医学
套细胞淋巴瘤
CD20
病理
CD19
细胞仪
流式细胞术
淋巴瘤
B细胞
淋巴增殖性病變
慢性淋巴细胞白血病
免疫分型
CD5型
免疫学
滤泡性淋巴瘤
毛细胞白血病
白血病
抗体
作者
Zofia Gross,Richard Veyrat‐Masson,Béatrice Grange,Sarah Huet,Aurélie Verney,Alexandra Traverse‐Glehen,Philippe Ruminy,Lucile Baseggio
摘要
Abstract Flow cytometry (FCM) has become a method of choice for immunologic characterization of chronic lymphoproliferative disease (CLPD). To reduce the potential subjectivities of FCM data interpretation, we developed a machine learning random forest algorithm (RF) allowing unsupervised analysis. This assay relies on 16 parameters obtained from our FCM screening panel, routinely used in the exploration of peripheral blood (PB) samples (mean fluorescence intensity values (MFI) of CD19, CD45, CD5, CD20, CD200, CD23, HLA‐DR, CD10 in CD19‐gated B cells, ratio of kappa/Lambda, and different ratios of MFI B‐cells/T‐cells [CD20, CD200, CD23]). The RF algorithm was trained and validated on a large cohort of more than 300 annotated different CLPD cases (chronic B‐cell leukemia, mantle cell lymphoma, marginal zone lymphoma, follicular lymphoma, splenic red pulp lymphoma, hairy cell leukemia) and non‐tumoral selected from PB samples. The RF algorithm was able to differentiate tumoral from non‐tumoral B‐cells in all cases and to propose a correct CLPD classification in more than 90% of cases. In conclusion the RF algorithm could be proposed as an interesting help to FCM data interpretation allowing a first B‐cells CLPD diagnostic hypothesis and/or to guide the management of complementary analysis (additional immunologic markers and genetic).
科研通智能强力驱动
Strongly Powered by AbleSci AI