Automated digital enumeration of plasma cells in bone marrow trephine biopsies of multiple myeloma

Research output: Contribution to journalArticlepeer-review

Abstract

Aims: Determination of the number of plasma cells in bone marrow biopsies is required for the diagnosis and ongoing evaluation of plasma cell neoplasms. We developed an automated digital enumeration platform to assess plasma cells identified by antigen expression in whole bone marrow sections in multiple myeloma, and compared it with manual assessments. Methods: Bone marrow trephine biopsy specimens from 91 patients with multiple myeloma at diagnosis, remission and relapse were stained for CD138 and multiple myeloma oncogene 1 (MUM1). Manual assessment and digital quantification were performed for plasma cells in the entire trephine section. Concordance rates between manual and digital methods were evaluated for each antigen by intraclass correlation analyses (ICC) with associated Spearman's correlations. Results: The digital platform counted 16 484-1 118 868 cells and the per cent CD138 and MUM1-positive plasma cells ranged from 0.05% to 93.5%. Overall concordance between digital and manual methods was 0.63 for CD138 and 0.89 for MUM1. Concordance was highest with diffuse plasma cell infiltrates (MUM1: ICC=0.90) and lowest when in microaggregates (CD138: ICC=0.13). Manual counts exceeded digital quantifications for both antigens (CD138: mean=26.4%; MUM1: mean=9.7%). Diagnostic or relapse threshold counts, as determined by CD138 manual assessments, were not reached with digital counting for 16 cases (18%). Conclusions: Automated digital enumeration of the entire, immunohistochemically stained bone marrow biopsy section can accurately determine plasma cell burden, irrespective of pattern and extent of disease (as low as 0.05%). This increases precision over manual visual assessments which tend to overestimate plasma burden, especially for CD138, and when plasma cells are in clusters.

Original languageEnglish
Article numberjclinpath-2020-207066
Pages (from-to)50-57
Number of pages8
JournalJournal of Clinical Pathology
Volume75
Issue number1
Early online date24 Nov 2020
DOIs
Publication statusPublished - 1 Jan 2022

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