Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles
- DOI
- 10.1002/acm2.70800
- Published
- 2026-09-21
- Container
- Journal of Applied Clinical Medical Physics
- Publisher
- Wiley
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1002/acm2.70800,
title = {Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles},
author = {Jun Yang and Longman Long and Guang Li and Jian Zeng and Caixiao Peng and Jingting Yan},
year = {2026},
journal = {Journal of Applied Clinical Medical Physics},
doi = {10.1002/acm2.70800},
url = {https://doi.org/10.1002/acm2.70800}
}RIS
TY - JOUR TI - Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles AU - Jun Yang AU - Longman Long AU - Guang Li AU - Jian Zeng AU - Caixiao Peng AU - Jingting Yan PY - 2026 JO - Journal of Applied Clinical Medical Physics DO - 10.1002/acm2.70800 UR - https://doi.org/10.1002/acm2.70800 ER -
APA
Yang, J., Long, L., Li, G., Zeng, J., Peng, C., & Yan, J. (2026). Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles. Journal of Applied Clinical Medical Physics. https://doi.org/10.1002/acm2.70800
Source records
- crossref · retrieved 2026-09-25T03:26:52.599Z