Robust evaluation of tissue-specific radiomic features for classifying breast tissue density grades
Our RFE-SHAP feature selection methodology for classifying breast tissue density generalized well to validation datasets after accounting for natural class imbalance, and the identified radiomic features properly captured the progression of density grades. Our results potentiate future research into correlating selected radiomic features with clinical descriptors of breast tissue density.
Authors
- Walter C. Mankowski (ORCID: https://orcid.org/0000-0001-8591-6592)
- Andrew D. A. Maidment (ORCID: https://orcid.org/0000-0003-1074-7063)
- Anne Marie McCarthy (ORCID: https://orcid.org/0000-0002-3068-2669)
- Despina Kontos (ORCID: https://orcid.org/0000-0001-9031-5126)
- Vincent Dong (ORCID: https://orcid.org/0000-0002-4570-8008)
- Bruno Barufaldi (ORCID: https://orcid.org/0000-0003-3954-3611)
- Telmo M. Silva Filho (ORCID: https://orcid.org/0000-0003-0826-6885)
Institutions
- University of Bristol (GB)
- Columbia University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1117/1.jmi.12.s2.s22010
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Terri Brodeur Breast Cancer Foundation