A novel deep learning segmentation and multimodal radiomics approach for radiation proctitis prediction in cervical cancer.
- DOI
- 10.1186/s13014-026-02905-x
- Published
- 2026 Aug 12
- Container
- Radiation oncology (London, England)
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13014-026-02905-x,
title = {A novel deep learning segmentation and multimodal radiomics approach for radiation proctitis prediction in cervical cancer.},
author = {He C and Wang Z and Liu Z and Su X and Hu Y and Cai J},
year = {2026},
journal = {Radiation oncology (London, England)},
doi = {10.1186/s13014-026-02905-x},
url = {https://doi.org/10.1186/s13014-026-02905-x}
}RIS
TY - JOUR TI - A novel deep learning segmentation and multimodal radiomics approach for radiation proctitis prediction in cervical cancer. AU - He C AU - Wang Z AU - Liu Z AU - Su X AU - Hu Y AU - Cai J PY - 2026 JO - Radiation oncology (London, England) DO - 10.1186/s13014-026-02905-x UR - https://doi.org/10.1186/s13014-026-02905-x ER -
APA
C, H., Z, W., Z, L., X, S., Y, H., & J, C. (2026). A novel deep learning segmentation and multimodal radiomics approach for radiation proctitis prediction in cervical cancer.. Radiation oncology (London, England). https://doi.org/10.1186/s13014-026-02905-x
Source records
- pubmed · retrieved 2026-11-10T18:53:21.989Z