ArticleInsights into imaging2026
Why we still miss breast cancers: strategies for improving mammography interpretation.
Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Cognitive Tunneling in Obstetrics and Gynecology: A Critical Review of Implications for Clinical Practice and Mitigation Strategies.Diseases (Basel, Switzerland) · 2026Review
- Contrast-enhanced mammography in the preoperative assessment of primary operable breast cancer: Results from a prospective, two-centre study.European journal of radiology open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnostic errors in mammography-particularly missed or delayed breast cancer detection-have a substantial impact on patient outcomes. These misdiagnoses remain a leading cause of malpractice claims in radiology, underscoring their serious clinical and legal implications. Contributing factors to errors in breast imaging include reader-related cognitive biases, lesion characteristics, patient-specific variables, and technical limitations. To address these challenges, a systematic approach is essential. Key strategies include structured error recognition, peer review processes, and robust quality assurance programs. Educational initiatives and system-level interventions-such as structured training, continuous feedback loops, and the integration of AI-driven computer-aided detection (CAD) tools-can significantly reduce diagnostic errors and enhance accuracy in breast imaging interpretation. This article aims to highlight common pitfalls in mammography, analyze root causes, and propose practical strategies for improvement. Real-life cases of missed diagnoses are included to reinforce key learning points and support radiologists in improving diagnostic precision and improving patient care. CRITICAL RELEVANCE STATEMENT: Missed or delayed breast cancer diagnoses stem from multiple factors. A multi-pronged strategy-combining peer review, bias mitigation, education, supportive environments, and AI tools-can improve diagnostic accuracy and enhance interpretive accuracy and advance quality standards in breast imaging practice. KEY POINTS: Missed or delayed breast cancer diagnoses on mammography continue to be a significant source of diagnostic error with serious clinical and medico-legal consequences. Contributing factors to missed or delayed breast cancer diagnoses include cognitive biases, subtle lesion characteristics, patient-specific variables, and technical limitations. Structured peer review, double reading, and robust quality assurance programs can reduce interpretive variability and improve diagnostic performance. Educational initiatives and AI-driven tools, such as computer-aided detection (CAD), support error reduction and enhance accuracy in breast imaging interpretation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.