Histopathology最新文献

Aims: Contralateral axillary lymph node metastasis (CAM) is a rare clinical condition in patients with breast cancer (BC). CAM can be either a locoregional event or a distant metastasis. Molecular application for clonal evolution in BC has not been reported in CAM cases.

Methods: We studied six patients with CAM with clinical, pathological and/or molecular evidence of distant metastasis; those patients had poor outcomes.

Results: Two cases with molecular analysis of paired primary and CAM established clonal evolution of the CAM with its corresponding primary with additional molecular alteration, increased tumour mutation burden, and copy number variations (CNVs) in the CAMs. Four cases containing alterations from genes potentially modulate chromatin organization, supporting chromatin and subsequent transcriptional signature changes are essential in CAM. Molecular analysis is critical to establish the connection between CAM and its primary counterpart. Distant CAM shows clonal evolution compared with its corresponding primary with additional molecular alterations, increased mutation burden and/or copy number variations.

Conclusion: CAM should be evaluated individually and handled in a personalized fashion. Evidence of a true metastatic CAM can be supported by distant metastasis to other organs, specific morphological features and/or clonal evolution.

Lung cancer is the leading cause of cancer related deaths worldwide, although some patients with early-stage disease can be cured with surgical resection. Standardised reporting of all clinically relevant pathological parameters is essential for best patient care and is also important for ongoing data collection and refinement of important pathological features that impact patient prognosis, staging and clinical care. Using the established International Collaboration on Cancer Reporting (ICCR) procedure, a representative international expert panel of nine lung pathologists as well as an oncologist was convened. Essential core elements and suggested non-core elements were identified for inclusion in the resected lung cancer pathology data set based on predetermined levels of evidence as well as consensus expert opinion. A lung cancer histopathology reporting guide was developed that includes relevant clinical, macroscopic, microscopic and ancillary testing. Critical review and discussion of current evidence was incorporated into the new data set including changes from the 2021 World Health Organisation (WHO) Classification of Thoracic Tumours, fifth edition, new requirements for grading invasive non-mucinous adenocarcinomas, assessment of response to neoadjuvant therapy and requirements for molecular testing in early-stage resected lung carcinomas. This ICCR data set represents incorporation of all relevant parameters for histology reporting of lung cancer resection specimens. Routine use of this data set is recommended for all pathology reporting of resected lung cancer and it is freely available worldwide on the ICCR website (https://www.iccr-cancer.org/datasets/published-datasets/). Widespread implementation will help to ensure consistent and comprehensive pathology reporting and data collection essential for lung cancer patient care, clinical trials and other research.

Aims: Giant cell interstitial pneumonia (GIP) is a fibrosing lung disease histologically characterized by centrilobular pulmonary fibrosis and cannibalistic intra-alveolar multinucleated giant cells. It is considered a form of pneumoconiosis caused particularly by secondary exposure to hard metals (cemented carbide or tungsten carbide). Hard metals are commonly used in various industrial applications, such as cutting tools, drilling tools, machine inserts, and other wear-resistant components. However, cases with unknown exposure that recurred in transplanted lungs have been described. This has led to the hypothesis of a complex etiopathogenesis, likely multifactorial, involving the coparticipation of immune mechanisms. We aimed to identify all the elements present in a series of GIP lung samples to better understand the pathogenic mechanisms of the disease.

Methods and results: We describe five cases of histologically diagnosed GIP in patients with occupational exposure to metallic dust using ultrastructural characterization to identify metal dust and to quantify asbestos fibres. We found that tungsten was present in three cases, albeit in trace amounts in two of them. Numerous elements were identified in all samples, including asbestos fibres in patients with endstage pulmonary fibrosis. Furthermore, in one of the described cases the recurrence of the disease was also observed in transplanted lungs.

Conclusion: These findings support the hypothesis that GIP may be due to elements other than hard metals, with asbestos possibly representing a contributory factor in the expression of a more severe fibrotic disease. The recurrence of GIP observed in transplanted organs strengthens the hypothesis of the existence of a not yet fully understood etiopathogenic immune mechanism.

Immune deficiency and dysregulation-associated lymphoproliferative disorders and lymphomas (IDD-LPDs) encompass a heterogeneous clinical and pathological spectrum of disorders that range from indolent lymphoproliferations to aggressive lymphomas. They arise in a variety of clinical settings and are associated with oncogenic viruses such as the Epstein-Barr virus (EBV) and Kaposi sarcoma-associated herpesvirus/human herpes virus (KSHV/HHV8) in some, but not all, cases. The recognition of IDD-LPDs as distinct from LPDs in immune competent patients is essential to tailor clinical management options for affected patients. The 5th edition of the World Health Organisation classification has introduced an integrated classification of IDD-LPDs with the goal of standardising diagnoses among different settings to enhance clinical decision support. In parallel, new knowledge in the field, particularly surrounding the role of oncogenic viruses and the tumour microenvironment, has led to clearer understanding of the complex pathogenesis of IDD-LPDs and how these features can be precisely harnessed for therapeutic purposes. In this perspective, we highlight the need for multidisciplinary decision-making to augment patient care as well as key areas where evolving concepts offer challenges and opportunities for clinical management, research and future iterations of the classification.

The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures. From diagnostic assistance to powerful research tools, the potential fields of application of machine learning techniques in pathology are vast and constitute the subject of considerable research work. The aim of this article is to provide an overview of the potential applications of AI in the field of haematopathology and to define the role that these emerging technologies could play in our laboratories in the short to medium term.

Myeloid neoplasms include myeloproliferative and myelodysplastic neoplasms and acute myeloid leukaemia. Historically, these diseases have been diagnosed based on clinicopathological features with sometimes arbitrary thresholds that have persisted even as molecular features were gradually incorporated into their classification. As such, although current diagnostic approaches can classify the majority of myeloid neoplasms accurately using a combination of molecular and clinicopathological features, some areas of overlap persist and occasionally pose diagnostic challenges. These include overlap across BCR::ABL1-negative myeloproliferative neoplasms; between clonal cytopenia of undetermined significance and myelodysplastic neoplasms; myelodysplastic/myeloproliferative neoplasms; and, detection of KIT mutations in myeloid neoplasms other than mastocytosis, raising the prospect of systemic mastocytosis. Molecular testing has become state of the art in the diagnostic work-up of myeloid neoplasms, and molecular patterns can inherently help to classify overlapping entities if considered within a framework of haematological presentations. For future development, molecular testing will likely include whole genome and transcriptome sequencing, and primarily molecular classifications of myeloid neoplasms have already been suggested. As such, genetically defined groups should still constitute the basis for our understanding of disease development from early onset to progression, while clinicopathological features could then be used to describe the stage of the disease rather than the specific type of myeloid neoplasm.

The Gleason score is the gold standard for grading of prostate cancer (PCa) and is assessed by assigning specific grades to different microscopical growth patterns. Aside from the Gleason grades, individual growth patterns such as cribriform architecture were recently shown to have independent prognostic value for disease outcome. PCa grading is performed on static tissue samples collected at one point in time, whereas in vivo epithelial tumour structures are dynamically invading, branching and expanding into the surrounding stroma. Due to the lack of models that are able to track human PCa microscopical developments over time, our understanding of underlying tissue dynamics is sparse. We postulate that human PCa expansion utilizes embryonic and developmental tubulogenetic pathways. The aim of this study is to provide a comprehensive overview of developmental pathways of normal epithelial tubule formation, elongation, and branching, and relate those to the static microscopical PCa growth patterns observed in daily clinical practise. This study could provide a rationale for the discerned pathological interobserver variability and the clinical outcome differences between PCa growth patterns.