ArticleJournal of the National Cancer Institute2025
Mammographic classification of interval breast cancers and artificial intelligence performance.
Article in Journal of the National Cancer Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Can AI models detect abnormality in screening mammography years before confirmed diagnosis?European radiology · 2026Article
- Missed and true interval cancers on digital breast tomosynthesis screening mammograms: radiologists' assessment and artificial intelligence markings.Acta radiologica open · 2026Article
- Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.European radiology · 2026Article
- Artificial intelligence for breast cancer prevention: the vision ahead.European radiology · 2026Review
- Clinically Meaningful AI Detection of Interval Breast Cancer at Digital Breast Tomosynthesis Screening.Radiology · 2025Article
- "Screening isn't Your Ticket to Vacation": In-depth Interviews With Women in the United States Who Experienced an Interval Breast Cancer.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
13 authors.
Funding
Abstract
backgroundEuropean studies suggest that artificial intelligence (AI) can reduce interval breast cancers. Research on interval breast cancer classification and AI's effectiveness in the United States, however, particularly using digital breast tomosynthesis and annual screening, is limited. We aimed to mammographically classify interval breast cancers and assess AI performance using a 12-month screening interval.
methodsFrom digital mammography and digital breast tomosynthesis screening mammograms acquired between 2010 and 2019 at a US tertiary-care academic center, we identified interval breast cancers diagnosed less than 12 months after a negative mammogram. At least 3 breast radiologists retrospectively classified interval breast cancers as missed-reading error, minimal signs-actionable, minimal signs-nonactionable, true interval, occult, or missed-technical error. A deep-learning AI tool assigned risk scores ranging from 1 to 10 to the negative index screening mammograms, with scores of 8 or higher considered "flagged." Statistical analysis evaluated associations among interval breast cancer types and AI exam scores, AI markings, and patient and tumor characteristics.
resultsFrom 184 935 screening mammograms (65% digital mammography, 35% digital breast tomosynthesis), we identified 148 interval breast cancers in 148 women (mean [SD] age = 61 [12] years). Of these, 26% were minimal signs-actionable, 24% were occult, 22% were minimal signs-nonactionable, 17% were missed-reading error, 6% were true interval, and 5% were missed-technical error (P < .001). AI scored 131 mammograms (17 errors excluded); it most frequently flagged exams with missed-reading error (90%), minimal signs-actionable (89%), and minimal signs-nonactionable (72%) (P = .02). AI localized mammographically visible types more accurately (35%-68%) than nonvisible types (0%-50%; P = .02).
conclusionAI more frequently flagged and accurately localized interval breast cancer types that were mammographically visible at screening (missed or minimal signs) compared with true interval or occult cancers.
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