Por qué todavía pasamos por alto los cánceres de mama: estrategias para mejorar la interpretación de la mamografía

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.