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Dysmegakaryopoiesis: hypolobated megakaryocyte (AB), binucleated micromegakaryocyte (C), multinucleated megakaryocyte. Shown at 100 x original magnification.
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According to WHO 2008 guidelines, the required percentage of cells manifesting dysplasia in the bone marrow to qualify as significant is 10% or over in one or more hematopoietic cell lineages, but this threshold is controversial. No 'normal' values have been established. Therefore, we investigated dyshematopoiesis in bone marrow aspirate squash pre...
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Citations
... Multiple studies have shown that hypogranulated dysplastic neutrophils on PBS can provide valuable insights into the diagnosis of MDS [41][42][43][44]. However, it is sometimes challenging for pathologists to identify them on PBS. ...
Simple Summary
This paper aims to highlight the latest advancements in the application of artificial intelligence in the diagnosis of myelodysplastic syndrome. This research focuses on a group of blood disorders called Myelodysplastic Syndrome (MDS), which can potentially develop into a more severe condition called Acute Myeloid Leukemia (AML). Detecting MDS early is crucial, but the current methods are time-consuming and labor-intensive. We aim to explore how artificial intelligence (AI) and machine learning (ML) can make the diagnosis of MDS faster and more accurate. AI involves computer programs that can think like humans, and ML is a part of AI that helps computers learn patterns and make predictions. By using these technologies, doctors can improve how they diagnose MDS, leading to better treatment and outcomes for patients.
Abstract
Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.
... The success of this exam depends irst of all on the pre-analytical stage, i.e. on the relevance of the indication, the quality of the sample, and the quality of the marrow smears [4]. The reading of the bone marrow examination then requires an experienced cytologist who synthesizes the clinical and biological data and the abnormalities observed on the bone marrow aspirate to propose one or more diagnostic hypotheses [5]. ...
Introduction: The bone marrow aspirate examination is defined as a quantitative and qualitative study of bone marrow cells obtained by puncture and aspiration. Aim: Our objective was to evaluate the practice of this exam at Andrainjato Fianarantsoa University Hospital in order to improve its diagnostic relevance. Method: This is a prospective and descriptive cross-sectional study of all bone marrow aspirates performed at the Andrainjato Fianarantsoa University Hospital Madagascar, during 18 months, from January 2021 to June 2022. Results: Forty-two bone marrow aspirate examinations were performed during the study period, among the 338 requests for hematological analysis received, representing a percentage of 1.26%. The average age of the patients was 32.17 years, with a sex ratio of 2.5. The prescription was of hospital origin in 83.3% of patients, motivated by the disturbance of the blood count in 78.6% of cases. Thirty-three requests were evaluated as relevant prescriptions. Coupled with the realization of the bone marrow examination, the haemograms were pathological in 78.6% of cases. The result of the bone marrow aspirate showed normal marrow cytology (16.7%), reactive marrow (23.8%), pathological marrow (50.0%), and hemodiluted marrow (9.5%). Dysmyelopoiesis (33.3%), multiple myeloma (23.8%), and acute leukemia (19.0%) were the main pathologies found. The difficulties encountered were related to the poor quality of the equipment and the non-availability of other complementary explorations. Conclusion: The bone marrow aspirate examination is technically feasible at Andrainjato Fianarantsoa University Hospital despite the existence of difficulties. The commitment to the process of continuous improvement of quality would impose the improvement of the technical platform.
... In addition, we lacked a centralized pathology and clinical review, which could have decreased the proportion of NOS cases and improved the quality of our data. This is particularly relevant for MDS, due to the poor inter-observed concordance in diagnosis and the numerous non-neoplastic conditions that can mimic such neoplasms 51,52 . Nevertheless, in spite of the unavoidable biases due to variability and variation in registration quality and coding practices, over 95% of cases had adequate morphology specification. ...
Comprehensive population-based data on myeloid neoplasms (MNs) are limited, mainly because some subtypes were not recognized as hematological cancers prior to the WHO publication in 2001, and others are too rare to allow robust estimates within regional studies. Herein, we provide incidence data of the whole spectrum of MNs in Spain during 2002–2013 using harmonized data from 13 population-based cancer registries. Cases ( n = 17,522) were grouped following the HAEMACARE groupings and 2013-European standardized incidence rates (ASR E ), incidence trends, and estimates for 2021 were calculated. ASR E per 100,000 inhabitants was 5.14 (95% CI: 5.00–5.27) for myeloproliferative neoplasms (MPN), 4.71 (95% CI: 4.59–4.84) for myelodysplastic syndromes (MDS), 3.91 (95% CI: 3.79–4.02) for acute myeloid leukemia, 0.83 (95% CI: 0.78–0.88) for MDS/MPN, 0.35 (95% CI: 0.32–0.39) for acute leukemia of ambiguous lineage, and 0.58 (95% CI: 0.53–0.62) for not-otherwise specified (NOS) cases. This study highlights some useful points for public health authorities, such as the remarkable variability in incidence rates among Spanish provinces, the increasing incidence of MPN, MDS, and MDS/MPN during the period of study, in contrast to a drop in NOS cases, and the number of cases expected in 2021 based on these data (8446 new MNs).
