Missed and true interval cancers on digital breast tomosynthesis screening mammograms: radiologists' assessment and artificial intelligence markings.
Authors
Affiliations (5)
Affiliations (5)
- Department of Breast Cancer Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway.
- Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway.
Abstract
Interval cancer, breast cancer detected after a negative screening examination but before the next scheduled appointment, represents a challenge in mammography screening programs due to less favorable histopathological characteristics compared to screen-detected cancer. To determine which interval cancers from digital breast tomosynthesis (DBT) were classified as missed and true by radiologists in a review, and to stratify the findings by risk scores and markings provided by an artificial intelligence (AI) model. In this retrospective informed consensus-based review, radiologists assessed mammograms from 46 interval cancers and classified those as false negative, minimal-sign significant or non-specific, or true negative. An AI risk score (1-7, low; 8-9, intermediate; or 10, high risk of malignancy) was available for each examination. For cases with AI risk scores of 8-10, the location of AI-detected markings was compared with the true cancer site. A total of 17% (8/46) of interval cancers were classified as false negative, 22% (10/46) as minimal-sign significant, 20% (9/46) as minimal-sign non-specific, and 41% (19/46) as true negative. The AI model correctly identified 35% (16/46) and incorrectly located 24% (11/46). Considering false negative and minimal-sign significant as cases with the highest probability of being diagnosed at screening due to mammographic visibility, the proportion of cases correctly identified by the AI model was reduced from 35% to 22% (10/46). About 20% of interval cancers have potential to be diagnosed earlier using AI in DBT screen-reading.