Fig. 4
Quantitative label-free detection of CML cells from patients during the TKI treatments. (A) The experimental method of quantitative label-free detection of CML cells from patients during the TKI treatments was illustrated. The classification AI model was pre-trained by the waveform signals obtained from leukocytes from CML patients at diagnosis and those from healthy individual. Leukocyte samples from CML patients during the TKI treatment and those from healthy individuals were then evaluated using the pre-trained AI model to calculate the ratio of CML cells in the sample. BCR::ABL1IS mRNA expressions was used as an indicator of remaining CML cell ratio in the samples. (B) Ratios of AI-predicted CML cells in the testing samples. Leukocytes in the samples were divided into WBCs, granulocytes, and lymphocytes from SSC-FSC scattergram, and the ratio of CML cells were calculated using the pre-trained AI model. CML patients at diagnosis for AI training, n = 6; healthy individual for testing (HC), n = 5; CML patients during the treatment for testing (CML), n = 11; p value by Mann–Whitney U test. (C) Diagnostic performance of CML cell detection by GC was evaluated by ROC analysis of HC and CML by the ratio of AI-predicted CML cells. The results were divided into WBC, granulocytes, and lymphocytes divided from SSC-FSC scattergram. (D) The correlation between AI-predicted CML cells and BCR::ABL1IS mRNA expressions. The r value was evaluated by Spearman’s rank correlation coefficient. CML chronic myeloid leukemia, TKI tyrosine kinase inhibitor, AI artificial intelligence, GC ghost cytometry, WBCs white blood cells, SSC side scatter, FSC forward scatter.
