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Bone Marrow Sampling and Transplants01:22

Bone Marrow Sampling and Transplants

Bone marrow transplant is a potential cure for several diseases, including cancer and specific genetic disorders. Notably, this procedure is applicable for patients suffering from aplastic anemia, certain types of leukemia, severe combined immunodeficiency disease (SCID), Hodgkin's disease, non-Hodgkin's lymphoma, multiple myeloma, thalassemia, sickle-cell disease, and certain cancers.
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Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells.

Katherina Baranova1, Christopher Tran2, Paul Plantinga2

  • 1Pathology and Laboratory Medicine, London Health Sciences Centre, London, Ontario, Canada kbaranov@uwo.ca.

Journal of Clinical Pathology
|May 6, 2021
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Summary

This study validates QuPath, an open-source digital pathology tool, for automatically quantifying CD138-positive bone marrow plasma cells (BMPCs). The tool shows reliable accuracy comparable to human pathologists, aiding in BMPC percentage analysis.

Keywords:
bone marrow neoplasmscomputer-assistedimage processingmultiple myelomapathologysurgical

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Hematopathology

Background:

  • Accurate quantification of bone marrow plasma cells (BMPCs) is crucial for diagnosing and monitoring plasma cell disorders.
  • Manual counting of BMPCs on CD138-stained bone marrow biopsies is labor-intensive and subject to inter-observer variability.
  • Development of automated tools is needed to improve efficiency and consistency in BMPC quantification.

Purpose of the Study:

  • To develop and validate QuPath, an open-source digital pathology software, for the automated quantification of CD138-positive BMPCs.
  • To assess the accuracy and reliability of QuPath's automated analysis against manual counting and pathologist estimates.
  • To establish QuPath as a validated tool for routine BMPC percentage analysis in clinical practice.

Main Methods:

  • CD138-stained bone marrow biopsy slides were analyzed using QuPath.
  • A training set of 10 biopsies involved manual cell counts to fine-tune QuPath's BMPC detection parameters, including neural network (NN) classification.
  • A testing set of 40 whole-slide images was analyzed, and QuPath's NN classifier output was compared with pathologist estimates.

Main Results:

  • QuPath's automated quantification of plasma cells showed strong correlation with manual counting (Pearson's r=0.96 for <30% BMPC).
  • The NN classifier demonstrated good concordance with pathologist estimates (intraclass correlation=0.83).
  • Agreement between the NN classifier and pathologists was comparable to inter-rater agreement between two human pathologists (weighted kappa values ranging from 0.80 to 0.90).

Conclusions:

  • QuPath is a validated open-source digital pathology tool for the reliable, automated quantification of BMPC percentage on CD138-stained slides.
  • The tool offers an acceptable error rate, assisting pathologists in BMPC analysis.
  • Automated analysis using QuPath can improve the efficiency and consistency of BMPC quantification in clinical settings.