Diagnostic error in breast pathology remains an important but often misunderstood aspect of routine practice. While diagnostic concordance is a key quality metric, it is essential to distinguish acceptable interobserver variability from true error and professional negligence, as these have different clinical, quality assurance, and medicolegal implications. Diagnostic errors arise across the diagnostic pathway and are typically multifactorial, reflecting interactions between cognitive interpretation, technical processes, and system-level factors rather than isolated individual failure. In breast pathology, diagnostic discordance and errors are more likely to occur in challenging or borderline lesions, limited sampling, or when clinical–radiological correlation is incomplete. Although overall diagnostic accuracy is high, even low error rates may have significant implications given the volume of cases and the potential impact on patient management.
This presentation will review the types, causes, and clinical impact of diagnostic discordance and error in breast pathology, with emphasis on distinguishing error from acceptable variability. It will also explore contributory factors, including case complexity, workload, and system design, alongside strategies to mitigate risk, including structured reporting, multidisciplinary engagement, second opinion pathways, and quality assurance processes. The evolving role of digital pathology and artificial intelligence will also be discussed, highlighting their potential to improve detection, consistency, and quality monitoring, while introducing new diagnostic and system-related challenges.

This talk will review key updates in the role of the tumour micro-environment in breast cancer concentrating on both immune cells and stromal cells.