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

• Type of Material: Article
• Authors: Ramraj Chandradevan, Emory University; Ahmed Aljudi, Emory University; Bradley R. Drumheller, Emory University; Nilakshan Kunananthaseelan, Emory University; Mohamed Amgad, Emory University; David Gutman, Emory University; Lee Cooper, Emory University; David Jaye, Emory University
• Language: English
• Date: 2020-01-01
• Publisher: Springer Nature
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s), under exclusive licence to United States and Canadian Academy of Pathology 2019.
• Final Published Version (URL): http://dx.doi.org/10.1038/s41374-019-0325-7
• Title of Journal or Parent Work: Laboratory Investigation
• Volume: 100
• Issue: 1
• Start Page: 98
• End Page: 109
• Grant/Funding Information: This research was supported by the National Cancer Institute Informatics Technology for Cancer Research grants U01CA220401 and U24CA19436201.
• Keywords: Pathology; Histology; Life Sciences & Biomedicine; Research & Experimental Medicine; DM96 system; Science & Technology; Medicine, Research & Experimental; Performance evaluation
• Research Categories: Health Sciences, Pathology; Biology, Cell; Health Sciences, Oncology; Biology, Neuroscience

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.