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Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer

机译:基于流式细胞术的新型血液学分析仪检测体液中恶性细胞的方法

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摘要

Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode “XN-BF gating algorithm” to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF channel: Rule 1 detects larger and clumped cell signals compared to the leukocytes, targeting the clustered malignant cells; Rule 2 detects middle sized mononuclear cells containing less granules than neutrophils with similar fluorescence signal to monocytes, targeting hematological malignant cells and solid tumor cells. BF samples that meet, at least, one rule were detected as malignant. To evaluate this novel gating algorithm, 92 various BF samples were collected. Manual microscopic differentiation with the May-Grunwald Giemsa stain and WBC count with hemocytometer were also performed. The performance of these three methods were evaluated by comparing with the cytological diagnosis. The XN-BF gating algorithm achieved sensitivity of 63.0% and specificity of 87.8% with 68.0% for positive predictive value and 85.1% for negative predictive value in detecting malignant-cell positive samples. Manual microscopic WBC differentiation and WBC count demonstrated 70.4% and 66.7% of sensitivities, and 96.9% and 92.3% of specificities, respectively. The XN-BF gating algorithm can be a feasible tool in hematology laboratories for prompt screening of malignant cells in various BF samples.
机译:对体液(BF)样品中的有核细胞进行形态学显微镜检查,以筛查恶性肿瘤。但是,形态上的区别既费时又费力。这项研究旨在开发一种新的基于流式细胞术的门控分析模式“ XN-BF门控算法”,使用自动血液分析仪Sysmex XN-1000来检测恶性细胞。 XN-BF模式配备了WDF白细胞(WBC)差分通道。我们向WDF通道添加了两种算法:与白细胞相比,规则1可以检测到更大且成簇的细胞信号,并以聚集的恶性细胞为目标;规则2检测的中型单核细胞所含颗粒比嗜中性粒细胞少,且荧光信号与单核细胞相似,靶向血液学恶性细胞和实体瘤细胞。检测到至少符合一项规则的BF样品为恶性。为了评估这种新颖的门控算法,收集了92种不同的高炉样品。还进行了May-Grunwald Giemsa染色的手动显微鉴别和血细胞计数器的WBC计数。通过与细胞学诊断比较来评估这三种方法的性能。 XN-BF门控算法在检测恶性细胞阳性样本中达到了63.0%的敏感性和87.8%的特异性,阳性预测值为68.0%,阴性预测值为85.1%。手动显微WBC分化和WBC计数分别显示了70.4%和66.7%的敏感性,以及96.9%和92.3%的特异性。 XN-BF门控算法可以在血液学实验室中作为一种可行的工具,用于快速筛查各种高炉样品中的恶性细胞。

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