将基于深度学习的血液异常检测作为 VEXAS 综合征筛查工具

IF 2.2 4区 医学 Q3 HEMATOLOGY International Journal of Laboratory Hematology Pub Date : 2024-09-14 DOI:10.1111/ijlh.14368
Cédric De Almeida Braga, Maxence Bauvais, Pierre Sujobert, Maël Heiblig, Maxime Jullien, Baptiste Le Calvez, Camille Richard, Valentin Le Roc'h, Emmanuelle Rault, Olivier Hérault, Pierre Peterlin, Alice Garnier, Patrice Chevallier, Simon Bouzy, Yannick Le Bris, Antoine Néel, Julie Graveleau, Olivier Kosmider, Perrine Paul‐Gilloteaux, Nicolas Normand, Marion Eveillard
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A deep learning algorithm is then proposed for automatic detection of these features.MethodsA multicentric dataset, comprising 9514 annotated PMN images was gathered, including <jats:italic>UBA1</jats:italic> mutated VEXAS (<jats:italic>n</jats:italic> = 25), <jats:italic>UBA1</jats:italic> wildtype myelodysplastic (<jats:italic>n</jats:italic> = 14), and <jats:italic>UBA1</jats:italic> wildtype cytopenic patients (<jats:italic>n</jats:italic> = 25). Statistical analysis on a subset of patients was performed to screen for significant abnormalities. 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引用次数: 0

摘要

导言VEXAS是2020年描述的一种综合征,由UBA1基因突变引起,表现出大量多形性的临床和血液学特征。然而,在筛查阶段,这些标准对于区分 VEXAS 和其他炎症缺乏意义。因此,这项工作首先侧重于在外周血(PB)多形核细胞(PMN)中挑出表明该综合征的发育不良特征。方法收集了一个多中心数据集,包括 9514 张注释 PMN 图像,其中包括 UBA1 突变 VEXAS(n = 25)、UBA1 野生型骨髓增生异常(n = 14)和 UBA1 野生型细胞减少患者(n = 25)。对部分患者进行了统计分析,以筛查重大异常。结果发现,VEXAS 患者与细胞增生症和骨髓增生异常对照组之间,具有假性佩尔格、核尖峰、空泡和颗粒减少的 PMN 的比例存在显著差异。这项研究表明,计算机辅助分析 PB 涂片(侧重于疑似 VEXAS 病例)可为确定哪些患者应接受分子检测提供有价值的见解。所介绍的深度学习方法可帮助血液学专家在开始进一步分析前引导他们的怀疑。
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Deep Learning‐Based Blood Abnormalities Detection as a Tool for VEXAS Syndrome Screening
IntroductionVEXAS is a syndrome described in 2020, caused by mutations of the UBA1 gene, and displaying a large pleomorphic array of clinical and hematological features. Nevertheless, these criteria lack significance to discriminate VEXAS from other inflammatory conditions at the screening step. This work hence first focused on singling out dysplastic features indicative of the syndrome among peripheral blood (PB) polymorphonuclears (PMN). A deep learning algorithm is then proposed for automatic detection of these features.MethodsA multicentric dataset, comprising 9514 annotated PMN images was gathered, including UBA1 mutated VEXAS (n = 25), UBA1 wildtype myelodysplastic (n = 14), and UBA1 wildtype cytopenic patients (n = 25). Statistical analysis on a subset of patients was performed to screen for significant abnormalities. Detection of these features on PB was then automated with a convolutional neural network (CNN) for multilabel classification.ResultsSignificant differences were observed in the proportions of PMNs with pseudo‐Pelger, nuclear spikes, vacuoles, and hypogranularity between patients with VEXAS and both cytopenic and myelodysplastic controls.Automatic detection of these abnormalities yielded AUCs in the range [0.85–0.97] and a F1‐score of 0.70 on the test set. A VEXAS screening score was proposed, leveraging the model outputs and predicting the UBA1 mutational status with 0.82 sensitivity and 0.71 specificity on the test patients.ConclusionThis study suggests that computer‐assisted analysis of PB smears, focusing on suspected VEXAS cases, can provide valuable insights for determining which patients should undergo molecular testing. The presented deep learning approach can help hematologists direct their suspicions before initiating further analyses.
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来源期刊
CiteScore
4.50
自引率
6.70%
发文量
211
审稿时长
6-12 weeks
期刊介绍: The International Journal of Laboratory Hematology provides a forum for the communication of new developments, research topics and the practice of laboratory haematology. The journal publishes invited reviews, full length original articles, and correspondence. The International Journal of Laboratory Hematology is the official journal of the International Society for Laboratory Hematology, which addresses the following sub-disciplines: cellular analysis, flow cytometry, haemostasis and thrombosis, molecular diagnostics, haematology informatics, haemoglobinopathies, point of care testing, standards and guidelines. The journal was launched in 2006 as the successor to Clinical and Laboratory Hematology, which was first published in 1979. An active and positive editorial policy ensures that work of a high scientific standard is reported, in order to bridge the gap between practical and academic aspects of laboratory haematology.
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