Why we still miss breast cancers - strategies for improving mammography interpretation

Sep 29, 2026

Uploaded by Levi

Diagnostic errors in mammography—especially missed or delayed breast cancer detection—substantially affect patient outcomes and remain a major driver of malpractice claims in radiology, with serious clinical and medico-legal consequences. Error contributors span reader-related cognitive biases, subtle lesion features, patient-specific variables, and technical limitations. A multipronged response is required, combining structured error recognition, peer review, and robust quality assurance. Educational initiatives and system interventions, including AI-driven CAD tools and continuous feedback, reduce interpretive variability and improve diagnostic accuracy.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Abstract

Critical relevance statement

Key points

Introduction

Reader factors

Chotai et al. Insights into Imaging (2026)17:8 [https://doi.org/10.1186/s13244-025-02168-2](https://doi.org/10.1186/s13244-025-02168-2) EDUCATIONAL REVIEW Open Access Why we still miss breast cancers: strategies for improving mammography interpretation Niketa Chotai 1* , Aishwarya Gadwal2, Divya Buchireddy3 and Wei Tse Yang4 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 multipronged 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. Keywords Missed breast cancer, Mammogram, Strategies, False negative *Correspondence: Niketa Chotai [niketachotai@gmail.com](niketachotai@gmail.com) Full list of author information is available at the end of the article © The Author(s) 2025 . Open Access This article is licensed under a Creative Commons Attribution 4 . 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/](http://creativecommons.org/)[ ](http://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/.)[.](licenses/by/4.0/.) Graphical Abstract Why we still miss breast cancers: strategies for improving mammography interpretation AI-assisted detection of a small right breast asymmetry with macrocalcifications—initially missed on scre

FAQs

Why do missed or delayed breast cancer diagnoses on mammography remain a major problem?

They continue to be a significant source of diagnostic error, leading to serious clinical impacts and medico-legal consequences. The document links these outcomes to diagnostic errors in mammography, particularly missed or delayed detection.

What factors contribute to diagnostic errors in breast imaging?

Contributing factors include reader-related cognitive biases, subtle lesion characteristics, patient-specific variables, and technical limitations. The text also distinguishes perception errors from interpretation errors.

What strategies does the article recommend to improve mammography interpretation?

Recommended strategies include structured error recognition, structured peer review (including double reading), and robust quality assurance programs. It also highlights educational initiatives and system-level interventions such as continuous feedback loops and AI-driven CAD tools.

Sep 29, 2026

Uploaded by Levi