Management of patients with lower-risk myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic stem cell disorders characterized by ineffective hematopoiesis with abnormal blood cell development (dysplasia) leading to cytopenias and an increased risk for progression to acute myeloid leukemia (AML). Patients with MDS can generally be classified as lower- (LR-MDS) or higher-risk (HR-MDS). As treatment goals for patients with LR-MDS and those with HR-MDS differ significantly, appropriate diagnosis, classification, and follow-up are critical for correct disease management. In this review, we focus on the diagnosis, prognosis, and treatment options, as well as the prediction of the disease course and monitoring of treatment response in patients with LR-MDS. We discuss how next-generation sequencing, increasing knowledge on mechanisms of MDS pathogenesis, and novel therapies may change the current treatment landscape in LR-MDS and why structured assessments of responses, toxicities, and patient-reported outcomes should be incorporated into routine clinical practice

CentroidNetV2:A hybrid deep neural network for small-object segmentation and counting

This paper presents CentroidNetV2, a novel hybrid Convolutional Neural Network (CNN) that has been specifically designed to segment and count many small and connected object instances. This complete redesign of the original CentroidNet uses a CNN backbone to regress a field of centroid-voting vectors and border-voting vectors. The segmentation masks of the individual object instances are produced by decoding centroid votes and border votes. A loss function that combines cross-entropy loss and Euclidean-distance loss achieves high quality centroids and borders of object instances. Several backbones and loss functions are tested on three different datasets ranging from precision agriculture to microbiology and pathology. CentroidNetV2 is compared to the state-of-the art networks You Only Look Once Version 3 (YOLOv3) and Mask Recurrent Convolutional Neural Network (MRCNN). On two out of three datasets CentroidNetV2 achieves the highest F1 score and on all three datasets CentroidNetV2 achieves the highest recall. CentroidNetV2 demonstrates the best ability to detect small objects although the best segmentation masks for larger objects are produced by MRCNN. (c) 2020 Elsevier B.V. All rights reserved

Tumor immune escape in acute myeloid leukemia: Class II-associated invariant chain peptide expression as result of deficient antigen presentation

In this overview, we discuss the role of class II-associated invariant chain peptide (CLIP) in acute myeloid leukemia (AML), one of the few tumors expressing HLA class II. The clinical impact, function and regulation of CLIP expression on leukemic cells is addressed, indicating its potential as immunotherapeutic target in AML

Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient)

Proposals for revised IWG 2018 hematological response criteria in patients with MDS included in clinical trials

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Increased prescription rate of anti-infective agents after diagnosis of myelodysplastic syndromes

The a priori risk for infections in patients with myelodysplastic syndromes (MDS) is unknown. This study examines prescription rates of anti-infective agents in MDS patients before and after diagnosis, in both in- and outpatient settings, to provide information on infection management in clinical practice. We performed a population-based study using the HemoBase registry, containing data of all MDS patients diagnosed since 2005 in Friesland, the Netherlands. Community and hospital pharmacies provided prescription data from 1995 to 2020. Data were obtained for 203 of 292 patients (70%). Patients received significantly more anti-infective agents, predominantly antibacterials (70%), after diagnosis compared to before: 148.7 defined daily dose/1000 days (DID) (95% CI: 146.9-150.5) and 55.1 DID (95% CI: 54.5-55.8, p < 0.01), respectively, corresponding to median 23.5 and 7.6 treatment days/year. Higher-risk (449.9 DID) and lower-risk patients (129.1 DID) both received significantly more anti-infective agents after diagnosis; comorbidities, neutropenia, and age did not show significant differences relative to prescription rates. Before diagnosis, 10% of patients had infection-related hospital admissions versus 38% after diagnosis. In conclusion, MDS patients received significantly more anti-infective agents compared to before diagnosis. This is the first study that has quantified the prescription rate of anti-infective agents within and beyond the clinical setting in MDS

Use of azacitidine and its safety and efficacy in daily clinical practice in The Netherlands:the OCEAN study

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