Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study
- DOI
- 10.3389/fmed.2026.1831103
- Published
- 2026-05-13
- Container
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fmed.2026.1831103,
title = {Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study},
author = {Qiang Fei and Xiaoli Mei and Jiacheng Zhao and Yili Xing and Yuanbin Li and Mei Yang and Jingnan Xue and Luying Qi and Yifeng Zheng and Hongxing Zhao and Hupo Bian},
year = {2026},
journal = {Frontiers in Medicine},
doi = {10.3389/fmed.2026.1831103},
url = {https://doi.org/10.3389/fmed.2026.1831103}
}RIS
TY - JOUR TI - Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study AU - Qiang Fei AU - Xiaoli Mei AU - Jiacheng Zhao AU - Yili Xing AU - Yuanbin Li AU - Mei Yang AU - Jingnan Xue AU - Luying Qi AU - Yifeng Zheng AU - Hongxing Zhao AU - Hupo Bian PY - 2026 JO - Frontiers in Medicine DO - 10.3389/fmed.2026.1831103 UR - https://doi.org/10.3389/fmed.2026.1831103 ER -
APA
Fei, Q., Mei, X., Zhao, J., Xing, Y., Li, Y., Yang, M., Xue, J., Qi, L., Zheng, Y., Zhao, H., & Bian, H. (2026). Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study. Frontiers in Medicine. https://doi.org/10.3389/fmed.2026.1831103
Source records
- crossref · retrieved 2026-09-25T16:47:15.906Z