PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma

Multiple Myeloma (MM) is a cytogenetically heterogeneous clonal plasma cell proliferative disease whose diagnosis is supported by analyses on histological slides of bone marrow aspirate. In summary, experts use a labor-intensive methodology to compute the ratio between plasma cells and non-plasma cells. Therefore, the key aspect of the methodology is identifying these cells, which relies on the experts' attention and experience. In this work, we present a valuable dataset comprising more than 5,000 plasma and non-plasma cells, labeled by experts, along with some patient diagnostics. We also share a Deep Neural Network model, as a benchmark, trained to identify and count plasma and non-plasma cells automatically. The contributions of this work are two-fold: (i) the labeled cells can be used to train new practitioners and support continuing medical education; and (ii) the design of new methods to identify such cells, improving the results presented by our benchmark. We emphasize that our work supports the diagnosis of MM in practical scenarios and paves new ways to advance the state-of-the-art.

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Institutions

Publication Details

Journal
Scientific Data
Published
2025-01-27
DOI
https://doi.org/10.1038/s41597-025-04459-1
Citations
2
Primary Topic
Multiple Myeloma Research and Treatments
Type
article
Field-Weighted Citation Impact
3.54

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article

PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma

Ricardo A. Rios, Brenno M. Alencar, Isabel Moraes, I Rosa et al.
2 citations
Scientific Data
Multiple Myeloma Research and Treatments
3.54
article

PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma

Ricardo A. Rios, Brenno M. Alencar, Isabel Moraes, I Rosa, Tiago J. S. Lopes, Songelí Menezes Freire, Tatiane Nogueira Rios, Roberto Meyer, Caio Lopes Borges Andrade, Allan S. dos Santos, Mariane M. dos Santos, Jorge L. S. B. Filho, Matheus A. Guimaraes, Herbert H. M. Santos, Maria I. C. S. e Silva, Marcos V. Ferreira, Gilson C. de Carvalho, Márcia M. L. Santos, Luciana M. P. B. Knop
article en
2 citations

Abstract

Multiple Myeloma (MM) is a cytogenetically heterogeneous clonal plasma cell proliferative disease whose diagnosis is supported by analyses on histological slides of bone marrow aspirate. In summary, experts use a labor-intensive methodology to compute the ratio between plasma cells and non-plasma cells. Therefore, the key aspect of the methodology is identifying these cells, which relies on the experts' attention and experience. In this work, we present a valuable dataset comprising more than 5,000 plasma and non-plasma cells, labeled by experts, along with some patient diagnostics. We also share a Deep Neural Network model, as a benchmark, trained to identify and count plasma and non-plasma cells automatically. The contributions of this work are two-fold: (i) the labeled cells can be used to train new practitioners and support continuing medical education; and (ii) the design of new methods to identify such cells, improving the results presented by our benchmark. We emphasize that our work supports the diagnosis of MM in practical scenarios and paves new ways to advance the state-of-the-art.

Scientific DataVol. 12(1)
Universidade Federal da Bahia (BR), Complexo Hospitalar Universitário Professor Edgard Santos (BR), Hospital São Rafael (BR)
National Center for Child Health and Development, Australian Government, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado da Bahia, Terumo Foundation for Life Sciences and Arts, Council for Science, Technology and Innovation, Morinaga Foundation For Health and Nutrition, Japan Society for the Promotion of Science
Openalex Percentile: Top 10%
Multiple Myeloma Research and Treatments
3.54
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