Deep learning and radiomics integration of photoacoustic/ultrasound imaging for non-invasive prediction of luminal and non-luminal breast cancer subtypes
The DLRN model effectively integrates PA/US imaging with clinical data, showing potential for preoperative molecular subtype prediction and guiding personalized treatment strategies for BC patients.
Authors
- Jinfeng Xu (ORCID: https://orcid.org/0000-0001-5380-4625)
- Fajin Dong (ORCID: https://orcid.org/0000-0002-4558-4885)
- Zhibin Huang (ORCID: https://orcid.org/0000-0002-7579-6969)
- Guoqiu Li
- Huaiyu Wu (ORCID: https://orcid.org/0000-0003-2616-1171)
- Hongtian Tian (ORCID: https://orcid.org/0000-0002-5495-7516)
- Sijie Mo (ORCID: https://orcid.org/0009-0007-0168-0414)
- Jing Chen (ORCID: https://orcid.org/0000-0001-8863-2843)
- Zhijie Chen (ORCID: https://orcid.org/0000-0003-0627-0856)
- Shuzhen Tang
- Mengyun Wang (ORCID: https://orcid.org/0000-0001-9475-9488)
- Jing Zheng (ORCID: https://orcid.org/0000-0002-8362-2125)
Institutions
- Mindray (China) (CN)
- Shenzhen Second People's Hospital (CN)
Publication Details
- Journal
- Breast Cancer Research
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1186/s13058-025-02113-7
- Citations
- 4
- Primary Topic
- Photoacoustic and Ultrasonic Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.70