Review Article - Special Collection: Transforming African LDS with DT and AI

Advances in estimating plasma cells in bone marrow: A comprehensive method review

Ethan J. Gantana, Ernest Musekwa, Zivanai C. Chapanduka
African Journal of Laboratory Medicine | Vol 13, No 1 | a2381 | DOI: https://doi.org/10.4102/ajlm.v13i1.2381 | © 2024 Ethan J. Gantana, Ernest Musekwa, Zivanai C. Chapanduka | This work is licensed under CC Attribution 4.0
Submitted: 30 November 2023 | Published: 11 July 2024

About the author(s)

Ethan J. Gantana, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town Department of Haematology, National Health Laboratory Service, Cape Town, South Africa
Ernest Musekwa, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town Department of Haematology, National Health Laboratory Service, Cape Town, South Africa
Zivanai C. Chapanduka, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town Department of Haematology, National Health Laboratory Service, Cape Town, South Africa

Abstract

The quantitation of plasma cells in bone marrow (BM) is crucial for diagnosing and classifying plasma cell neoplasms. Various methods, including Romanowsky-stained BM aspirates (BMA), immunohistochemistry, flow cytometry, and radiological imaging, have been explored. However, challenges such as patchy infiltration and sample haemodilution can impact the reliability of BM plasma cell percentage estimates. Bone marrow plasma cell percentage varies across methods, with immunohistochemically stained biopsies consistently yielding higher values than Romanowsky-stained BMA or flow cytometry alone. CD138 or MUM1 immunohistochemistry and artificial intelligence image analysis on whole-slide images are emerging as promising tools for accurate plasma cell identification and quantification. Radiological imaging, particularly with advanced technologies like dual-energy computed tomography and radiomics, shows potential for multiple myeloma diagnosis, although standardisation remains a challenge. Molecular techniques, such as allele-specific oligonucleotide quantitative polymerase chain reaction and next-generation sequencing, offer insights into clonality and measurable residual disease. While no consensus exists on a gold standard method for BM plasma cell quantitation, CD138-stained biopsies are favoured for accurate estimation and play a pivotal role in diagnosing and assessing multiple myeloma treatment responses. Combining multiple methods, such as BMA, BM biopsy, and flow cytometry, enhances accuracy of diagnosis and classification of plasma cell neoplasms. The quest for a gold standard requires ongoing research and collaboration to refine existing methods. Furthermore, the rise of digital pathology is anticipated to reshape laboratory medicine and the role of pathologists in the digital era.

What this study adds: This article adds a comprehensive review and comparison of different methods for plasma cell estimation in the bone marrow, highlighting their strengths and limitations. The goal is to contribute valuable insights that can guide the selection of optimal techniques for accurate plasma cell estimation.


Keywords

plasma cell neoplasms; multiple myeloma; digital pathology; artificial intelligence; haematology; pathology

Sustainable Development Goal

Goal 3: Good health and well-being

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