Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma

Habil Zare, Ali Bashashati, Robert Kridel, Nima Aghaeepour, Gholamreza Haffari, Joseph M. Connors, Randy D. Gascoyne, Arvind Gupta, Ryan R. Brinkman, Andrew P. Weng

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Mantle cell lymphoma (MCL) and small lymphocytic lymphoma (SLL) exhibit similar but distinct immunophenotypic profiles. Many cases can be diagnosed readily by flow cytometry (FCM) alone; however, ambiguous cases are frequently encountered and necessitate additional studies, including immunohistochemical staining for cyclin D1 and fluorescence in situ hybridization for IgH-CCND1 rearrangement. To determine if greater diagnostic accuracy could be achieved from FCM data alone, we developed an unbiased, machine-based algorithm to identify features that best distinguish between the 2 diseases. By applying conventional diagnostic criteria to the flow cytometry data, we were able to assign 28 of 44 (64%) MCL and 48 of 70 (69%) SLL cases correctly. In contrast, we were able to assign all 44 (100%) MCL and 68 of 70 (97%) SLL cases correctly using a novel set of criteria, as identified by our automated approach. The most discriminating feature was the CD20/CD23 mean fluorescence intensity ratio, and we found unexpectedly that inclusion of FMC7 expression in the diagnostic algorithm actually reduced its accuracy. This study demonstrates that computational methods can be used on existing clinical FCM data to improve diagnostic accuracy and suggests similar computational approaches could be used to identify novel prognostic markers and perhaps subdivide existing or define new diagnostic entities.

Original languageEnglish (US)
Pages (from-to)75-85
Number of pages11
JournalAmerican journal of clinical pathology
Volume137
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

Keywords

  • Automated data analysis
  • Bioinformatics
  • Diagnostic algorithm
  • Flow cytometry
  • Immunopathology
  • Mantle cell lymphoma
  • Small lymphocytic lymphoma

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

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