The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer
Age, lesion size, burr, blood flow, and AI risk score are independent predictors of sclerosing adenosis and early breast cancer. The combined clinical ultrasound feature and AI model are correlated with AI risk score, US routine features, and clinical data, superior to the clinical ultrasound model and BI-RADS grading, and have good diagnostic performance, which can provide clinicians with a more effective diagnostic tool.
Authors
- Wenjia Hu (ORCID: https://orcid.org/0000-0001-6738-7535)
- Yinlong Liu (ORCID: https://orcid.org/0000-0002-6468-8233)
- Qiuyu Liu (ORCID: https://orcid.org/0000-0003-0622-266X)
- Gang Wu (ORCID: https://orcid.org/0000-0002-3814-8225)
- Yifei Liu (ORCID: https://orcid.org/0009-0009-1098-8044)
- Jing Zhao (ORCID: https://orcid.org/0009-0001-2450-4137)
- Haohui Zhu (ORCID: https://orcid.org/0000-0002-2147-864X)
- Ma B (ORCID: https://orcid.org/0000-0003-1258-9054)
Institutions
- Henan Provincial People's Hospital (CN)
Publication Details
- Journal
- Breast Cancer Targets and Therapy
- Published
- 2025-02-01
- DOI
- https://doi.org/10.2147/bctt.s483496
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72