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’.

Abstract Background Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality – for the specific purpose of assessing AI/ML improvements – is currently missing. Methods A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on “ AI in the Laboratory of the Future” prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. Results The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. Conclusions A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.