Abstract
The DBSCAN clustering algorithm, an in-house method of unsupervised machine learning, was used to explore specific histotopographical features of the megakaryocytic lineage in the bone marrow biopsies of patients with JAK2- or CALR-mutated essential thrombocythemia and prefibrotic primary myelofibrosis. Ninety-five bone marrow biopsies of patients with essential thrombocythemia and primary myelofibrosis were investigated to assess the histotopography of megakaryocytes, specifically, the mean number of megakaryocytes in one cluster, as well as the mean number of clusters and megakaryocytes per 1 mm2 of section area. The logistic regression model was statistically significant: χ2 = 14.703, p = 0.023, Nagelkerke R2 = 19.6%. Analysis of the histotopographical features of megakaryocytes allowed correct differentiation between essential thrombocythemia and primary myelofibrosis in 71.6% cases. The differences in the histotopographical features of megakaryocytes in the bone marrow of with JAK2- or CALR-mutated essential thrombocythemia and primary myelofibrosis revealed by the DBSCAN clustering algorithm indicate that a relationship exists between the disease and the pattern of megakaryocytic lineage development that can be used to create a logistic regression model for differentiating these diseases.
Published Version
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