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Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells
  1. Katherina Baranova1,
  2. Christopher Tran1,
  3. Paul Plantinga1,
  4. Nikhil Sangle1,2
  1. 1 Pathology and Laboratory Medicine, London Health Sciences Centre, London, Ontario, Canada
  2. 2 Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, London, Ontario, Canada
  1. Correspondence to Dr Katherina Baranova, Pathology and Laboratory Medicine, London Health Sciences Center, London, Ontario, Canada; kbaranov{at}


Aims The objective of this study was to develop and validate an open-source digital pathology tool, QuPath, to automatically quantify CD138-positive bone marrow plasma cells (BMPCs).

Methods We analysed CD138-scanned slides in QuPath. In the initial training phase, manual positive and negative cell counts were performed in representative areas of 10 bone marrow biopsies. Values from the manual counts were used to fine-tune parameters to detect BMPCs, using the positive cell detection and neural network (NN) classifier functions. In the testing phase, whole-slide images in an additional 40 cases were analysed. Output from the NN classifier was compared with two pathologist’s estimates of BMPC percentage.

Results The training set included manual counts ranging from 2403 to 17 287 cells per slide, with a median BMPC percentage of 13% (range: 3.1%–80.7%). In the testing phase, the quantification of plasma cells by image analysis correlated well with manual counting, particularly when restricted to BMPC percentages of <30% (Pearson’s r=0.96, p<0.001). Concordance between the NN classifier and the pathologist whole-slide estimates was similarly good, with an intraclass correlation of 0.83 and a weighted kappa for the NN classifier of 0.80 with the first rater and 0.90 with the second rater. This was similar to the weighted kappa between the two human raters (0.81).

Conclusions This represents a validated digital pathology tool to assist in automatically and reliably counting BMPC percentage on CD138-stained slides with an acceptable error rate.

  • bone marrow neoplasms
  • image processing
  • computer-assisted
  • multiple myeloma
  • pathology
  • surgical

Data availability statement

No data are available. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data (whole-slide scanned images) is not available.

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Data availability statement

No data are available. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data (whole-slide scanned images) is not available.

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  • Handling editor Mary Frances McMullin.

  • Twitter @KBaranoma

  • Contributors NS conceived and designed the initial experiment, and supervised the team. PP reviewed the slides and provided estimated bone marrow plasma cell percentage for the experiments. CT supervised KB in implementation of the project and assisted in carrying out case search and machine-learning development. KB performed the experiments and analysed the data. All authors contributed to the manuscript and the interpretation of results.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.