Association between Megakaryocyte Abnormalities on Bone Marrow Smear and Response to Thrombopoietin Receptor Agonists in Adult Patients with Primary Immune Thrombocytopenia

Introduction: Immune thrombocytopenia (ITP) is an autoimmune disease due to peripheral destruction but also impaired central production of platelets. Autoimmune reaction directed against megakaryocytes (MKs) has been described, and may explain morphological abnormalities of MKs observed in some patients with primary ITP. Thrombopoietin receptor agonists (TPO-RAs) are indicated as second-line treatments for ITP, but no predictive factors of response used in clinical routine practice has been demonstrated. The utility of systematic bone marrow smears (BMS) at ITP diagnosis is discussed. Howerer, it is usually recommended before second-line treatments. Two studies have suggested an association between MK abnormalities and response to corticosteroids in primary ITP, but none have investigated this association for TPO-RAs. This study aimed to investigate the association between MK abnormalities and response to TPO-RAs in adult patients with primary ITP. Methods: The source of population was the CARMEN registry. The CARMEN (Cytopénies Auto-immunes: Registre Midi-PyréneEN) registry is aimed at the prospective follow-up of all incident ITP adults in the French Midi-Pyrénées region (South-West of France, 3 million inhabitants) since June 2013. Each investigator follows all adult patients (aged ≥18 years) with incident ITP in routine visit or hospital stay. ITP was defined by international definition (platelet count <100 x 10 9/L and exclusion of other causes of thrombocytopenia). The study population consisted in all patients included in the CARMEN registry between June 2013 and March 2018 with primary ITP, treated by TPO-RA and with a BMS before initiating TPO-RA. We excluded the patients with a number of MKs <10 MK on the BMS. Morphological abnormalities were established based on literature and defined by consensus among 3 expert cytologists (AR, JBR and VDM). All MKs present on each smear were analyzed. MKs were categorized by the presence of dysplasia (monolobed MK and/or separated nuclei and/or microMKs), and according to their stage of maturation (basophilic, granular and thrombocytogenic). All patients' medical charts were reviewed by two experts in ITP (OW and GM) to determine the response to TPO-RAs. Response was defined by a platelet count between 30 and 100 G/L with at least a doubling of basal platelet count according to the international definition. In case of subsequent exposure to both TPORAs in a single patient, response was defined by response to at least one TPO-RA in the main analysis. We performed a subgroup analysis by TPORAs. Results: During the study period, 451 patients with incident ITP were included in CARMEN-registry. Among them, 105 had been treated by TPO-RAs, including 65 with BMS before the exposure to TPORA. We then excluded 20 patients with secondary ITP and 7 with less than 10 MKs on the BMS. We finally included 38 patients in the analysis. Median age at diagnosis was 71 years (interquartile range - IQR: 31 - 94) and 34.2% were women. Thirty-three patients were treated with eltrombopag, 17 with romiplostim including 13 who were exposed to both TPORAs. Thirty-four (89.4%) achieved response. The median number of MKs analyzed per patient was 137 (IQR: 50 - 265). All results are presented in Table 1. In the main analysis, there was no significant difference in the median percentage of dysplastic MKs in responders (4.0%, 95% confidence interval - CI: 2.3 - 6.4) and non-responders (4.5%, 95% CI: 0.7 - 7.1). There was a trend for a higher proportion of granular MKs (4.5%, 95% CI: 3 - 6) and basophilic MKs (30.1%, 95% CI: 21.9 - 39.1) in non-responders comparing to responders (granular: 2.0%, 95% CI: 0 - 4.1; basophilic: 21.3%, 95% CI: 11.4 - 40.7). Results were similar in the subgroup of patients treated with eltrombopag (data not shown; the low number of patients treated with romiplostim precluded any analysis). Conclusion: In this study, neither MK abnormalities nor the pattern of MK maturation stages were significantly associated with response to TPO-RAs. These results do not support a systematic bone marrow smear in patients with primary ITP to look for morphological predictive factors of response to TPO-RA. Figure 1 Figure 1. Disclosures Comont: AstraZeneca: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding. Moulis: Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Grifols: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sobi: Membership on an entity's Board of Directors or advisory committees; Argenx: Membership on an entity's Board of Directors or advisory committees.

