Objective To explore the characteristics of morphology and immunology in acceleration phase and blastic phase of chronic myelogenous leukemia(CML).Methods Seventy-three cases of CML-BP bone marrow specimens were respectively conducted the morphology and related cell chemical dyeing observation for determining the FAB type.Flow cytometry was used to detect series immunological related antigens.Results Among 73 cases of FAB typing,there were 44 cases of CML-AML,21 cases of ALL and 8 cases.21 cases CML-ALL patients In the immunophenotyping by flow cytometry,among 21 cases of CML-ALL,there were 19 cases of B-ALL,2 cases of T-ALL,moreover 12 cases contained myeloid marker.Among 8 cases CML-HAL,the immunophenotypes were 6 cases of B+-My and 2 cases of T+ My.Among 44 cases CML-AML,15 cases contained T cell marker,and 2cases contained B cell marker,other cases had no cross-lineage expression.Among 73 cases of CML-BP,29 cases conducted the flow cytometry detection in the acceleration phase,in which 16 cases urgently changed to AML,and 13 cases to non-AML(9 cases of ALL and 4 cases of HAL).Among non-AML cases,2 cases had the simultaneous existence of myeloid primitive cells and precursor lymphocyte in the acceleration phase and other 9 cases were myeloid primitive cell or accompanied by lymphocyte marker.Conclusion Flow cytometry has a certain implication role for the direction and differentiation diagnosis of CML-BP.

Objective:To explore the diagnostic value and problems of artificial intelligence (AI) bone marrow cell recognition technology in the detection of minimal residual disease (MRD) of leukemia.Methods:A total of 65 cases with minimal residual disease of leukemia confirmed by flow cytometry from the Hematology Medical Center of Xinqiao Hospital affiliated to the Army Medical University (AMMU) from November 1 to December 31, 2020 were collected. The bone marrow Wright′s staining smears were obtained, and all bone marrow smears were scanned and classified automatically without artificial intervention by the analysis system based on Artificial Intelligence platform (morphogo). AI-MRD was defined to positive when the proportion of primary cells was more than 3%. According to the number of AI automatic recognition cells, the cases were divided into 18 cases of less than 500 (L500), 35 cases of 500 to 1900 (between 500 and 1900, B1900), and 12 cases of more than 1900 (M1900), no overlap or omission between groups. Kappa consistency test was performed on the results of artificial intelligence test and the results of flow cytometry for minimal residual disease of leukemia (MFC-MRD) in each group. The receiver operating characteristic curve (ROC) of the artificial intelligence test results of each group of patients was drawn based on the MFC-MRD results, and the sensitivity, specificity and accuracy of the area under the curve (AUC) value and AI results were calculated.Results:After grouping according to the number of cells automatically recognized by AI, the detection results of L500 group were MFC-MRD+/AI-MRD+7 cases, MFC-MRD+/AI-MFC-2 cases, MFC-MRD-/AI-MRD+6 cases, MFC-MRD-/AI-MRD-3 cases; In B1900 group, MFC-MRD+/AI-MRD+13 cases, MFC-MRD+/AI-MFC-6 cases, MFC-MRD-/AI-MRD+6 cases, MFC-MRD-/AI-MRD-10 cases; The results of M1900 group were MFC-MRD+/AI-MRD+5 cases, MFC-MRD+/AI-MFC-0 cases, MFC-MRD-/AI-MRD+1 case, MFC-MRD-/AI-MRD-6 cases. Taking MFC-MRD as the determination standard, the sensitivity of AI-MRD detection in L500 group, B1900 group and M1900 group was 53.8%, 68.4% and 83.3%, the specificity was 60%, 62.5% and 100%, the accuracy was 55.6%, 65.7% and 91.7%, and the AUC value were 0.568 P=0.654, 0.678 P=0.069,1.000 P=0.000. Conclusions:This study preliminarily explored the diagnostic value and problems of AI bone marrow cell recognition in the detection of minimal residual disease of leukemia. It was confirmed that when 3% of the proportion of blasts in AI cell classification is set>3% as the positive threshold of AI-MRD, the consistency between AI and MFC-MRD detection increases with the increase of the number of cells recognized by AI.