Invasive lobular carcinoma (ILC) is the second most common breast cancer subtype but remains underrecognized as a distinct clinical entity, partly due to its underrepresentation in studies. Growing interest in ILC-specific research highlights ongoing challenges in diagnosis, classification, and biological understanding, including the roles of immunohistochemistry (IHC) and artificial intelligence (AI).
Histologically ILC can be roughly subdivided in tumors showing a classic architectural pattern (classic-ILC), and tumors showing alternative architectural and/or cytological patterns. Classic ILC has well-defined histological features, whereas ILC with alternative patterns lack standardized criteria, leading to variability in diagnosis and reporting. The clinical significance of ILC with alternative patterns is matter of debate, and Nottingham grading system should be applied regardless of cyto-architectural features in ILC. Diagnostic difficulty is compounded by overlap with other breast lesions, tumors with mixed morphology or ambiguous architectural features, resulting in high discordance rates between pathologists.
Molecularly, ILC is characterized by disruption of the E-cadherin adhesion complex, usually due to CDH1 gene alterations, resulting in loss of cell cohesion. While loss of E-cadherin is typical, some cases show aberrant or retained expression, complicating diagnosis. IHC—especially combined E-cadherin and catenin staining—can improve diagnostic consistency when interpreted alongside morphology.
AI shows promise in distinguishing ILC and predicting molecular features, but current models perform better for classic than non-classic ILC, likely due to limited training data. Overall, improved classification, integration of morphology, molecular data, and advanced tools are needed to enhance ILC diagnosis and clinical management.

The WHO Classification of Tumours 6th edition, is an update of the 2019 5th edition. It 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.

The methodology has been strengthened:

1.An end-user survey is conducted before each book and results are taken into consideration on the volume; 
2.Updates are based on current research evidence - Evidence based pathology;
3.To increase the books accessibility each volume has an editor from a resource challenged setting and suggestions from the subcommittee on these issues are considered; 
4.Attempts to increase harmonisation across volumes are being made; 
5.Tumours are as refined and clarified as possible
6.WSI’s are included for almost all entities.

The 6th edition provides major updates in the following areas:
·      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’.