Pediatric visceral leishmaniasis: diagnosis and therapy

Visceral leishmaniasis is a generalized infection of the reticuloendothelial system, it is fatal if left untreated in most cases. Early diagnosis and effective therapy can reduce the risk of disability and mortality. However, in non-endemic areas diagnosis may be delayed or missed. In the article we presented a case report of a child with imported visceral leishmaniasis. The gradual onset of the disease with irregular fever, growing weakness, fatigue, loss of appetite, anemia, cytopenic syndrome required differential diagnosis with oncological diseases. The diagnosis of leishmaniasis was made by microscopy of a bone marrow smear and the detection of leishmania. Recovery occurred after the prolonged course of amphotericin B and supportive treatment.

Hemophagocytic Lymphohistiocytosis in Children

Objective of the Paper: To present a clinical case of a 2.5-year girl hospitalised with severe pneumonia with associated hemophagocytic lymphohistiocytosis (HLH). Key Points. HLH is a syndrome, the clinical representation of which is a system sepsis-like inflammatory reaction; in the majority of cases, this condition is hardly diagnosed by paediatricians. Associated pancytopenia and hyperferritinaemia are of diagnostic importance. Here we present a clinical case of a 2.5-year girl who was hospitalised with severe pneumonia and associated HLH. As there is no common approach to the management of HLH, we summoned a board of doctors and decided to start steroid therapy (dexamethasone) and IV immunoglobulin (Octagam), and if there was no effect from therapy within a week, cytostatic agents would be added. The therapy was initiated promptly after macrophages had been found in bone marrow smear. Clinical and paraclinic improvements were noted on day 5, so aggressive immunosuppresion was not initiated. In a month there were no signs of organ dysfunction; the values normalised, and the child was discharged from the inpatient clinic. Conclusion. HLH is associated with high mortality, therefore, early diagnosis and therapy are critical. The specificity of this case is that we managed to promptly diagnose the condition, initiated treatment and achieved complete remission without aggressive immunosuppresion. Keywords: hemophagocytic lymphohistiocytosis, macrophages, pancytopenia.

Transfer Learning for Identifying Morphological Heterogeneity of Neutrophils Nuclei in Hematological Diseases Based on Nuclei Semantic Segmentations of Bone Marrow Smear

It has been known that neutrophils are also very important in hematological diseases, such as dysplasia of neutrophils in myelodysplastic syndromes (MDS). But we pay more attention to immature blood cells (blasts) on bone marrow smear, because their proportion and morphology are key indicators for diagnosing malignant hematological diseases. We seem to pay less attention to neutrophils' roles in the diagnosis and the pathogenesis of hematological diseases. As we know, the survival period of neutrophils is relatively short and the neutrophils nuclei are rich in genetic information. Therefore, we assumed that the changes of neutrophils, especially the changes of neutrophils nuclei may be a mirror that reflects different hematological diseases. Here we will explore the differences among the morphology of neutrophils nuclei in hematological diseases, and try to find the bases of these differences. We selected neutrophils from 4 types of malignant hematological diseases, which is chronic myelogenous leukemia (CML), multiple myeloma (MM), acute myeloblastic leukemia with maturation (AML-M2), acute monocytic leukemia (AML-M5) and normal bone marrow. Neutrophils were identified by two senior diagnosticians (diagnostic experience longer than 10 years). In this study, we first segmented the neutrophils image by hand, and then use ilastik, an interactive semantic segmentations tool, to perform the nucleus segmentation. We applied transfer learning to training classification model of the segmented nuclei. We found that the neutrophils nuclei from different hematological diseases can be distinguished by the model. That is, we can preliminarily judge which kind of disease based on the neutrophils nuclei. These findings suggest that it is possible to have specific neutrophils nuclei features among different hematological diseases. However, due to the black box effect of the deep learning model, we still do not know what these features are. In order to clarify these potential features of neutrophils, we performed feature extraction (including texture analysis) on the segmented nuclei, and applied Fiji or imagej to obtain the feature parameters of the nuclei. At last using R language packages rattle (version 5.3.0) for features comparison and analysis. We found that there are differences in some feature parameters of the neutrophils nuclei among them. Maybe these are the basis for the heterogeneity of neutrophils in different hematological diseases. Here we provide a preliminary study for the neutrophils phenotype heterogeneity in different hematological diseases by deep learning. Differences in morphology of neutrophils nuclei may be the base of the heterogeneity. The features analysis of the neutrophils nuclei based on machine learning pixel classification further explains the basis of image specific difference discovered by transfer learning. In the future, the morphology of the neutrophils nuclei probably play an important role on distinguishing different hematological diseases. Disclosures No relevant conflicts of interest to declare.

