Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor
The combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.
Authors
- Anqin Zhang (ORCID: https://orcid.org/0000-0003-1572-9585)
- Yingshi Su
- Zhenqiang Lian (ORCID: https://orcid.org/0000-0002-0240-7432)
- Ning Mao (ORCID: https://orcid.org/0000-0001-7097-5530)
- Rui Zhang (ORCID: https://orcid.org/0000-0002-6371-9050)
- Yan Zhang (ORCID: https://orcid.org/0000-0003-0159-9062)
- Xin D. Gao (ORCID: https://orcid.org/0000-0002-7108-3574)
- Wei Xia (ORCID: https://orcid.org/0000-0001-5721-9108)
- Yanyu Wang (ORCID: https://orcid.org/0000-0001-6915-0187)
- Wei Shi (ORCID: https://orcid.org/0009-0003-0208-6012)
Institutions
- Chinese Academy of Sciences (CN)
- Yuhuangding Hospital (CN)
- Suzhou University of Science and Technology (CN)
- Suzhou Institute of Biomedical Engineering and Technology (CN)
- Jinan Institute of Quantum Technology (CN)
- Zhujiang Hospital (CN)
- Guangdong Province Women and Children Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1186/s40644-024-00771-y
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China