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Abstract
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.
Footnotes
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.
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