Combining handcrafted radiomics features and deep learning features within different regions of interest in planning CT images for the prediction of symptomatic radiation pneumonitis in locally advanced non-small cell lung cancer
- DOI
- 10.1186/s13014-026-02896-9
- Published
- 2026-07-31
- Container
- Radiation Oncology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13014-026-02896-9,
title = {Combining handcrafted radiomics features and deep learning features within different regions of interest in planning CT images for the prediction of symptomatic radiation pneumonitis in locally advanced non-small cell lung cancer},
author = {Xuan Li and Hao Hang and Yan Kong and Jinmeng Zhang and Mingming Su and Zongqiong Sun and Yan Zhu and Fei Yang and Qing Zhao and Jianjiao Ni and Jianfeng Huang},
year = {2026},
journal = {Radiation Oncology},
doi = {10.1186/s13014-026-02896-9},
url = {https://doi.org/10.1186/s13014-026-02896-9}
}RIS
TY - JOUR TI - Combining handcrafted radiomics features and deep learning features within different regions of interest in planning CT images for the prediction of symptomatic radiation pneumonitis in locally advanced non-small cell lung cancer AU - Xuan Li AU - Hao Hang AU - Yan Kong AU - Jinmeng Zhang AU - Mingming Su AU - Zongqiong Sun AU - Yan Zhu AU - Fei Yang AU - Qing Zhao AU - Jianjiao Ni AU - Jianfeng Huang PY - 2026 JO - Radiation Oncology DO - 10.1186/s13014-026-02896-9 UR - https://doi.org/10.1186/s13014-026-02896-9 ER -
APA
Li, X., Hang, H., Kong, Y., Zhang, J., Su, M., Sun, Z., Zhu, Y., Yang, F., Zhao, Q., Ni, J., & Huang, J. (2026). Combining handcrafted radiomics features and deep learning features within different regions of interest in planning CT images for the prediction of symptomatic radiation pneumonitis in locally advanced non-small cell lung cancer. Radiation Oncology. https://doi.org/10.1186/s13014-026-02896-9
Source records
- crossref · retrieved 2026-11-07T19:19:05.951Z