The feasibility of the radiomics models for tumor-infiltrating lymphocytes level prediction in breast cancer based on dynamic contrast-enhanced MRI
Radiomics features extracted from dynamic contrast-enhanced MRI, when integrated with clinical characteristics, represent a promising non-invasive approach for predicting TIL levels in breast cancer. This methodology may facilitate clinical decision-making through enhanced pretreatment tumor characterization without requiring invasive tissue sampling.
Authors
- Peng Yuan (ORCID: https://orcid.org/0000-0003-4627-8203)
- Dingbao Chen (ORCID: https://orcid.org/0000-0001-8581-3550)
- Xuege Hu
- Yi Wang (ORCID: https://orcid.org/0000-0002-2633-2295)
- Yulu Liu (ORCID: https://orcid.org/0000-0001-6024-1701)
- Xiufeng Chen (ORCID: https://orcid.org/0000-0003-1798-3851)
- Shu Wang (ORCID: https://orcid.org/0009-0002-4312-0865)
Institutions
- Peking University (CN)
- Aerospace Center Hospital (CN)
- Peking University People's Hospital (CN)
- Beijing Haidian Hospital (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1186/s12880-025-01968-8
- 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
- National Key Research and Development Program of China