Key parameters in intratumoral-peritumoral region fusion models: optimizing deep learning radiomics for breast cancer diagnosis
This study suggests that the selection of PC images, OP ROIs, and the DE strategy effectively improves the performance of intratumoral-peritumoral region fusion models for predicting BC.
Authors
- Gong-Quan Chen
- Hai Hui Huang (ORCID: https://orcid.org/0000-0001-7430-0119)
- Juntao Shen (ORCID: https://orcid.org/0009-0009-7505-789X)
- Qiao Hu (ORCID: https://orcid.org/0000-0001-9492-0761)
- Haimei Lun
- Lin Li (ORCID: https://orcid.org/0000-0002-8014-8296)
- Yunxia Deng
- Ling Zhang (ORCID: https://orcid.org/0000-0002-6467-7067)
- Hui-Hua Wu
Institutions
- Guangxi Medical University (CN)
- Guilin Medical University (CN)
- The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
- Minda Hospital (CN)
- Hegang People's Hospital (CN)
- 181st Hospital of Chinese People's Liberation Army (CN)
- Affiliated Hospital of Guilin Medical College (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-03
- DOI
- https://doi.org/10.3389/fonc.2025.1587879
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00