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During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.

The classification of neuroendocrine neoplasms (NENs) differs between organ systems and currently causes considerable confusion. A uniform classification framework for NENs at any anatomical location may reduce inconsistencies and contradictions among the various systems currently in use. The classification suggested here is intended to allow pathologists and clinicians to manage their patients with NENs consistently, while acknowledging organ-specific differences in classification criteria, tumor biology, and prognostic factors. The classification suggested is based on a consensus conference held at the International Agency for Research on Cancer (IARC) in November 2017 and subsequent discussion with additional experts. The key feature of the new classification is a distinction between differentiated neuroendocrine tumors (NETs), also designated carcinoid tumors in some systems, and poorly differentiated NECs, as they both share common expression of neuroendocrine markers. This dichotomous morphological subdivision into NETs and NECs is supported by genetic evidence at specific anatomic sites as well as clinical, epidemiologic, histologic, and prognostic differences. In many organ systems, NETs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, and/or the presence of necrosis; NECs are considered high grade by definition. We believe this conceptual approach can form the basis for the next generation of NEN classifications and will allow more consistent taxonomy to understand how neoplasms from different organ systems inter-relate clinically and genetically.

Purpose MMP9 is a matricellular protein associated with extracellular matrix (ECM) remodelling, that promotes tumour progression, and modulates the activity of cell adhesion molecules and cytokines. This study aims to assess the prognostic value of MMP9 and its association with cytoskeletal modulators in early-stage invasive breast cancer (BC). Methods MMP9 expression was evaluated by immunohistochemistry using a well-characterised series of primary BC patients with long-term clinical follow-up. Association with clinicopathological factors, patient outcome and ECM remodelling BC-biomarkers were investigated. METABRIC dataset, BC-GenExMiner v4.0 and TCGA were used for the external validation of MMP9 expression. GSEA gene enrichment analyses were used to evaluate MMP9 associated pathways. Results MMP9 immunopositivity was observed in the stroma and cytoplasm of BC cells. Elevated MMP9 protein levels were associated with high tumour grade, high Nottingham Prognostic Index, and hormonal receptor negativity. Elevated MMP9 protein expression correlated significantly with cytokeratin 17 (Ck17), Epidermal Growth Factor Receptor (EGFR), proliferation (Ki67) biomarkers, cell surface adhesion receptor (CD44) and cell division control protein 42 (CDC42). Cytoplasmic MMP9 expression was an independent prognostic factor associated with shorter BC-specific survival. In the external validation cohorts, MMP9 expression was also associated with poor patients’ outcome. Transcriptomic analysis confirmed a positive association between MMP9 and ECM remodelling biomarkers. GSEA analysis supports MMP9 association with ECM and cytoskeletal pathways. Conclusion This study provides evidence for the prognostic value of MMP9 in BC. Further functional studies to decipher the role of MMP9 and its association with cytoskeletal modulators in BC progression are warranted.

Background Breast cancer (BC) is a heterogeneous disease characterised by variant biology and patient outcome. The amino acid transporter, SLC7A5, plays a role in BC although its impact on patient outcome in different BC subtypes remains to be validated. This study aimed to determine whether the clinicopathological and prognostic value of SLC7A5 is different within the molecular classes of BC. Methods SLC7A5 was assessed at the genomic level, using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data ( n = 1980), and proteomic level, using immunohistochemical analysis and tissue microarray (TMA) ( n = 2664; 1110 training and 1554 validation sets) in well-characterised primary BC cohorts. SLC7A5 expression correlated with clinicopathological and biological parameters, molecular subtypes and patient outcome. Results SLC7A5 mRNA and protein expression were strongly correlated with larger tumour size and higher grade. High expression was observed in triple negative (TN), human epidermal growth factor receptor 2 (HER2)+, and luminal B subtypes. SLC7A5 mRNA and protein expression was significantly associated with the expression of the key regulator of tumour cell metabolism, c-MYC, specifically in luminal B tumours only ( p = 0.001). High expression of SLC7A5 mRNA and protein was associated with poor patient outcome ( p < 0.001) but only in the highly proliferative oestrogen receptor (ER)+/ luminal B ( p = 0.007) and HER2+ classes of BC ( p = 0.03). In multivariate analysis, SLC7A5 protein was an independent risk factor for shorter breast-cancer-specific survival only in ER+ high-proliferation tumours ( p = 0.02). Conclusions SLC7A5 appears to play a role in the aggressive highly proliferative ER+ subtype driven by MYC and could act as a potential therapeutic target. Functional assessment is necessary to reveal the specific role played by this transporter in the ER+ highly proliferative subclass and HER2+ subclass of BC.

Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1–70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.

