Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics
We present a reliable and interpretable tool for non-invasively assessing EGFR expression status in patients with breast cancer. The most important predictors captured tumor heterogeneity and microstructural uniformity, highlighting the biological relevance of radiomic patterns in EGFR-positive tumors. This model integrates advanced imaging analyses with machine learning, underscoring the potential of radiomics to advance precision oncology.
Authors
- Huohu Zhong (ORCID: https://orcid.org/0000-0001-7575-960X)
- Shanshan Su (ORCID: https://orcid.org/0000-0003-3850-3968)
- Jiemin Chen (ORCID: https://orcid.org/0000-0002-5671-1405)
- Zhirong Xu (ORCID: https://orcid.org/0000-0002-2928-468X)
- Guorong Lyu (ORCID: https://orcid.org/0000-0003-3123-1138)
- Handong Wang (ORCID: https://orcid.org/0000-0001-5989-9338)
- Xiaoqian Zhang (ORCID: https://orcid.org/0000-0003-1631-4971)
- Jiayi Ye (ORCID: https://orcid.org/0000-0002-9887-7972)
Institutions
- Fujian Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-17
- DOI
- https://doi.org/10.3389/fonc.2025.1683164
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00