... This highlights the multiplicity of tests that have to be performed in order to confirm the diagnostic of MDS and is indicative of the inter-observer diagnostic heterogeneity -indeed, while cytogenetics or the detection of cytopenias can be relatively unambiguous, the inter-individual (inter-expert) concordance for the detection of dysplasias is between 0.7-0.8, but can drop to 0.5 when the task is that of classifying specific abnormal cell types [357][358][359]. As such, it is reasonable that MDS can require independent confirmation when there is a low degree of dysplasia or only one dysplastic lineage is identified [356]. ...
... It is worth noting that recent publications using cytomorphology (either automated or derived by an expert) in combination with machine-learning focus specifically on the bone marrow, showing that there is more information to be mined from cellular morphology than what has been thus far inferred from human expert inspection [361,362], but make no effort to expand this analysis to WBS. Together with the aforementioned fact that inter-individual concordance can be lacking in WBS and bone marrow slide analysis [357][358][359], the need for reproducible and more objective diagnostic criteria becomes clearer. ...
Haematopoiesis is the complex process of blood cell production, carried out by haematopoietic stem cells in the bone marrow. Over the human lifespan, it generates quadrillions of cells which participate in essential bodily functions such as immunity (whiteblood cells), oxygen distribution (red blood cells) and blood coagulation (platelets). However, several conditions affect this process --- a lack of nutrients such as iron or folate can lead to alterations to the production of red and white blood cells, whereas the accumulation of somatic mutations contributes to the formation of genetically distinct subpopulations of haematopoietic stem cells (or clones) whose behaviour is partly determined by mutations conferring growth advantages (also known as driver mutations). When mutations in cancer-associated genes are present in the blood of healthy individuals this is known as clonal haematopoiesis, a benign condition that can progress to blood cancers such as myelodysplastic syndromes, characterized by an excess of abnormally developed (or dysplastic) cells in the bone marrow and in the blood. Blood cancers such as these are usually diagnosed by trained experts using a number of complementary analyses, including the inspection of blood cell morphology (cytomorphology) --- which can have high inter-individual variance --- and differential cell counts under the microscope. In this work, I have studied how the haematopoietic system and blood are altered in two distinct settings --- the evolution of somatic mutations in healthy individuals and the cytomorphological alterations to blood cells in myelodysplastic syndromes. Firstly, I studied the somatic evolution of the haematopoietic system in healthy individuals using longitudinal sequencing and single-cell-derived colonies. With simulations, I developed a computational model and applied it to a cohort of elderly individuals with clonal haematopoiesis and show i) how driver genetics determine the growth rate of different clones, ii) how clones appear consistently through life (with few exceptions), iii) that clones decelerate due to an increasingly competitive clonal landscape and iv) how mutations which confer greater growth advantages are associated with a greater risk of developing haematological malignancies. Secondly, I used a cohort of digitalised whole blood smears from healthy individuals and individuals with anaemia or myelodysplastic syndromes to study how each condition leads to cytomorphological alterations through computational methods. To do this, i) I developed and implemented methods for the high-throughput detection of blood cells from digitized whole blood slides, ii) developed a cellular characterisation protocol that captures morphological features relevant for the prediction of clinically-relevant conditions and the presence of specific mutations in myelodysplastic syndromes and iii) described novel associations between blood cell phenotype and anaemia or myelodysplastic syndrome subtypes. By studying how haematopoietic stem cells evolve in the human body, I contributed to the understanding of early cancer development and to the broader field of human somatic evolution, and by quantitatively studying alterations to cytomorphology in clinically-relevant conditions, I showed how this can reveal novel blood cell phenotypes which can aid in diagnostic and prognostic. Both projects offer different perspectives into haematopoiesis as a dynamic process and contribute to clinical research by highlighting connections between somatic evolution in healthy individuals and cancer onset, and by discovering previously unknown cellular phenotypes-disease associations.
... However, another study which examined bone marrow squash slides of 120 healthy bone marrow samples found that the median proportion of cells with changes of dysgranulopoiesis was 10%, with cytoplasmic hypogranularity being the most common form of dyspoiesis (93%). They also noted a strong correlation of age and frequency of dysgranulopoiesis with younger donors showing more frequent granulopoietic dysplasia compared with older donors.42 ...