Automatic Bone Marrow Cell Identification and Classification By Deep Neural Network

Purpose Differential counting of blood cells is the basis of diagnostic hematology. In many circumstances, identification of cells in bone marrow smears is the golden standard for diagnosis. Presently, methods for automatic differential counting of peripheral blood are readily available commercially. However, morphological assessment and differential counting of bone marrow smears are still performed manually. This procedure is tedious, time-consuming and laden with high inter-operator variation. In recent years, deep neural networks have proven useful in many medical image recognition tasks, such as diagnosis of diabetic retinopathy, and detection of cancer metastasis in lymph nodes. However, there has been no published work on using deep neural networks for complete differential counting of entire bone marrow smear. In this work, we present the results of using deep convolutional neural network for automatic differential counting of bone marrow nucleated cells. Materials & Methods The bone marrow smears from patients with either benign or malignant disorders in National Taiwan University Hospital were recruited in this study. The bone marrow smears are stained with Liu's stain, a modified Romanowsky stain. Digital images of the bone marrow smears were taken using 1000x oil immersion lens and 20MP color CCD camera on a single microscope with standard illumination and white-balance settings. The contour of each nucleated cell was artificially defined. These cells were then divided into a training/validation set and a test set. Each cell was then classified into 1 of the 11 categories (blast, promyelocyte, neutrophilic myelocyte, neutrophilic metamyelocyte, neutrophils, eosinophils and precursors, basophil, monocyte and precursors, lymphocyte, erythroid lineage cells, and invalid cell). In training/validation set, the classification of each cell was annotated once by experienced medical technician or hematologist. The annotated dataset was used to train a Path-Aggregation Network for instance segmentation task. In test set, cell classification was annotated by three medical technicians or hematologists; only over 2/3 consensus was regarded as valid. After the neural network model was fully trained, the ability of the model to classify and detect bone marrow nucleated cells was evaluated in terms of precision, recall and accuracy. During the model training, we used group normalization and stochastic gradient descent optimizer for training. Random noise, Gaussian blur, rotation, contrast and color shift were also used as means for data augmentation. Results The digital images of 150 bone marrow aspirate smears were taken for this study. They included 61 for acute leukemia, 39 for lymphoma, 2 for myelodysplastic syndrome (MDS), 2 for myeloproliferative neoplasm (MPN), 10 for MDS/MPN, 12 for multiple myeloma, 4 for hemolytic anemia, 9 for aplastic anemia, 8 for infectious etiology and 3 for solid cancers. The final data contained 5927 images and 187730 nucleated bone marrow cells, which were divided into 2 sets: 5630 images containing 170966 cells as the training/validation set, and 297 images containing 16764 cells as the test set. Among the 16764 cells annotated in test set, 15676 cells (93.6 %) reached over 2/3 consensus. The trained neural network achieved 0.832 recall and 0.736 precision for cell detection task, 0.79 mean intersection over union (IOU) for cell segmentation task, mean average precision of 0.659 and accuracy of 0.801 for cell classification. For individual cell categories, the model performs the best with "erythroid-lineage-cells" (0.971 recall, 0.935 precision) and the worst with "monocyte-and-precursors" (0.825 recall, 0.337 precision). Conclusions We have created the largest and the most comprehensive annotated bone marrow smear image dataset for deep neural network training. Compared with previous works, our approach is more practical for clinical application because it is able to take in an entire field of smear and generate differential counts without any other preprocessing steps. Current results are highly encouraging. With continued expansion of dataset, our model would be more precise and clinically useful. Figure Disclosures Yeh: aether AI: Other: CEO and co-founder. Yang:aether AI: Employment. Tien:Novartis: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Research Funding; Roche: Honoraria; Johnson &Johnson: Honoraria; Alexion: Honoraria; BMS: Honoraria; Roche: Research Funding; Celgene: Honoraria; Pfizer: Honoraria; Abbvie: Honoraria. Hsu:aether AI: Employment.

