Morphology in the diagnosis of multiple myeloma: past, present, and future

Christian O. Ramos-Peñafiel, Hematology Service, Hospital General de México Dr. Eduardo Liceaga, Secretaría de Salud, Mexico City. México
Carlos Martínez-Murillo, Department of Hematology, Hospital General de México Dr. Eduardo Liceaga, Mexico City, Mexico
Juan F. Zazueta-Pozos, Department of Hematology, Hospital General de México Dr. Eduardo Liceaga, Mexico City, Mexico
Juan J. Navarrete-Pérez, Department of Anatomic Pathology, Hospital General de México Dr. Eduardo Liceaga, Secretaría de Salud, Mexico City, Mexico
Adán G. Gallardo-Rodríguez, School of Sport Sciences, Universidad Anahuac México, Huixquilucan, State of Mexico, Mexico

Multiple myeloma (MM) is a heterogeneous plasma cell neoplasm characterized by the overproduction of monoclonal immunoglobulins and disruption of normal hematopoiesis. Although the advent of modern diagnostic tools has transformed disease monitoring, cellular morphology continues to play a pivotal role in both initial recognition and complementary evaluation. Historically, abnormal plasma cell morphology was the cornerstone of diagnosis, with variants such as flame cells, Mott cells, Russell and Dutcher bodies providing important but sometimes subtle diagnostic clues. Bone marrow biopsies and immunohistochemistry remain essential for assessing the cellular microenvironment, establishing clonality through light chain restriction, and classifying disease extent. Flow cytometry (FC) has become a highly sensitive method to differentiate normal from clonal plasma cells, monitor measurable residual disease, and detect circulating tumor cells in peripheral blood, which may identify high-risk subgroups. Its reproducibility and rapid turnaround make it indispensable in current clinical practice, although challenges to standardization remain. Complementary molecular techniques and cytogenetic profiling further enhance risk stratification and therapeutic decision-making. Recently, artificial intelligence (AI) has emerged as an innovative tool capable of improving diagnostic accuracy, analyzing bone marrow smears with deep learning algorithms, and integrating clinical, genomic, and imaging data into advanced prognostic models. AI-based systems hold promise for reducing diagnostic times, minimizing observer variability, and increasing access to specialized evaluations through telemedicine. While technological advances have expanded the diagnostic armamentarium for MM, morphology remains a fundamental and irreplaceable element. Its integration with FC, immunohistochemistry, molecular tools, and emerging AI-based platforms defines a more comprehensive approach, ensuring accurate diagnosis, optimized monitoring, and personalized patient care.

Keywords: Multiple myeloma. Plasma cells. Flow cytometry. Immunohistochemistry. Bone marrow.