Abstract
Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely: > 90% sensitivity and > 90% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.
Highlights
Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages
Philadelphia chromosome–negative myeloproliferative neoplasms (Ph-negative MPNs) are a group of hematological disorders that result from malignant transformations of hematopoietic stem c ells[1,2], and are characterized by abnormal proliferation of mature bone marrow (BM) cell lineages, which include polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (PMF)[3]
In order to reduce the workload and inter- and intra-personal inconsistency, we previously developed an automated image analysis system using deep convolutional neural networks (CNNs) based-image analysis algorithms using a total of 695,030 normal and abnormal blood cells
Summary
Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Philadelphia chromosome–negative myeloproliferative neoplasms (Ph-negative MPNs) are a group of hematological disorders that result from malignant transformations of hematopoietic stem c ells[1,2], and are characterized by abnormal proliferation of mature bone marrow (BM) cell lineages (i.e., granulocytes, erythrocytes, and megakaryocytes), which include polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (PMF)[3]. Since various non-hematologic disorders can cause leukocytosis, thrombocytosis, and polycythemia, careful evaluation of the morphology in PB cells is critical for accurate initial diagnoses of Ph-negative MPNs, especially for detecting abnormalities in the cells. Immature granulocytes and nucleated RBCs are known to be observed in MF, including overt PMFs and secondary MFs6,7
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