Startseite Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears
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Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears

  • Floris Chabrun , Valentin Lacombe , Xavier Dieu , Franck Geneviève und Geoffrey Urbanski ORCID logo EMAIL logo
Veröffentlicht/Copyright: 2. Februar 2023
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Clinical Chemistry and Laboratory Medicine (CCLM)
Aus der Zeitschrift Clinical Chemistry and Laboratory Medicine (CCLM) Band 61 Heft 7

Abstract

Objectives

VEXAS syndrome is a newly described autoinflammatory disease associated with UBA1 somatic mutations and vacuolization of myeloid precursors. This disease possesses an increasingly broad spectrum, leading to an increase in the number of suspected cases. Its diagnosis via bone-marrow aspiration and UBA1-gene sequencing is time-consuming and expensive. This study aimed at analyzing peripheral leukocytes using deep learning approaches to predict VEXAS syndrome in comparison to differential diagnoses.

Methods

We compared leukocyte images from blood smears of three groups: participants with VEXAS syndrome (identified UBA1 mutation) (VEXAS); participants with features strongly suggestive of VEXAS syndrome but without UBA1 mutation (UBA1-WT); participants with a myelodysplastic syndrome and without clinical suspicion of VEXAS syndrome (MDS). To compare images of circulating leukocytes, we applied a two-step procedure. First, we used self-supervised contrastive learning to train convolutional neural networks to translate leukocyte images into lower-dimensional encodings. Then, we employed support vector machine to predict patients’ condition based on those leukocyte encodings.

Results

The VEXAS, UBA1-WT, and MDS groups included 3, 3, and 6 patients respectively. Analysis of 33,757 images of neutrophils and monocytes enabled us to distinguish VEXAS patients from both UBA1-WT and MDS patients, with mean ROC-AUCs ranging from 0.87 to 0.95.

Conclusions

Image analysis of blood smears via deep learning accurately distinguished neutrophils and monocytes drawn from patients with VEXAS syndrome from those of patients with similar clinical and/or biological features but without UBA1 mutation. Our findings offer a promising pathway to better screening for this disease.

Keywords: autoinflammatory disorder; deep learning; myelodysplastic syndromes; VEXAS syndrome
Corresponding author: Geoffrey Urbanski, MD, MPH, PhD, University of Angers, Inserm 1083, CNRS 6015, Mitolab Team, MITOVASC Institute, SFR ICAT, F-49000 Angers, France; Service de Médecine Interne et Immunologie Clinique, Centre Hospitalier Universitaire Angers, Angers, France; and Department of Orofacial Sciences, School of Dentistry, University of California, San Francisco, CA, USA, Phone: +33 2 41 35 40 03, +1-415/476-5976, E-mail:
Floris Chabrun and Valentin Lacombe contributed equally to this work.

Acknowledgments

This work was granted access to the HPC resources of IDRIS under the allocation 2022-AD011011303R2 made by GENCI. We are thankful to Dr. Marc Ferré for providing those resources, and to Mr. Samuel Ross Gilbert for proofreading the English version of this manuscript.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: This study was approved by the ethics committee of Angers University Hospital (#2022–094) and was conducted in compliance with the Declaration of Helsinki. All participants gave non-opposition informed consent. We applied the STARD (Standards for Reporting Diagnostic accuracy studies) recommendations.

  6. Data availability: Processed data (encodings) along with deep learning models are publicly available online at “https://github.com/fchabrun/VEXAS-BloodSmear”.

References

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2022-1283).

