Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence
This study demonstrates that radiomics-based AI can effectively identify and predict radiologists' interpretation errors in dense mammograms, with distinct radiomic features linked to false positives and false negatives in Chinese and Australian cohorts.
Authors
- Patrick Brennan (ORCID: https://orcid.org/0000-0001-8611-7258)
- Ziba Gandomkar (ORCID: https://orcid.org/0000-0001-6480-3572)
- Xuetong Tao (ORCID: https://orcid.org/0000-0002-7669-7241)
- Warren Reed (ORCID: https://orcid.org/0000-0002-8937-2198)
- Tong Li (ORCID: https://orcid.org/0000-0003-4956-765X)
Institutions
- The University of Sydney (AU)
- Cancer Council NSW (AU)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2024-10-09
- DOI
- https://doi.org/10.1093/bjr/tqae195
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Australian Government
- National Breast Cancer Foundation
- Cancer Institute NSW