Integrating multimodal ultrasound imaging and machine learning for predicting luminal and non-luminal breast cancer subtypes
This study establishes a multimodal machine learning model integrating advanced ultrasound imaging techniques to preoperatively distinguish luminal from non-luminal breast cancers. The model demonstrates significant potential to improve diagnostic accuracy and generalization, representing a notable advancement in non-invasive breast cancer diagnostics.
Authors
- Wei Lü (ORCID: https://orcid.org/0000-0003-0829-5440)
- Hao Bai (ORCID: https://orcid.org/0000-0002-7007-7172)
- Qing Li (ORCID: https://orcid.org/0000-0003-1184-7584)
- Cheng Qiu (ORCID: https://orcid.org/0000-0001-9626-3697)
- Xi Chen (ORCID: https://orcid.org/0009-0006-4690-5894)
- Y. Zhang (ORCID: https://orcid.org/0000-0002-6485-4844)
- Zhirong Shen (ORCID: https://orcid.org/0000-0003-4552-1670)
- Chang Gu (ORCID: https://orcid.org/0000-0002-8997-2900)
- Hao Zhang (ORCID: https://orcid.org/0000-0002-6012-8642)
- Yan Fu (ORCID: https://orcid.org/0000-0001-8112-8097)
- Hongping Chen (ORCID: https://orcid.org/0000-0002-1670-5449)
- Dong Liu (ORCID: https://orcid.org/0009-0008-7321-8840)
- Xiaohua Ni (ORCID: https://orcid.org/0000-0002-9527-6426)
- Guo Xue Li
Institutions
- Anhui Medical University (CN)
- Nantong University (CN)
- Affiliated Hospital of Nantong University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-08
- DOI
- https://doi.org/10.3389/fonc.2025.1558880
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00