Predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients using a deep learning model
DeepSurv demonstrated superior predictive performance for RIHT in nasopharyngeal carcinoma patients compared to traditional models. Deep learning-based predictions offer high accuracy, which may enable personalized patient management and have great potentials in mitigating the risk of RIHT. These findings suggested that incorporating such model into clinical practice could be beneficial for the management of RITH.
Authors
- Zhongde Mu (ORCID: https://orcid.org/0000-0002-1984-4049)
- Xia He (ORCID: https://orcid.org/0000-0002-7314-1002)
- Dan Zong
- Mingjun Ding
- Yichen Mao (ORCID: https://orcid.org/0009-0007-2351-6308)
Institutions
- Jiangsu Cancer Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Clinical and Translational Radiation Oncology
- Published
- 2025-03-13
- DOI
- https://doi.org/10.1016/j.ctro.2025.100946
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Jiangsu Commission of Health
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Jiangsu Postdoctoral Research Foundation
- Jiangsu Provincial Commission of Health and Family Planning