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

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 nine 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 utilized Canonical Correlation Analysis of 176 training cases to project the multi-dimensional space of all 26 immunophenotypic parameters into 36 two-dimensional plots for each possible pair-wise 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.