Histopathology最新文献

Follicular lymphoma (FL) is the second most common type of lymphoma (20% of all non-Hodgkin lymphomas), derived from germinal centre (GC) B cells, and is characterised by its significant clinical, prognostic and biological heterogeneity, leading to complexity in management. Despite significant biological investigation and indisputable clinical progress since the advent of the immunotherapy era more than 20 years ago, much remains to be done to understand and cure this lymphoma. Today, FL is metaphorically a giant puzzle on the table with patches of sky, landscape and foliage clearly appearing. However, many of the remaining pieces are held by various stakeholders (e.g. clinicians, pathologists, researchers, drug developers) without global agreement on what the gaps are, or any clear blueprint on how to solve the puzzle of understanding the heterogeneity of this disease and create curative and tailored therapies. With the advent of new investigation and drug technologies, together with recent advances in our capacity to manage big data, the time seems ripe for a change of scale. More than ever, this will require collaboration between and within all stakeholders to overcome the current bottlenecks in the field. As for every investigator, we acknowledge that this first draft is necessarily biased, incomplete and some FL expert readers might recognise some remaining gaps not addressed. We hope they will reply to make this effort a collaborative one to assemble all the pieces in the most ideal fashion. As such, this review intends to be a first step and an interactive platform to a collaborative roadmap towards better understanding and care of FL.

Myelodysplastic syndromes/neoplasms (MDS) and acute myeloid leukaemia (AML) are neoplastic haematopoietic cell proliferations that are diagnosed and classified based on a combination of morphological, clinical and genetic features. Specifically, the percentage of myeloblasts in the blood and bone marrow is a key feature that has historically separated MDS from AML and, together with several other morphological parameters, defines distinct disease entities within MDS. Both MDS and AML have recurrent genetic abnormalities that are increasingly influencing their definitions and subclassification. For example, in 2022, two new MDS entities were recognised based on the presence of SF3B1 mutation or bi-allelic TP53 abnormalities. Genomic information is more objective and reproducible than morphological analyses, which are subject to interobserver variability and arbitrary numeric cut-offs. Nevertheless, the integration of genomic data with traditional morphological features in myeloid neoplasm classification has proved challenging by virtue of its sheer complexity; gene expression and methylation profiling also can provide information regarding disease pathogenesis, adding to the complexity. New machine-learning technologies have the potential to effectively integrate multiple diagnostic modalities and improve on historical classification systems. Going forward, the application of machine learning and advanced statistical methods to large patient cohorts can refine future classifications by advancing unbiased and robust previously unrecognised disease subgroups. Future classifications will probably incorporate these newer technologies and higher-level analyses that emphasise genomic disease entities over traditional morphologically defined entities, thus promoting more accurate diagnosis and patient risk stratification.

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.

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.