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Aims Materials and methods Results Conclusions This study evaluates the utility of breast specific markers in identifying breast cancer subtypes within metastatic settings. The subtype alteration in metastatic disease and its consequent impact on breast‐specific marker expression is also examined.GATA‐binding protein 3 (GATA3), mammaglobin (MMG), transcriptional repressor GATA binding 1 (TRSP1) and SRY‐box transcription factor 10 (SOX10) expression were assessed in a large cohort of metastatic breast cancer (MBC) cases and correlated with the characteristics of both MBC and primary breast cancer (PBC).GATA3 was the most sensitive in MBC (83.1%), followed by TRPS1 (77.0%), MMG (58.5%) and SOX10 (7.1%). This trend was consistent in hormonal receptor (HR)+ and HR− MBC. Combining GATA3/TRPS1 yielded the highest detection rates in the overall cohort (90.1%) and HR+ MBC (97.1%), while TRSP1/MMG was most effective in HR− (76.2%) and TN (71.1%) MBC. Marker expression did not correlate with metastatic site, except SOX10 in lung metastases (P = 0.031). Subtype discordance between MBC and PBC occurred in 43 cases (24.4%), with GATA3 expression in HR− MBC significantly linked to subtype discordance (P = 0.005). Conversely, SOX10 expression was significantly associated with subtype concordance in HR− and TNBC (P ≤ 0.003). Despite a higher expression of GATA3 in all HR− cases, TRSP1 outperformed GATA3 in detecting concordant HR− cases (64.0% versus 38.5%). TRPS1 and SOX10 were expressed in more than 50% of concordant TNBC cases.The expression of breast‐specific markers is mainly determined by the PBC subtype. GATA3 retains high sensitivity in HR+ cancers, even after HR loss during metastasis. TRPS1 and SOX10 are identified as valuable markers in TNBC metastasis. [ABSTRACT FROM AUTHOR]

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists—namely, molecular and hormonal analysis, grading, mitotic figure counting, ki‐67 indexing, tumour‐infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open‐source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature. [ABSTRACT FROM AUTHOR]