Background In over 20% of breast conserving operations, postoperative pathological assessment of the excised tissue reveals positive margins, requiring additional surgery. Current techniques for intra-operative assessment of tumor margins are insufficient in accuracy or resolution to reliably detect small tumors. There is a distinct need for a fast technique to accurately identify tumors smaller than 1 mm 2 in large tissue surfaces within 30 min. Methods Multi-modal spectral histopathology (MSH), a multimodal imaging technique combining tissue auto-fluorescence and Raman spectroscopy was used to detect microscopic residual tumor at the surface of the excised breast tissue. New algorithms were developed to optimally utilize auto-fluorescence images to guide Raman measurements and achieve the required detection accuracy over large tissue surfaces (up to 4 × 6.5 cm 2 ). Algorithms were trained on 91 breast tissue samples from 65 patients. Results Independent tests on 121 samples from 107 patients - including 51 fresh, whole excision specimens - detected breast carcinoma on the tissue surface with 95% sensitivity and 82% specificity. One surface of each uncut excision specimen was measured in 12–24 min. The combination of high spatial-resolution auto-fluorescence with specific diagnosis by Raman spectroscopy allows reliable detection even for invasive carcinoma or ductal carcinoma in situ smaller than 1 mm 2 . Conclusions This study provides evidence that this multimodal approach could provide an objective tool for intra-operative assessment of breast conserving surgery margins, reducing the risk for unnecessary second operations.

Background Cancer cells must alter their metabolism to support proliferation. Immune evasion also plays a role in supporting tumour progression. This study aimed to find whether enhanced glutamine uptake in breast cancer (BC) can derive the existence of specific immune cell subtypes, including the subsequent impact on patient outcome. Methods SLC1A5, SLC7A5, SLC3A2 and immune cell markers CD3, CD8, FOXP3, CD20 and CD68, in addition to PD1 and PDL1, were assessed by using immunohistochemistry on TMAs constructed from a large BC cohort ( n  = 803). Patients were stratified based on SLC protein expression into accredited clusters and correlated with immune cell infiltrates and patient outcome. The effect of transient siRNA knockdown of SLC7A5 and SLC1A5 on PDL1 expression was evaluated in MDA-MB-231 cells. Results High SLCs were significantly associated with PDL1 and PD1 +, FOXP3 +, CD68 + and CD20 + cells ( p  < 0.001). Triple negative (TN), HER2 + and luminal B tumours showed variable associations between SLCs and immune cell types ( p  ≤ 0.04). The expression of SLCs and PDL1, PD1 +, FOXP3 + and CD68 + cells was associated with poor patient outcome ( p  < 0.001). Knockdown of SLC7A5 significantly reduced PDL1 expression. Conclusion This study provides data that altered glutamine pathways in BC that appears to play a role in deriving specific subtypes of immune cell infiltrates, which either support or counteract its progression.

Background Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients ( n  = 344) treated with lumpectomy at Nottingham University Hospital, UK. Methods The cohort was split case-wise into training ( n  = 159, 31 with 10-year recurrence) and validation ( n  = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk. Results The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3–25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0–13.8), p  < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences ( p  < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p  = 0.0006). Conclusions Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients.

Background Atypical ductal hyperplasia (ADH) is a common diagnosis in the mammographic era and a significant clinical problem with wide variation in diagnosis and treatment. After a diagnosis of ADH on biopsy a proportion are upgraded to carcinoma upon excision; however, the remainder of patients are overtreated. While ADH is considered a non-obligate precursor of invasive carcinoma, the molecular taxonomy remains unknown. Main text Although a few studies have revealed some of the key genomic characteristics of ADH, a clear understanding of the molecular changes associated with breast cancer progression has been limited by inadequately powered studies and low resolution methodology. Complicating factors such as family history, and whether the ADH present in a biopsy is an isolated lesion or part of a greater neoplastic process beyond the limited biopsy material, make accurate interpretation of genomic features and their impact on progression to malignancy a challenging task. This article will review the definitions and variable management of the patients diagnosed with ADH as well as the current knowledge of the molecular landscape of ADH and its clonal relationship with ductal carcinoma in situ and invasive carcinoma. Conclusions Molecular data of ADH remain sparse. Large prospective cohorts of pure ADH with clinical follow-up need to be evaluated at DNA, RNA, and protein levels in order to develop biomarkers of progression to carcinoma to guide management decisions.

Human epidermal growth factor receptor 2 (HER2) overexpression is present in approximately 15% of early invasive breast cancers, and is an important predictive and prognostic marker. The substantial benefits achieved with anti-HER2 targeted therapies in patients with HER2-positive breast cancer have emphasised the need for accurate assessment of HER2 status. Current data indicate that HER2 test accuracy improved following previous publication of guidelines and the implementation of an external quality assessment scheme with a decline in false-positive and false-negative rates. This paper provides an update of the guidelines for HER2 testing in the UK. The aim is to further improve the analytical validity and clinical utility of HER2 testing by providing guidelines of test performance parameters, and recommendations on the postanalytical interpretation of test results. HER2 status should be determined in all newly diagnosed and recurrent breast cancers. Testing involves immunohistochemistry with >10% complete strong membrane staining defining a positive status. In situ hybridisation, either fluorescent or bright field chromogenic, is used either upfront or in immunohistochemistry borderline cases to detect the presence of HER2 gene amplification. Situations where repeat HER2 testing is advised are outlined and the impact of genetic heterogeneity is discussed. Strict quality control and external quality assurance of validated assays are essential. Testing laboratories should perform ongoing competency assessment and proficiency tests and ensure the reliability and accuracy of the assay. Pathologists, oncologists and surgeons involved in test interpretation and clinical use should adhere to published guidelines and maintain accurate performance and consistent interpretation of test results.