Received: 2022-12-18
Accepted: 2023-01-17
Published Online: 2023-02-02
Published in Print: 2023-06-27

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2022-1283
Schlagwörter für diesen Artikel
autoinflammatory disorder; deep learning; myelodysplastic syndromes; VEXAS syndrome

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. ChatGPT: Angel or Demond? Critical thinking is still needed
  4. Mini Review
  5. Diagnostic accuracy of Siemens SARS-CoV-2 Antigen (CoV2Ag) chemiluminescent immunoassay for diagnosing acute SARS-CoV-2 infection: a pooled analysis
  6. Opinion Papers
  7. Neurofilament light chain as neuronal injury marker – what is needed to facilitate implementation in clinical laboratory practice?
  8. Clinical evidence requirements according to the IVDR 2017/746: practical tools and references for underpinning clinical evidence of IVD-MDs
  9. EFLM Papers
  10. Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI)
  11. Detection of antinuclear antibodies: recommendations from EFLM, EASI and ICAP
  12. Analytical aspects of the antinuclear antibody test by HEp-2 indirect immunofluorescence: EFLM report on an international survey
  13. Guidelines and Recommendations
  14. Variability of cardiac troponin levels in normal subjects and in patients with cardiovascular diseases: analytical considerations and clinical relevance
  15. General Clinical Chemistry and Laboratory Medicine
  16. Pre-analytical long-term stability of neopterin and neurofilament light in stored cerebrospinal fluid samples
  17. Development of a candidate reference measurement procedure by ID-LC-MS/MS for total tau protein measurement in cerebrospinal fluid (CSF)
  18. Assessing the commutability of candidate reference materials for the harmonization of neurofilament light measurements in blood
  19. Surface plasmon resonance assays for the therapeutic drug monitoring of infliximab indicate clinical relevance of anti-infliximab antibody binding properties
  20. An antibody-free LC-MS/MS method for the quantification of sex hormone binding globulin in human serum and plasma
  21. Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears
  22. Establishing quality indicators for point of care glucose testing: recommendations from the Canadian Society for Clinical Chemists Point of Care Testing and Quality Indicators Special Interest Groups
  23. Clinical implication by differential analytical performances of serum free light chain quantitation analysis using fully automated analyzers
  24. Simultaneous analysis of antihyperglycemic small molecule drugs and peptide drugs by means of dual liquid chromatography high-resolution mass spectrometry
  25. Reference Values and Biological Variations
  26. Reference intervals for thyroid biomarkers to enhance the assessment of thyroid status in childhood and adolescence
  27. Biological variation of CA 15-3, CA 125 and HE 4 on lithium heparinate plasma in apparently healthy Caucasian volunteers
  28. Cancer Diagnostics
  29. Individual risk prediction of high grade prostate cancer based on the combination between total prostate-specific antigen (PSA) and free to total PSA ratio
  30. Cardiovascular Diseases
  31. Using logistic regression models to investigate the effects of high-sensitivity cardiac troponin T confounders on ruling in acute myocardial infarction
  32. Infectious Diseases
  33. Assessment of humoral and cellular immunity after bivalent BNT162b2 vaccination and potential association with reactogenicity
  34. Three rounds of a national external quality assessment reveal a link between disharmonic anti-SARS-CoV-2 antibody quantifications and the infection stage
  35. Corrigendum
  36. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories
  37. Letters to the Editor
  38. Please do not call it Theranos
  39. Lot-to-lot bias for high-sensitivity cardiac troponin I concentrations ≥1000 ng/L
  40. Lot-to-lot reagent changes and commutability of quality testing materials for total bile acid measurements
  41. On the importance of sampling interval in studies of biological variation in thyroid function
  42. Stability of plasma renin concentration based on plasma freezing time, as an adjunct to the stability data reported in the paper by Hepburn and others
  43. Falsely low beta-hCG results in pregnant woman on Siemens Atellica: don’t forget the “hook effect”
  44. Proenkephalin A 119–159 (penKid) – a novel biomarker and its quantification on the Nexus IB10 POC system for assessing kidney function
  45. Investigating the polyuria-polydipsia syndrome: the “PP” Shiny app
  46. Evaluation of a novel, stand-alone system to measure interleukin-6
  47. Spurious low WBC count in the WNR channel of Sysmex XN-9000 hematology analyzer in a case with leukocyte aggregation
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