Predicting the quality of digital mammography from the perspective of detectability of microcalcifications: A Radiomics approach
The combination of radiomic features and a machine learning regression model demonstrated the ability to account for variations in acquisition systems and simulated image degradations. This approach offers a promising tool for image quality assessment in clinical mammography, particularly in tasks involving microcalcification detection, without the need for labor-intensive observer studies.
Authors
- Lucas Exposto Soares (ORCID: https://orcid.org/0000-0001-8772-6899)
- Denise Yanikian Nersissian (ORCID: https://orcid.org/0000-0002-7761-3177)
- Lucas R. Borges (ORCID: https://orcid.org/0000-0003-3863-7085)
- Renato F. Caron (ORCID: https://orcid.org/0000-0003-0173-4797)
- Marcelo A. C. Vieira (ORCID: https://orcid.org/0000-0002-6038-7740)
Institutions
- Universidade Federal de São Carlos (BR)
- Universidade de São Paulo (BR)
- Institute of Physics (PL)
- Real Time Tomography (United States) (US)
- Hospital de Câncer de Barretos (BR)
- Institute of Physics of the Slovak Academy of Sciences (SK)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.18061
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior