Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells

Abstract

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.

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Acknowledgements

This research was supported by the National Cancer Institute Informatics Technology for Cancer Research grants U01CA220401 and U24CA19436201. We thank Drs Geoffrey Smith and Thomas Durant for helpful suggestions and comments on the paper. RC and NK performed this work as part of a senior research course at the Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka (instructor Dr. N. W. Nuwan Dayananda, PhD). We thank the University of Moratuwa and Dr Dayanada for their support.

Funding

This research was supported in part by the National Cancer Institute Informatics Technology for Cancer Research grants U01CA220401 and U24CA19436201.

Author information

AAA and BRD generated and reviewed annotations. RC and NK developed algorithms and performed experiments. MA and DAG provided technical support for the annotation platform and database. LADC directed development and implementation of the annotation protocol, all computational approaches, and designed experiments. DLJ reviewed annotations, provided slides, conceived of the problem, and directed the project. RC, AAA, LADC, and DLJ wrote and edited the paper.

Correspondence to Lee A. D. Cooper or David L. Jaye.

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This work was presented at the Academy of Clinical Laboratory Physicians and Scientists (ACLPS) 2018 meeting as a platform presentation.

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Chandradevan, R., Aljudi, A.A., Drumheller, B.R. et al. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Lab Invest 100, 98–109 (2020) doi:10.1038/s41374-019-0325-7

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