Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics
The nomogram prediction model, developed by integrating clinical pathological and radiomic features, demonstrated significant performance in predicting the efficacy of NAC in breast cancer, providing valuable reference for clinical personalized prediction planning.
Authors
- Jianing Cai (ORCID: https://orcid.org/0009-0004-6383-558X)
- Ye Chun Ruan (ORCID: https://orcid.org/0000-0002-4266-6789)
- Mingming Zhao (ORCID: https://orcid.org/0000-0001-5513-1305)
- Xingda Zhang (ORCID: https://orcid.org/0000-0001-5823-8490)
- Min Yan (ORCID: https://orcid.org/0000-0002-5184-9210)
- Siwei Cao (ORCID: https://orcid.org/0000-0002-1017-9556)
- Mengru Li (ORCID: https://orcid.org/0000-0003-2697-4615)
- Yang Wang (ORCID: https://orcid.org/0000-0001-7875-9111)
- Bo Gao (ORCID: https://orcid.org/0000-0002-9550-1339)
- Yun Jin (ORCID: https://orcid.org/0000-0001-7750-1370)
- Xiaoying Cheng (ORCID: https://orcid.org/0000-0001-8965-7399)
- Xing‐Yuan Liu
Institutions
- Harbin Medical University (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-04-17
- DOI
- https://doi.org/10.3389/fmed.2025.1582560
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China