异常特征的“圆底烧瓶”形基于体积的散点图作为怀疑持续性多克隆B细胞淋巴细胞增多症的触发因素。

Abnormal characteristic "round bottom flask" shape volume-based scattergram as a trigger to suspect persistent polyclonal B-cell lymphocytosis.

作者信息

Bigorra Laura, Larriba Iciar, Gutiérrez-Gallego Ricardo

机构信息

Hematology Department, Synlab Global Diagnostics, Verge de Guadalupe, 18, 08950 Esplugas de Llobregat, Barcelona, Spain; Department of Experimental & Health Sciences, Pompeu Fabra University, Barcelona Biomedical Research Park, Dr. Aiguader 88, 08003 Barcelona, Spain.

Hematology Department, Synlab Global Diagnostics, Verge de Guadalupe, 18, 08950 Esplugas de Llobregat, Barcelona, Spain.

出版信息

Clin Chim Acta. 2020 Dec;511:181-188. doi: 10.1016/j.cca.2020.10.015. Epub 2020 Oct 15.

DOI:10.1016/j.cca.2020.10.015

PMID:33068629

Abstract

BACKGROUND AND AIMS

The diagnosis of persistent polyclonal B-cell lymphocytosis (PPBL) is often challenging because of the lack of features and the overlap with the peripheral expression of splenic marginal zone lymphomas (SMZL). To obtain new clues for PPBL detection and diagnosis, all data provided by the DxH 800 analyzer (including scatter and cell population data (CPD)) was exploited and combined using a machine learning (ML) approach.

MATERIALS AND METHODS

A total 211 samples from 101 normal controls and 110 patients (PPBL and SMZL) were assessed. Age, gender, full blood count, CPD, scatter, flags and CellaVision differentials were also considered. A ML model was built for true classification purposes.

RESULTS

PPBL and SMZL shared increased absolute lymphoid counts, atypical lymphoid flag presence and CPD values (8 out of 14). A typical "round-bottom-flask" shape scattergram was described for the first time for PPBL which was also present in 51.4% of SMZL cases. The developed ML model render a global classification accuracy of 93.4%, allowing the detection of all pathological cases, with mean misclassification rates of 12% among PPBL and SMZL.

CONCLUSION

Our ML model represents a new unbiased tool than can be widely applied in the laboratory as an aid for detection of PPBL.

摘要

背景与目的

持续性多克隆B细胞淋巴细胞增多症（PPBL）的诊断往往具有挑战性，因为缺乏特征且与脾边缘区淋巴瘤（SMZL）的外周表现存在重叠。为了获得PPBL检测和诊断的新线索，利用DxH 800分析仪提供的所有数据（包括散射和细胞群体数据（CPD）），并采用机器学习（ML）方法进行整合。

材料与方法

评估了来自101名正常对照和110名患者（PPBL和SMZL）的总共211份样本。还考虑了年龄、性别、全血细胞计数、CPD、散射、标记和CellaVision分类结果。构建了一个用于真实分类目的的ML模型。

结果

PPBL和SMZL的绝对淋巴细胞计数增加、存在非典型淋巴细胞标记以及CPD值（14项中有8项）存在共性。首次描述了PPBL典型的“圆底烧瓶”形散点图，51.4%的SMZL病例中也存在该散点图。所开发的ML模型的总体分类准确率为93.4%，能够检测出所有病理病例，PPBL和SMZL中的平均误分类率为12%。