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.

Centro de Investigación Biomédica en Red de Cáncer Instituto de Salud Carlos 3 Madrid Spain

Clinic 3 Special Hematology Laboratory Rostock University Medical School Rostock Germany

Clinica Universidad de Navarra Centro de Investigacion Medica Aplicada CIBER ONC CB16 12 00369 Pamplona Spain

Clinical and Translational Research Program Cancer Research Center University of Salamanca Salamanca Spain

CLIP Department of Pediatric Hematology and Oncology Charles University and University Hospital Motol Prague Czech Republic

Cytognos SL Salamanca Spain

Department of Diagnostic Sciences Ghent University Ghent Belgium

Department of Immunology Erasmus MC University Medical Center Rotterdam Rotterdam Netherlands

Department of Immunology Leiden University Medical Center Leiden Netherlands; and

Department of Internal Medicine 2 University of Schleswig Holstein Kiel Germany

Department of Medicine and Cytometry Service University of Salamanca Salamanca Spain

FACS Stem Cell Laboratory Kantonsspital Aarau AG Aarau Switzerland

Secção de Citometria de Fluxo Instituto Português de Oncologia de Lisboa Francisco Gentil Lisbon Portugal

Systems and Computing Department Federal University of Rio de Janeiro Rio de Janeiro Brazil

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