Automated Detection of Dysplasia: Data Mining from Our Hematology Analyzers

Diagnostics (Basel). 2022 Jun 26;12(7):1556. doi: 10.3390/diagnostics12071556.

Abstract

Myelodysplastic syndromes (MDSs) are clonal hematopoietic diseases of the elderly, characterized by chronic cytopenia, ineffective and dysplastic hematopoiesis, recurrent genetic abnormalities and increased risk of progression to acute myeloid leukemia. Diagnosis on a complete blood count (CBC) can be challenging due to numerous other non-neoplastic causes of cytopenias. New generations of hematology analyzers provide cell population data (CPD) that can be exploited to reliably detect MDSs from a routine CBC. In this review, we first describe the different technologies used to obtain CPD. We then give an overview of the currently available data regarding the performance of CPD for each lineage in the diagnostic workup of MDSs. Adequate exploitation of CPD can yield very strong diagnostic performances allowing for faster diagnosis and reduction of time-consuming slide reviews in the hematology laboratory.

Keywords: cell population data; complete blood count; hematology analyzer; leukocyte differential; myelodysplastic syndrome.

Publication types

  • Review

Grant support

This research received no external funding.