCLC datasets who received definitive CRT, including 17 and 6 AF incidences, respectively. The network input had 159 features with clinical, dosimetry, and diagnostic. The class imbalance was mitigated by synthetic minority oversampling technique. To handle various types of input features, machine learning-based model adopted an intervention technique that chose one feature with the largest weight at each dosimetry sub-group in feature selection process, while deep learning-based model employed a hybrid architecture assigning different types of networks to corresponding input paths. Performance was assessed by area under the curve (AUC). The key features were investigated for the machine and deep learning-based models.

Results: The hybrid deep learning model outperformed the machine learning-based algorithm in internal validation (AUC: 0.817 vs. 0.801) and produced more consistent performance in external validation (AUC: 0.806 vs. 0.776). Importantly, maximum dose to heart and sinoatrial node (SAN) were found to be the key features for both learning-based models in external and internal validations.

Conclusions: The learning-based predictive models showed consistent prediction performance across internal and external cohorts, identifying maximum heart and SAN dose as key features for the incidence of AF.

Affiliations

Sang Kyun Yoo 1, Kyung Hwan Kim 2, Jae Myoung Noh 3, Jaewon Oh 4, Gowoon Yang 5, Jihun Kim 6, Nalee Kim 3, Hojin Kim 7, Hong In Yoon 8
1Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea; Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul, South Korea.
2Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
3Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
4Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
5Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea; Department of Radiation Oncology, Cha University Ilsan Cha Hospital, Cha University School of Medicine, Gyeonggi-do, South Korea.
6Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
7Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea. Electronic address: hjhenrykim@yuhs.ac.
8Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea. Electronic address: yhi0225@yuhs.ac.