Citations

The 6th edition of the WHO Classification of Breast Tumours introduces both major and minor changes based on recent advances in our understanding of breast biology, developments in diagnostic modalities, identification of specific molecular targets and new treatment regimens necessitating modifications to pathology reporting and tumour biomarker categorisation. This review summarises the main changes that strive towards a classification of global relevance. In invasive carcinoma, predictive factors increasingly inform modern breast cancer treatment. The 6th edition provides an update on HER2 reporting categories following the DESTINY‐Breast 04 and 06 trials. Terminologies used to classify invasive tumours are clarified, with the term ‘variant’ now reserved for molecular/genetic alterations. Invasive lobular carcinoma (ILC) with extracellular mucin is recognised as a new diagnostic entity with prognostic implications. The diagnosis of mucinous carcinoma (MC) is reserved for mucin secreting carcinomas with grade 1 or 2 morphology and a favourable biomarker profile. The diagnosis of malignant phyllodes tumours requires only four of the original five adverse histological criteria. Classification of neuroendocrine tumours (NETs) is revised, recognising that the unified model, promoted in the 5th edition, is difficult to apply to the breast. New approaches to the classification of adenomyoepithelioma are discussed but the 5th edition system is broadly retained. A new section on ‘Small Diagnostic Samples’ outlines the merits of non‐operative biopsy diagnosis, the B coding system and the importance of multidisciplinary review. Changes to diagnostic practice and the emerging role of artificial intelligence, with advantages and challenges, are discussed in a new section on ‘Digital Pathology’.

Background Breast phyllodes tumours (PT) are rare biphasic neoplasms consisting of epithelial and stromal components. They are classified into benign, borderline and malignant categories. Diagnosing and grading PTs present a challenge for pathologists, as there are multiple histological parameters and their own tiers. We aim to investigate the potential role of artificial intelligence (AI) as a diagnostic aid in determining PT grade. Materials and methods We investigated 15 PT whole slide images (WSIs), comprising 5 benign, 5 borderline and 5 malignant cases. We sought to classify and retrieve the most relevant WSIs by matching histological features at different patch sizes, using the Yottixel framework for WSI processing. Patches were extracted at a 20× magnification level. We then used the KimiaNet to extract feature vectors and transformed these into barcode representations. Barcodes of a query WSI were compared with those of others in the archive, allowing us to identify the most similar WSI and determine the PT grades from histological similarities. Results We utilized ‘majority‐n accuracy’ as a measure of correctness, that is when the majority of the top‐n search results have the correct diagnosis as the query patient. We achieved a maximum reported accuracy of 67%, with a 3000 × 3000 patch size when grading PT at majority voting with n = 4. Conclusion Despite the small sample size and absence of fine‐tuning, our study demonstrated the potential of AI‐based PT grade stratification using various patch sizes through histological matching. This serves as a preliminary proof of concept, with the prospect of refinement for potential routine clinical application.

Tumor-infiltrating lymphocytes (TILs) are prognostic in invasive breast cancer. However, their prognostic significance in ductal carcinoma in situ (DCIS) has been controversial. To investigate the prognostic role of TILs in DCIS outcome, we used different scoring methods for TILs in multi-national cohorts from Asian and European women. Self-described race was genetically confirmed using QC Infinium array combined with radmixture software. Stromal TILs, touching TILs, circumferential TILs, and hotspots were quantified on H&E-stained slides and correlated with the development of second breast cancer events (BCE) and other clinico-pathological variables. In univariate survival analysis, age older than 50 years, hormone receptor positivity and the presence of circumferential TILs were weakly associated with the absence of BCE at the 5-year follow-up in all cohorts (p < 0.03; p < 0.02; and p < 0.02, respectively, adjusted p = 0.11). In the multivariable analysis, circumferential TILs were an independent predictor of a better outcome (Wald test p = 0.01), whereas younger age was associated with BCE. Asian patients were younger with larger, higher grade, HR negative DCIS lesions, and higher TIL variables. The spatial arrangement of TILs may serve as a better prognostic indicator in DCIS cases than stromal TILs alone and may be added in guidelines for TILs evaluation in DCIS.