Gantana Ethan J, Musekwa Ernest, Chapanduka Zivanai C
Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Department of Haematology, National Health Laboratory Service, Cape Town, South Africa.
Afr J Lab Med. 2024 Jul 11;13(1):2381. doi: 10.4102/ajlm.v13i1.2381. eCollection 2024.
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.
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.
骨髓中浆细胞的定量对于浆细胞肿瘤的诊断和分类至关重要。人们已经探索了多种方法，包括罗曼诺夫斯基染色的骨髓穿刺液（BMA）、免疫组织化学、流式细胞术和放射影像学。然而，诸如局灶性浸润和样本血液稀释等挑战会影响骨髓浆细胞百分比估计的可靠性。骨髓浆细胞百分比因方法而异，免疫组织化学染色的活检标本始终比单独的罗曼诺夫斯基染色BMA或流式细胞术产生更高的值。CD138或MUM1免疫组织化学以及全玻片图像的人工智能图像分析正成为准确识别和定量浆细胞的有前途的工具。放射影像学，特别是双能计算机断层扫描和放射组学等先进技术，在多发性骨髓瘤诊断方面显示出潜力，尽管标准化仍然是一个挑战。分子技术，如等位基因特异性寡核苷酸定量聚合酶链反应和下一代测序，有助于了解克隆性和可测量的残留疾病。虽然对于骨髓浆细胞定量的金标准方法尚无共识，但CD138染色的活检标本有利于准确估计，并在诊断和评估多发性骨髓瘤治疗反应中起关键作用。结合多种方法，如BMA、骨髓活检和流式细胞术，可提高浆细胞肿瘤诊断和分类的准确性。寻求金标准需要持续的研究和合作以改进现有方法。此外，数字病理学的兴起预计将重塑检验医学以及病理学家在数字时代的作用。
本文对骨髓中浆细胞估计的不同方法进行了全面综述和比较，突出了它们的优势和局限性。目的是提供有价值的见解，以指导选择用于准确浆细胞估计的最佳技术。
