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自动化识别白细胞亚群可提高基于数据库的专家指导诊断方向在急性白血病中的标准化:一项 EuroFlow 研究。

Automated identification of leukocyte subsets improves standardization of database-guided expert-supervised diagnostic orientation in acute leukemia: a EuroFlow study.

机构信息

Institut Necker-Enfants Malades, Institut National de Recherche Médicale U1151, Laboratory of Onco-Hematology, Assistance Publique-Hôpitaux de Paris, Hôpital Necker Enfants-Malades, Université de Paris, Paris, France.

FACS/Stem Cell Laboratory, Kantonsspital Aarau, Aarau, Switzerland.

出版信息

Mod Pathol. 2021 Jan;34(1):59-69. doi: 10.1038/s41379-020-00677-7. Epub 2020 Sep 30.

Abstract

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide toward the relevant classification panel and final diagnosis. In this study, we designed and validated an algorithm for automated (database-supported) gating and identification (AGI tool) of cell subsets within samples stained with ALOT. A reference database of normal peripheral blood (PB, n = 41) and bone marrow (BM; n = 45) samples analyzed with the ALOT was constructed, and served as a reference for the AGI tool to automatically identify normal cells. Populations not unequivocally identified as normal cells were labeled as checks and were classified by an expert. Additional normal BM (n = 25) and PB (n = 43) and leukemic samples (n = 109), analyzed in parallel by experts and the AGI tool, were used to evaluate the AGI tool. Analysis of normal PB and BM samples showed low percentages of checks (<3% in PB, <10% in BM), with variations between different laboratories. Manual analysis and AGI analysis of normal and leukemic samples showed high levels of correlation between cell numbers (r > 0.95 for all cell types in PB and r > 0.75 in BM) and resulted in highly concordant classification of leukemic cells by our previously published automated database-guided expert-supervised orientation tool for immunophenotypic diagnosis and classification of acute leukemia (Compass tool). Similar data were obtained using alternative, commercially available tubes, confirming the robustness of the developed tools. The AGI tool represents an innovative step in minimizing human intervention and requirements in expertise, toward a "sample-in and result-out" approach which may result in more objective and reproducible data analysis and diagnostics. The AGI tool may improve quality of immunophenotyping in individual laboratories, since high percentages of checks in normal samples are an alert on the quality of the internal procedures.

摘要

急性白血病(AL)的精确分类对于适当的治疗至关重要。EuroFlow 先前设计了一个用于指导相关分类面板和最终诊断的急性白血病定向管(ALOT)。在这项研究中,我们设计并验证了一种用于自动(基于数据库)门控和识别(AGI 工具)ALOT 染色样本中细胞亚群的算法。构建了一个参考数据库,其中包含 41 例正常外周血(PB)和骨髓(BM)样本和 45 例 ALOT 分析的正常 BM 样本,作为 AGI 工具自动识别正常细胞的参考。未明确识别为正常细胞的群体被标记为检查,并由专家进行分类。通过专家和 AGI 工具平行分析的额外正常 BM(n=25)和 PB(n=43)和白血病样本(n=109)用于评估 AGI 工具。分析正常 PB 和 BM 样本显示检查细胞的百分比较低(PB 中<3%,BM 中<10%),不同实验室之间存在差异。正常和白血病样本的手动分析和 AGI 分析显示细胞数量之间具有高度相关性(所有 PB 细胞类型的 r>0.95,BM 中 r>0.75),并且使用我们之前发表的用于免疫表型诊断和急性白血病分类的自动数据库指导专家监督定向工具(Compass 工具)对白血病细胞进行高度一致的分类。使用替代的商业可用管获得了类似的数据,证实了开发工具的稳健性。AGI 工具代表了在最小化人类干预和专业知识要求方面的创新步骤,朝着“样本输入和结果输出”方法迈进,这可能导致更客观和可重复的数据分析和诊断。AGI 工具可以提高各个实验室的免疫表型分析质量,因为正常样本中高百分比的检查是对内部程序质量的警告。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/590c/7806506/be254485196f/41379_2020_677_Fig1_HTML.jpg

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