Evidence map›Paper›PMID 34814179›Full record

ArticleBlood advances2022

Expert-independent classification of mature B-cell neoplasms using standardized flow cytometry: a multicentric study.

Sebastian Böttcher, Robby Engelmann, Georgiana Grigore, Paula Fernandez, Joana Caetano, Juan Flores-Montero, Vincent H J van der Velden, Michaela Novakova, Jan Philippé, Matthias Ritgen and 9 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Blood advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 21 citations in OpenAlex.

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  8. Flow Cytometry of CD5-Positive Hairy Cell Leukemia.Molecular diagnosis & therapy · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 10 institutions in 7 countries.

Sebastian BöttcherClinic III (Hematology, Oncology and Palliative Medicine), Special Hematology Laboratory, Rostock University Medical School, Rostock, Germany.ORCID 0000-0002-3603-761X
Robby EngelmannClinic III (Hematology, Oncology and Palliative Medicine), Special Hematology Laboratory, Rostock University Medical School, Rostock, Germany.ORCID 0000-0002-1048-3638
Georgiana GrigoreCytognos SL, Salamanca, Spain.
Paula FernandezFACS/Stem Cell Laboratory, Kantonsspital Aarau AG, Aarau, Switzerland.
Joana CaetanoSecção de Citometria de Fluxo, Instituto Português de Oncologia de Lisboa Francisco Gentil, Lisbon, Portugal.ORCID 0000-0001-7509-9066
Juan Flores-MonteroClinical and Translational Research Program, Cancer Research Center (IBMCC-CSIC/USAL-IBSAL), University of Salamanca, Salamanca, Spain.ORCID 0000-0002-1119-4387
Vincent H J van der VeldenDepartment of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.
Michaela NovakovaCLIP - Department of Pediatric Hematology and Oncology, Charles University and University Hospital Motol, Prague, Czech Republic.
Jan PhilippéDepartment of Diagnostic Sciences, Ghent University, Ghent, Belgium.ORCID 0000-0003-4857-5746
Matthias RitgenDepartment of Internal Medicine II, University of Schleswig-Holstein, Kiel, Germany.
Leire BurgosClinica Universidad de Navarra, Centro de Investigacion Medica Aplicada (CIMA), Instituto de Investigacion Sanitaria de Navarra (IDISNA), CIBER-ONC CB16/12/00369, Pamplona, Spain.ORCID 0000-0003-0998-2496
Quentin LecrevisseCytognos SL, Salamanca, Spain.ORCID 0000-0001-8715-5846
Sandra LangeClinic III (Hematology, Oncology and Palliative Medicine), Special Hematology Laboratory, Rostock University Medical School, Rostock, Germany.
Tomas KalinaCLIP - Department of Pediatric Hematology and Oncology, Charles University and University Hospital Motol, Prague, Czech Republic.ORCID 0000-0003-4475-2872
Javier Verde VelascoCytognos SL, Salamanca, Spain.
Rafael Fluxa RodriguezCytognos SL, Salamanca, Spain.
Jacques J M van DongenDepartment of Immunology, Leiden University Medical Center, Leiden, Netherlands; and.ORCID 0000-0002-3650-7087
Carlos E PedreiraSystems and Computing Department, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Alberto OrfaoClinical and Translational Research Program, Cancer Research Center (IBMCC-CSIC/USAL-IBSAL), University of Salamanca, Salamanca, Spain.
Universidad de Salamanca · ESUniversity of Rostock · DECharles University · CZChristian-Albrechts-Universität zu Kiel · DEErasmus MC · NLGhent University · BEKantonsspital Aarau · CHLeiden University Medical Center · NLNavarre Institute of Health Research · ESUniversidade Federal do Rio de Janeiro · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reproducible expert-independent flow-cytometric criteria for the differential diagnoses between mature B-cell neoplasms are lacking. We developed an algorithm-driven classification for these lymphomas by flow cytometry and compared it to the WHO gold standard diagnosis. Overall, 662 samples from 662 patients representing 9 disease categories were analyzed at 9 laboratories using the previously published EuroFlow 5-tube-8-color B-cell chronic lymphoproliferative disease antibody panel. Expression levels of all 26 markers from the panel were plotted by B-cell entity to construct a univariate, fully standardized diagnostic reference library. For multivariate data analysis, we subsequently used canonical correlation analysis of 176 training cases to project the multidimensional space of all 26 immunophenotypic parameters into 36 2-dimensional plots for each possible pairwise differential diagnosis. Diagnostic boundaries were fitted according to the distribution of the immunophenotypes of a given differential diagnosis. A diagnostic algorithm based on these projections was developed and subsequently validated using 486 independent cases. Negative predictive values exceeding 92.1% were observed for all disease categories except for follicular lymphoma. Particularly high positive predictive values were returned in chronic lymphocytic leukemia (99.1%), hairy cell leukemia (97.2%), follicular lymphoma (97.2%), and mantle cell lymphoma (95.4%). Burkitt and CD10+ diffuse large B-cell lymphomas were difficult to distinguish by the algorithm. A similar ambiguity was observed between marginal zone, lymphoplasmacytic, and CD10- diffuse large B-cell lymphomas. The specificity of the approach exceeded 98% for all entities. The univariate immunophenotypic library and the multivariate expert-independent diagnostic algorithm might contribute to increased reproducibility of future diagnostics in mature B-cell neoplasms.

Indexed as

Lymphoma, FollicularLymphoma, Large B-Cell, DiffuseAdultFlow CytometryHumansImmunophenotypingReproducibility of Results

Identifiers

PMID34814179
PMCPMC8945320
OpenAlexW3216280607

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.