Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer
The multi-modal radiomics model based on US, MM, CT, and MRI could accurately predict pCR in breast cancer after NAT, which was superior to all radiomic signatures. Incorporating clinical risk factors may further improve the performance of the muti-modal radiomics model, which could provide valuable information for guiding treatment decisions.
Authors
- Xiaobo Du (ORCID: https://orcid.org/0000-0001-6348-0636)
- Wenqiang Tang (ORCID: https://orcid.org/0000-0002-2972-5748)
- Xinlan Liu (ORCID: https://orcid.org/0000-0001-5241-115X)
- Qiuyan Wan
- Jie Lin (ORCID: https://orcid.org/0000-0002-5952-2319)
- Haonan Xu (ORCID: https://orcid.org/0000-0001-8873-0320)
- Yuwen Liang
- Kunyuan Gan
Institutions
- North Sichuan Medical University (CN)
- Mianyang Central Hospital (CN)
- Sichuan Mianyang 404 Hospital (CN)
- Affiliated Hospital of North Sichuan Medical College (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1186/s12885-025-14407-2
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36