1 paper(s) published in this issue.
[01]
Paper ID: IJIRMS-V1I3-001
Missed and true interval cancers on digital breast tomosynthesis screening mammograms: radiologists' assessment and artificial intelligence markings
Khushali Rajendragiri Goswami , Shital Kiran Davangere Padmanabh
| Department of Pediatric and Preventive Dentistry, College of Dental Sciences, Amargadh, Bhavnagar, Gujarat, India
Medical Sciences
Pg. 1–13
Published: 23 Aug 2026
Background: 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. Purpose: 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. Material and methods: 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. Results: 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). Conclusion: About 20% of interval cancers have potential to be diagnosed earlier using AI in DBT screen-reading.
