Utility of Multiparametric Breast MRI Radiomics to Predict Cyclin D1 and TGF-β1 Expression
The integration of clinical features and multisequence MRI radiomics in a combined modeling approach holds significant predictive value for the expression status of cyclin D1 and TGF-β1. The model provides a noninvasive, dynamic evaluation method that provides effective guidance for clinical treatment.
Authors
- Jiaqi Xu
- Zhenhua Zhao (ORCID: https://orcid.org/0000-0001-8952-8677)
- Mingzhu Wei
- Shujie Yu (ORCID: https://orcid.org/0000-0001-8073-3180)
- Yu Zhang (ORCID: https://orcid.org/0009-0005-4827-3619)
- Mengying Zheng (ORCID: https://orcid.org/0009-0002-2052-0866)
Institutions
- Shaoxing University (CN)
- Shaoxing People's Hospital (CN)
Publication Details
- Journal
- Journal of Computer Assisted Tomography
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1097/rct.0000000000001717
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00