Positivity Rate of Systematic Bone Marrow Smear Above 60-Year-Old Patients with Newly Diagnosed Immune Thrombocytopenia to Screen for Associated Hematological Malignancy. Results from the French Prospective Multicenter Carmen Registry

Introduction:There are discrepancies across recommendations about the indication of bone marrow smear in adults diagnosed for immune thrombocytopenia (ITP). The 2011 American Society of Hematology guidelines do not recommend bone marrow smear in case of typical ITP. In contrast, the 2010 international consensus and the 2017 French guidelines recommend systematic bone marrow smear in adults aged >60 years even in case of typical ITP to detect a blood cancer, particularly myelodysplastic syndrome. This recommendation is driven from expert consensus. Data are lacking about the positivity rate of this examination in older patients with typical ITP. The aim of this study was to assess the positivity rate of bone marrow smear at ITP diagnosis in >60-year-old patients with no other clinical or biological sign of hematological malignancy. Methods:Data source was theCARMEN (Cytopénies Auto-immunes : Registre Midi-PyréneEN) registry. All adult patients with an incident diagnosis of ITP in the French Midi-Pyrénées region (South of France, 3 million inhabitants) are prospectively enrolled since June 2013 in the multicenter CARMEN registry. ITP is defined by international guidelines (platelet count <100 x 109/L and exclusion of other causes of thrombocytopenia). Investigations performed at ITP diagnosis in a real-life basis, including bone marrow smear, are recorded with their results. Study population was selected among the patients included in the CARMEN registry from June 2013 to December 2018. Inclusion criteria were: age>60 years; absence of clinical signs of hematological malignancy (lymphadenopathy, hepatomegaly, splenomegaly); isolated thrombocytopenia on blood count; bone marrow smear performed at ITP diagnosis. We described patients with abnormal bone marrow smear and implications for ITP management. Results:We identified 114patients (66 men and 48 women) satisfying all inclusion criteria. Mean age at ITP diagnosis was 76 years (standard deviation - SD: 9 years). Platelet count at diagnosis was 32.7 x 109/L (SD: 27.7 x 109/L) and 58 patients presented with bleeding: skin bleeding only (n=33), oral bleeding (n=17), epistaxis (n=10) and hematuria (n=3). Only one patient had an abnormal bone marrow smear corresponding to a characterized hematological disease: a myelodysplastic syndrome. It was a 62-year-old man without medical history who presented in 2014 with extensive skin bleeding, and isolated thrombocytopenia (6 x 109/L). Other blood count parameters were: hemoglobin: 15.2 g/dL; MCV: 83 fL; leukocytes: 5.3 x 109/L; polynuclear neutrophils: 3.5 x 109/L; lymphocytes: 1.0 x 109/L; monocytes: 0,3 x 109/L . Bone marrow smear revealed normal cellularity. The megakaryocytic lineage was normally represented with significant number of megakaryocytes with multiple separated nuclei. Significant dysgranulopoiesis was also observed with pseudo-Pelger-Huët anomaly and cytoplasmic hypogranulation. Some erythroblasts with defective haemoglobination or cytoplasmic vacuolation were present. This aspect was compatible with the diagnosis of myelodysplastic syndrome with multilineage dysplasia (MDS-MLD). Karyotype was normal. The patient was initially treated for ITP with steroids and intravenous immunoglobulins (with partial response), then with danazol (complete response), eltrombopag (after loss of response to danazol and ocular bleeding, resulting in complete response) and more recently romiplostim (after loss of response to eltrombopag, resulting in complete response). He was treated in 2019 by rituximab to spare exposure to thrombopoietin receptor agonists, without efficacy. Before Rituximab, another bone marrow smear was performed (4 years after the first one) with the same cytologic and cytogenetic features (MLD-MDS with normal caryotype). Conclusions:Diagnosis of hematological malignancy is very uncommon in >60 year-old patients who present with typical ITP. Myelodysplastic syndrome was found in 1 (0.8%) patient in this series, and did not impact the management or the evolution of the patient with a five-year follow-up. Overall, this study sustains guidelines that does not recommend systematic testing for bone marrow examination in >60 year-old patients with typical ITP. Disclosures Comont: BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Recher:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sunesis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria; Macrogenics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Beyne-Rauzy:Novartis: Research Funding; Cellgene: Research Funding. Moulis:CSL Behring: Research Funding; Amgen pharma: Research Funding, Speakers Bureau; Novartis pharma: Research Funding, Speakers Bureau.