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This case report describes the case of a man in his seventies presenting with a nasopharyngeal deposit of myeloid sarcoma associated with acute monomyelocytic leukaemia. He presented with right nasal obstruction associated with unilateral pulsatile tinnitus. CT and MRI scans of sinuses identified a moderately restricting mucosal swelling of the right torus tubarius, and a biopsy of the lesion diagnosed a nasal deposit of myeloid sarcoma. [ABSTRACT FROM AUTHOR]

Summary: The diagnosis of myeloproliferative neoplasms (MPN) requires the integration of clinical, morphological, genetic and immunophenotypic findings. Recently, there has been a transformation in our understanding of the cellular and molecular mechanisms underlying disease initiation and progression in MPN. This has been accompanied by the widespread application of high‐resolution quantitative molecular techniques. By contrast, microscopic interpretation of bone marrow biopsies by haematologists/haematopathologists remains subjective and qualitative. However, advances in tissue image analysis and artificial intelligence (AI) promise to transform haematopathology. Pioneering studies in bone marrow image analysis offer to refine our understanding of the boundaries between reactive samples and MPN subtypes and better capture the morphological correlates of high‐risk disease. They also demonstrate potential to improve the evaluation of current and novel therapeutics for MPN and other blood cancers. With increased therapeutic targeting of diverse molecular, cellular and extra‐cellular components of the marrow, these approaches can address the unmet need for improved objective and quantitative measures of disease modification in the context of clinical trials. This review focuses on the state‐of‐the‐art in image analysis/AI of bone marrow tissue, with an emphasis on its potential to complement and inform future clinical studies and research in MPN. [ABSTRACT FROM AUTHOR]

Keywords: Castleman disease; interleukins; POEMS; KSHV/HHV8; HIV; TAFRO EN Castleman disease interleukins POEMS KSHV/HHV8 HIV TAFRO 328 337 10 11/01/21 20211101 NES 211101 Methodology This "Good Practice Paper" was compiled according to the British Society for Haematology (BSH) process at (http://www.b-s-h.org.uk/guidelines). Despite the success of IL-6-signalling blockade, there remains a significant unmet clinical need not only for patients who are intolerant of anti-IL-6 therapy, but also for a reliable disease-modifying therapy.50 Tocilizumab is a monoclonal antibody that antagonises the IL-6 receptor (IL-6R) to block signal transduction.57 Tocilizumab is licensed in Japan, but not in the UK, for the treatment of iMCD-TAFRO. Survival rates for MCD are not clear given changes in in diagnostic criteria, reporting and therapy.7 Nevertheless, the 5-year OS rate for MCD have been estimated at ˜65%.4 Human herpesvirus 8-associated MCD (HHV8-MCD) A proportion of cases of MCD have been attributed to infection with HHV8. The choice of therapy in MCD depends on the presentation of the patient as well as identification of drivers of disease: HHV8-MCD, POEMS-MCD, iMCD (NOS and TAFRO). [Extracted from the article]

Summary Diagnosis of essential thrombocythaemia (ET) is challenging in patients lacking JAK2/CALR/MPL mutations. In a retrospective evaluation of 320 patients with ‘triple‐negative thrombocytosis’, we assessed utility of bone marrow histology (90.9% of patients) and myeloid gene panel (MGP, 55.6%). Supportive histology (‘myeloproliferative neoplasm‐definite/probable’, 36.8%) was associated with higher platelet counts and varied between centres. 14.6% MGP revealed significant variants: 3.4% JAK2/CALR/MPL and 11.2% other myeloid genes. Final clinical diagnosis was strongly predicted by histology, not MGP. 23.7% received cytoreduction (17.6% under 60 years). Real‐world ‘triple‐negative’ ET diagnosis currently depends heavily on histology; we advocate caution in MGP‐negative cases and that specific guidelines are needed. [ABSTRACT FROM AUTHOR]

We present a multi-scale graphical network that can capture the relevant representations of individual cell morphology, topological structure of cell communities in a tissue image, as well as whole slide level attributes. This helps to effectively merge the disease relevant cell morphology to the overall topological context within the sample, within one unified deep framework. From the explainability point of view, instead of empirical design, the graphs are designed with biomedical considerations in mind in order to have translational validity. We also provide a clinically interpretable visualisation of the cells and their micro- and macro-environment by leveraging label noise reduction. We demonstrate the efficacy of our methodology on myeloproliferative neoplasms (MPN), a haematopoietic stem cell disorder as an exemplar test case. The proposed method achieves an encouraging performance in the robust separation of different MPN subtypes in this exciting new dataset as part of this work.

Image-based cell phenotyping is an important and open problem in computational pathology. The two principal challenges are: 1) making the cell cluster properties insensitive to experimental settings (like seed point and feature selection) and 2) ensuring that the phenotypes emerging are biologically relevant and support clinical reporting. To gauge robustness, we first compare the consistency of the phenotypes using self-supervised and supervised features. Through case classification, we analyse the relevance of the self-supervised and supervised feature sets with respect to the clinical diagnosis. In addition, we demonstrate how we can add model explainability through Shapley values to identify more disease relevant cellular phenotypes and measure their importance in context of the disease. Here, myeloproliferative neoplasms, a haematopoietic stem cell disorder, where one particular cell type is of diagnostic relevance is used as an exemplar. The experiments conducted on a set of bone marrow trephines demonstrate an improvement of 7.4 % in accuracy for case classification using cellular phenotypes derived from the supervised scenario.