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Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

机译:自动化数据库指导的专家指导的急性白血病免疫表型诊断和分类方法

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摘要

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications.
机译:急性白血病(AL)的准确分类对于充分治疗至关重要。 EuroFlow先前已经设计了一个AL定向管(ALOT)来引导相关的分类小组(T细胞急性淋巴细胞白血病(T-ALL),B细胞前体(BCP)-ALL和/或急性髓细胞白血病(AML))和最终诊断。现在,我们建立了一个参考数据库,其中包含656个典型的AL样本(145个T-ALL,377个BCP-ALL,134个AML),并通过标准化协议进行了处理和分析。使用基于主成分分析(PCA)的图和自动分类算法,将单个患者的单细胞与数据库进行直接比较,随后评估了另外783例病例。根据数据库指导的结果,将患者分类为:(i)典型的T,B或髓样,没有或没有; (ii)具有向另一个血统的过渡成分; (iii)非典型的;或(iv)混合血统。使用该自动算法,在781/783例病例中(99.7%)选择了右面板,并且在超过93%的病例中(85%的T-ALL,97%的BCP-ALL)已经提供了与最终WHO诊断可比的数据。 ,95%的AML和87%的混合表型AL患者),即使没有完整特征面板上的数据。我们的结果表明,数据库指导的分析有助于对ALOT结果进行标准化解释,并允许准确选择相关分类面板,从而为设计未来的WHO AL分类提供了坚实的基础。

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