Dysgranulopoiesis is a condition in which granulocytic production is defective and is most often described in neoplastic conditions. However, it can also be frequently seen in non‐neoplastic conditions. Early suspicion and detection of these non‐neoplastic causes may prevent further invasive and expensive interventions. In this review, we take a look at the various causes of dysgranulopoiesis with an emphasis on non‐neoplastic etiologies, followed by a detailed outline of the laboratory approach for determining its many causes.
... MDS primarily affect elderly patients. According to the Surveillance, Epidemiology and End Results (SEER) [2][3][4][5][6][7][8][9] Autops Case Rep (São Paulo). 2021; 11:e2021274 database, only about 3-6% of MDS patients are less than 50 years old at the time of diagnosis. ...
... Dysplasia itself, even if prominent, is not definitive evidence of a clonal process because it is frequently seen in patients with reactive secondary cytopenia. [5][6][7][8] Several nutritional (such as folic acid, vitamin B12 and copper deficiency), toxic (such as heavy metals particularly arsenic, lead and zinc, drugs) and other factors (such as congenital disorders, infections, and autoimmune disorders) can cause myelodysplastic changes but not atypia. ...
Background
Myelodysplastic syndromes (MDS) mainly occur in the elderly but can rarely affect younger individuals too. The correct diagnosis relies on careful morphologic evaluation, cytogenetic/molecular results, and excluding reactive conditions mimicking MDS. We present the clinical, pathologic, cytogenetic, and molecular features of a case of MDS with excess blasts-2 (MDS-EB-2) in a 30-year-old male who was found to have pancytopenia during his hospitalization for coronavirus disease 2019 (COVID-19) and discuss the diagnostic challenges of MDS in patients with COVID-19.
Case presentation
A 30-year-old man presented to an outside hospital with fever, chills, weakness, coughing spells, dizziness and shortness of breath and was diagnosed with bilateral pneumonia due to COVID-19. At the outside hospital, he was found to be pancytopenic, and a subsequent bone marrow aspiration and biopsy raised concern for a COVID-19 induced hemophagocytic lymphohistiocytosis. In addition, MDS could not be ruled out. The patient was thus referred to our institute for further management. The patient’s peripheral blood showed pancytopenia with occasional dysplastic neutrophils and a few teardrop cells. Given the diagnostic uncertainty, a bone marrow aspiration and a biopsy were repeated revealing a hypercellular bone marrow with erythroid hyperplasia, megakaryocytic hyperplasia, trilineage dysplasia, increased blasts (13%), many ring sideroblasts, and mild to moderate myelofibrosis, consistent with MDS-EB-2. Chromosomal analysis revealed isochromosome 14. Next generation sequencing demonstrated SF3B1 K700E mutation.
Discussion and conclusion
The diagnosis of MDS can be challenging, particularly in young patients. Cytopenia and myelodysplastic features have been reported in COVID-19 patients, making the diagnosis of MDS more elusive. A careful pathologic examination of the bone marrow with ancillary studies including flow cytometry, immunohistochemistry, and cytogenetic and molecular studies in combination with a thorough clinical evaluation, leads to the accurate diagnosis.
... Consequently, there is a recognized need for standardization in assessing cytomorphological features in MDS [16,22,23]. Given the success of machine learning in analyzing histomorphological features in many non-hematopoietic pathology domains [24,25,26], there is a clear opportunity to develop ML-based approaches to support standardization cytomorphology in MDS. ...
One of the major obstacles to reach diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges whether we can now solve diagnostic imaging's fundamental challenge. This paper briefly reviews the problem and how eventually both supervised and unsupervised AI technologies could help overcome it.
... 24,25 In CCUS, cytopenia has generally been defined by any value below institutional laboratory reference values, so we ad- lineages has also been observed within the BM of healthy older individuals. 28,29 In this study, however, dysplasia in CCUS patients was associated with MDS-related molecular abnormalities, suggesting that disordered hematopoiesis was not an incidental finding but likely arose from underlying genetic alterations. ...
Objectives
Although morphologic dysplasia is not typically considered a feature of CCUS, we have consistently observed low‐level bone marrow (BM) dysplasia among CCUS patients. We sought to determine whether sub‐diagnostic BM dysplasia in CCUS patients is associated with other clinico‐pathologic findings of myelodysplastic syndrome (MDS).
Methods
We identified 49 CCUS patients, 25 with sub‐diagnostic dysplasia (CCUS‐D), and 24 having no dysplasia (CCUS‐ND). We compared the clinical, histologic, and laboratory findings of CCUS‐D and CCUS‐ND patients to 49 MDS patients, including blood cell counts, BM morphology, flow cytometry, cytogenetics, and results of next‐generation sequencing.
