Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters

Abstract

The diagnosis of myelodysplastic syndromes (MDS) requires a high clinical index of suspicion to prompt bone marrow studies as well as subjective assessment of dysplastic morphology. We sought to determine if data collected by automated hematology analyzers during complete blood count (CBC) analysis might help to identify MDS in a routine clinical setting. We collected CBC parameters (including those for research use only and cell population data) and demographic information in a large (>5,000), unselected sequential cohort of outpatients. The cohort was divided into independent training and test groups to develop and validate a random forest classifier that identifies MDS. The classifier effectively identified MDS and had a receiver operating characteristic area under the curve (AUC) of 0.942. Platelet distribution width and the standard deviation of red blood cell distribution width were the most discriminating variables within the classifier. Additionally, a similar classifier was validated with an additional, independent set of >200 patients from a second institution with an AUC of 0.93. This retrospective study demonstrates the feasibility of identifying MDS in an unselected outpatient population using data routinely collected during CBC analysis with a classifier that has been validated using two independent data sets from different institutions.

Validation of DNA Methylation to Predict Outcome in Acute Myeloid Leukemia by Use of xMELP

“…Further, in contrast to previous classifiers utilizing SuperPC analysis, we used a random forest method to exploit the robust properties of ensemble machine learning methods (26). We recently obtained robust classification results using a random forest classifier to segregate myeloid neoplasms from reactive conditions (27) and wished to utilize a similar approach for AML survival prediction.…”

Computational Pathology: A Path Ahead

“…10–16 In hematology, machine learning methods applied to analyzing automated complete blood count parameters have been used to accurately identify patients with myelodysplasia. 17 Software is actively being developed that provides the next level of “logistical computational” software, interfaces, and tools that allow user-friendly movement of next-generation sequencing files and data sets through instruments and the bioinformatics pipeline to the laboratory information system; between the laboratory information system and online curated data sources that allow a pathologist to generate an accurate and up-to-date report; and from the laboratory information system to the EHRs as both human-readable and computationally mineable data. 11,12,18,19 In addition, computational quality assurance and quality control tools are being incorporated into the analysis of these large and multidimensional data sets to ensure that the data meet quality standards.…”

Abnormal neutrophil scattergram obtained using Pentra MS CRP in the patients with myelodysplastic syndrome showing dysgranulopoiesis

“…The gold standard for myelodysplasia diagnosis is careful microscopic observation by an experienced hematologist or hematopathologist [1,2,[5][6][7], but this procedure may be dependent on the subjectivity of the respective observer, with interobserver variation in the obtained results [7,9,10]. On the other hand, we and other investigators reported that the characteristic neutrophil distribution pattern obtained using automated hematology analyzers such as Coulter LHseries, Sysmex XE-2100, or CELL-DYN SAPPHIRE could be a convenient as well as objective finding for peripheral blood dysgranulopoiesis in patients with MDS [11][12][13][14][15]. These investigators evaluated hematology analyzers capable of flow cytometry (FCM) for 5part white blood cell differentials .…”