Acquired amegakaryocytic thrombocytopenia (AATP) is a rare disorder in which severely low platelet levels occur due to reduced or complete absence of megakaryocytes in the bone marrow. The pathophysiology of this disease is not fully understood, although anti-thyroid peroxidase antibodies (anti-TPO) binding to cellular-myeloproliferative leukemia (c-mpl) receptors is a proposed mechanism. Currently, no standard published guideline for treatment exists, but immunosuppressive therapies have been used based on the proposed mechanism and associated conditions. We present a case of a 57-year-old male who presented to the hospital with a 3-day history of progressive weakness and dysphagia. He had recently been discharged from an outside health system after evaluation for suspected gastrointestinal bleeding, although esophagogastroduodenoscopy and colonoscopy did not uncover a source of bleeding. Fifteen days later, he was admitted to our hospital for septic shock and acute renal failure with suspected lower gastrointestinal bleeding (melena on presentation). He was found to have a rapidly declining platelet count with a nadir of 0. Due to severe thrombocytopenia, filgrastim was administered. A bone marrow biopsy revealed findings consistent with amegakaryocytosis with otherwise preserved cell lines. Hematologic labs improved with the initiation of appropriate treatment for severe sepsis. After performing an extensive workup, the likely etiology of transient AATP in this case was severe sepsis-induced immune dysregulation and bone marrow suppression.

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Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.

Primary pulmonary Hodgkin\'s lymphoma (PPHL) is a rare subtype of lymphoma that comprises a small percentage of primary pulmonary lymphomas. Due to its rarity and nonspecific symptoms, PPHL often presents diagnostic challenges. This case report presents a unique case of PPHL mimicking granulomatosis with polyangiitis, emphasizing the difficulties encountered during the diagnostic process. A 53-year-old female presented with vague symptoms including weakness, oedema, dry cough, and nasal cavity ulceration. Laboratory investigations revealed elevated C-reactive protein levels, a white blood cell count with neutrophilia, and lymphopaenia. Initial treatment with oral corticosteroids for suspected polyangiitis yielded no response. The patient subsequently developed a low-grade fever and pruritic erythematous rash. Diagnostic procedures, including bronchial brush biopsy, bronchial washing, mediastinal lymph node biopsy, nasal cavity ulceration biopsy, and initial lung biopsy, were inconclusive and resulted in exclusion of granulomatosis with polyangiitis. A subsequent computed tomography scan indicated disease progression in the left lung. A lung biopsy revealed fibrotic tissue with nodules containing Hodgkin- Reed-Sternberg cells, leading to the final diagnosis of classic Hodgkin lymphoma, nodular sclerosis subtype. Positron emission tomography scan findings confirmed PPHL. The patient received multiple chemotherapeutic regimens, with brentuximab vedotin demonstrating efficacy as the sole effective treatment. This exceptional case of PPHL underscores the extensive diagnostic and therapeutic workup involving a multidisciplinary team of clinicians, radiologists, and pathologists. Increased awareness of PPHL and its distinctive features will aid in the diagnosis of similar cases in the future, benefitting both clinicians and pathologists.

Deep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce novel, automatically generated visual summaries of bone marrow aspirate specimens called cell projection plots (CPPs). Encompassing relevant biological patterns such as neutrophil maturation, CPPs provide a compact summary of bone marrow aspirate cytology. To gauge clinical relevance, CPPs were inspected by 3 hematopathologists, who decided whether corresponding diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with a matching degree of 85%. Our finding suggests CPPs can represent clinically relevant information from bone marrow aspirate specimens and may be used to efficiently summarize bone marrow cytology to pathologists. CPPs could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic-support tool for digital pathology workflows.

The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist\'s horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field.

Fibroblastic reticulum cell tumor (FRCT) is a rare dendritic neoplasm arising from fibroblastic reticulum cells (FBRCs) and exhibiting peculiar cytokeratin expression. FRCTs usually involve the lymph nodes, although they can also be encountered in the spleen and soft tissues. FRCTs are composed of mildly atypical spindle or ovoid cells, arranged in loose whorls, which express almost invariably low-weight cytokeratins, smooth muscle actin, and CD68. An admixed lymphoplasmacytic infiltrate is also frequently present in solid organ sites. The clinical picture may vary from very indolent to aggressive disease exhibiting features of malignancy, such as cytological pleomorphism, necrosis, or high mitotic rate and metastatic potential. FRCT is a challenging diagnosis, due to its rarity and deceptive cytokeratin expression. Hereafter, we revise the most recent literature regarding such condition and report the case of an extremely indolent splenic FRCT, with no features of malignancy.

Hematopoietic disorders are serious diseases that threaten human health, and the diagnosis of these diseases is essential for treatment. However, traditional diagnosis methods rely on manual operation, which is time consuming and laborious, and examining entire slide is challenging. In this study, we developed a weakly supervised deep learning method for diagnosing malignant hematological diseases requiring only slide-level labels. The method improves efficiency by converting whole-slide image (WSI) patches into low-dimensional feature representations. Then the patch-level features of each WSI are aggregated into slide-level representations by an attention-based network. The model provides final diagnostic predictions based on these slide-level representations. By applying the proposed model to our collection of bone marrow WSIs at different magnifications, we found that an area under the receiver operating characteristic curve of 0.966 on an independent test set can be obtained at 10× magnification. Moreover, the performance on microscopy images can achieve an average accuracy of 94.2% on two publicly available datasets. In conclusion, we have developed a novel method that can achieve fast and accurate diagnosis in different scenarios of hematological disorders.

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub.com/doi/10.1177/2374289517715040.1.

Chronic myeloproliferative neoplasms (MPNs) are hematopoietic stem cell neoplasms with driver events including the BCR-ABL1 translocation leading to a diagnosis of chronic myeloid leukemia (CML), or somatic mutations in JAK2, CALR, or MPL resulting in Philadelphia-chromosome-negative MPNs with constitutive activation of the JAK-STAT signaling pathway. In the Philadelphia-chromosome-negative MPNs, modern sequencing panels have identified a vast molecular landscape including additional mutations in genes involved in splicing, signal transduction, DNA methylation, and chromatin modification such as ASXL1, SF3B1, SRSF2, and U2AF1. These additional mutations often influence prognosis in MPNs and therefore are increasingly important for risk stratification. This review focuses on the molecular alterations within the WHO classification of MPNs and laboratory testing used for diagnosis.