A novel radiomics model combining GTVp, GTVnd, and clinical data for chemoradiotherapy response prediction in patients with advanced NSCLC
The GTVp-based radiomics model achieved an area under the curve (AUC) of 0.855 in the training cohort and 0.775 in the validation cohort. The multimodal composite model (integrating GTVp, GTVnd, and clinical parameters) significantly outperformed the GTVp-only model, with a training AUC of 0.862 and validation AUC of 0.863, demonstrating superior predictive performance for concurrent chemoradiotherapy response in this patient population.
Authors
- Tony He (ORCID: https://orcid.org/0000-0002-5363-7780)
- Benlan Li
- Xia Ji (ORCID: https://orcid.org/0000-0001-7597-2769)
- Y. J. Mo (ORCID: https://orcid.org/0000-0003-4308-3152)
- Mingdan Zhao
- Bo Du (ORCID: https://orcid.org/0000-0002-0059-8458)
- Dan Zou
- Fangyang Lu
- Zhongjun Huang
- Bing Lü (ORCID: https://orcid.org/0000-0001-9385-3931)
- Ya Li (ORCID: https://orcid.org/0000-0002-3351-7909)
- Min Zhang (ORCID: https://orcid.org/0009-0007-4363-8492)
- Jie Peng (ORCID: https://orcid.org/0000-0001-8907-701X)
- Yong Hu (ORCID: https://orcid.org/0000-0002-4440-1450)
Institutions
- Guiyang Medical University (CN)
- Wuhan Pulmonary Hospital (CN)
- Affiliated Hospital of Guizhou Medical University (CN)
- Anyang Hospital of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-07-24
- DOI
- https://doi.org/10.3389/fmed.2025.1596788
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00