<scp>MRI</scp> Radiomics Signatures of 21‐Gene Recurrence Score for Predicting Survival in <scp>ER</scp> +/ <scp>HER2</scp> − Breast Cancer
The radiomic signatures within the tumor and peritumoral region exhibited potential to guide decisions on the use of chemotherapy and predict survival.
Authors
- Qifeng Wang (ORCID: https://orcid.org/0000-0002-3036-6084)
- Tianwen Xie (ORCID: https://orcid.org/0009-0008-5454-4624)
- Qin Xiao (ORCID: https://orcid.org/0000-0002-5587-2928)
- Lizhi Xie (ORCID: https://orcid.org/0000-0002-4379-9348)
- Wei Tang (ORCID: https://orcid.org/0000-0002-4431-3506)
- Ke‐Da Yu (ORCID: https://orcid.org/0000-0002-2883-1282)
- Yang Chen (ORCID: https://orcid.org/0000-0003-0147-3443)
- Yan Huang (ORCID: https://orcid.org/0000-0001-5466-7714)
- Li Liu (ORCID: https://orcid.org/0000-0003-2314-8756)
- Weijun Peng
- Yajia Gu
Institutions
- Shanghai Medical College of Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/cam4.71172
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Shanghai Anti-Cancer Association
- National Natural Science Foundation of China
- Fudan University