Results
No statistically significant differences were observed between CCUS‐D and CCUS‐ND patients in the degree of cytopenias, BM cellularity, myeloid‐to‐erythroid ratio, or the presence of flow cytometric abnormalities. However, compared to CCUS‐ND, CCUS‐D patients exhibited increased mutations in myeloid malignancy‐associated genes, including non‐TET2/DNMT3A/ASXL1 variants, spliceosome (SF3B1, SRSF2, ZRSR2, or U2AF1) variants, and IDH2/RUNX1/CBL variants. CCUS‐D patients were also enriched for higher variant allele frequencies and co‐mutation of TET2/DNMT3A/ASXL1 with other genes.
Conclusions
CCUS‐D patients exhibit a molecular (but not clinical) profile more similar to MDS patients than CCUS‐ND, suggesting CCUS‐D may represent a more immediate precursor to MDS and may warrant closer clinical follow‐up.
... 13 While there is significant interobserver variability in diagnosing MDS in general, the interobserver variability is highest among low-grade MDS compared with MDS with increased blasts. 14,15 Finally, it is very important to recognize that in some low-grade MDS cases, little to no overt morphologic dysplasia may be present. In MDS with ring sideroblasts (while the ring sideroblasts themselves constitute dysplastic erythroid cells), only minimal dyserythropoiesis may be visualized on the Wright-Giemsa-stained aspirate smear. ...
Objectives:
Myelodysplastic syndromes (MDS) are a group of myeloid neoplasms that are often difficult to diagnose due to their pathologic and clinical heterogeneity. The key features of MDS are peripheral blood cytopenias, ineffective hematopoiesis manifesting as morphologic dysplasia, and clonal genetic abnormalities. The most difficult diagnostic dilemmas often arise in low-grade MDS cases (lacking excess blasts), which can be difficult to distinguish from other causes of cytopenia. This distinction requires the integration of information from the peripheral blood (both CBC parameters and morphology), bone marrow morphology, genetic studies, and interrogation of the clinical record to exclude secondary causes.
Methods:
We discuss the approach to the diagnosis of low-grade MDS (cases lacking increased blasts), including a diagnostic algorithm and two illustrative cases.
Results:
The appropriate use of ancillary studies is important to support or dispute the likelihood of low-grade MDS in conjunction with the findings of morphologic dysplasia. Interpreting the results of cytogenetics and next-generation sequencing can be challenging and must incorporate the emerging knowledge of clonal hematopoiesis of indeterminate potential.
Conclusions:
The role of pathologists in integrating data from multiple sources in the diagnosis of low-grade MDS is evolving and becoming increasingly complex; in this challenging diagnostic setting, it is important to feel comfortable with uncertainty and maintain a conservative approach.
... This reflects the way in which this type of genomic data is increasingly integrated into diagnostic decision making in a real-world context, especially in areas where morphological interpretation is challenging and inter-observer agreement is poor. 23,24 Another practical aspect regarding diagnostic workup is the limited availability of specific non-genomic tests (e.g., telomere length analysis) in the accredited setting which may have contributed to a variable rate of performance of these tests in the cohort. ...
Bone marrow failure (BMF) related to hypoplasia of haematopoietic elements in the bone marrow is a heterogeneous clinical entity with a broad differential diagnosis including both inherited and acquired causes. Accurate diagnostic categorisation is critical to optimal patient care and detection of genomic variants in these patients may provide this important diagnostic and prognostic information. We performed real-time, accredited (ISO15189) comprehensive genomic characterisation including targeted sequencing and whole exome sequencing in 115 patients with BMF syndrome (median age 24 years, range 3 months - 81 years). In patients with clinical diagnoses of inherited BMF syndromes, acquired BMF syndromes or clinically unclassifiable BMF we detected variants in 52% (12/23), 53% (25/47) and 56% (25/45) respectively. Genomic characterisation resulted in a change of diagnosis in 30/115 (26%) including the identification of germline causes for 3/47 and 16/45 cases with pre-test diagnoses of acquired and clinically unclassifiable BMF respectively. The observed clinical impact of accurate diagnostic categorisation included choice to perform allogeneic stem cell transplantation, disease-specific targeted treatments, identification of at-risk family members and influence of sibling allogeneic stem cell donor choice. Multiple novel pathogenic variants and copy number changes were identified in our cohort including in TERT, FANCA, RPS7 and SAMD9. Whole exome sequence analysis facilitated the identification of variants in two genes not typically associated with a primary clinical manifestation of BMF but also demonstrated reduced sensitivity for detecting low level acquired variants. In conclusion, genomic characterisation can improve diagnostic categorisation of patients presenting with hypoplastic BMF syndromes and should be routinely performed in this group of patients.
