Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles

Jun Yang, Longman Long, Guang Li, Jian Zeng, Caixiao Peng, Jingting Yan

Open source

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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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

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