Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data
Our study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based "Data Amalgamation" model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC.
Authors
- Chuangui Song (ORCID: https://orcid.org/0000-0002-3384-0634)
- Yushuai Yu (ORCID: https://orcid.org/0009-0002-2494-4981)
- Ruiliang Chen (ORCID: https://orcid.org/0009-0004-5608-1273)
- Kaiyan Huang (ORCID: https://orcid.org/0009-0005-1635-9354)
- Xin Yu (ORCID: https://orcid.org/0009-0006-6581-2204)
- Jie Zhang
- Jialu Yi
Institutions
- Fujian Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
- Fujian Provincial Cancer Hospital (CN)
- Union Hospital (CN)
Publication Details
- Journal
- The Breast
- Published
- 2024-08-09
- DOI
- https://doi.org/10.1016/j.breast.2024.103786
- Citations
- 24
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.70