Deep learning model based on DCE-MRI: fusion of 3D features of tumor, peritumoral vessels and metastatic lymph nodes for prediction of pathological complete response to neoadjuvant therapy in breast cancer
Fusing three-dimensional imaging features of tumors, peritumoral vessels, and metastatic lymph nodes, the DLR model shows favorable predictive efficacy. Importantly, the CCDLR model, constructed by incorporating clinical characteristics, exhibits significantly superior performance, underscoring its promising potential for clinical application in predicting pathological complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer.
Authors
- Mengting Yang (ORCID: https://orcid.org/0000-0003-4352-558X)
- A. Zhang (ORCID: https://orcid.org/0009-0005-1467-7762)
- Lei Zhang (ORCID: https://orcid.org/0000-0003-2343-084X)
- Yuemin Zhu (ORCID: https://orcid.org/0000-0001-6814-1449)
- Yue Du (ORCID: https://orcid.org/0000-0002-1095-1120)
- Zhihuan Zhou (ORCID: https://orcid.org/0000-0002-1738-064X)
Institutions
- Bengbu Medical College (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Shanghai Traditional Chinese Medicine Hospital (CN)
- The Second Affiliated Hospital of Bengbu Medical College (CN)
- First Affiliated Hospital of Bengbu Medical College (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-12-02
- DOI
- https://doi.org/10.3389/fonc.2025.1664631
- Primary Topic
- MRI in cancer diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00