An Automated Pipeline for Cell Differentials on Whole-Slide Bone Marrow Aspirate Smears. (9th November 2022)

• Record Type: Journal Article
• Authors: Lewis, Joshua; Shebelut, Conrad; Drumheller, Bradley; Zhang, Xuebao; Shanmugam, Nithya; Attieh, Michel; Horwath, Michael; Khanna, Anurag; Smith, Geoffrey; Gutman, David
• Is Part Of: American journal of clinical pathology. Volume 158(2022)Supplement 1
• Page Start: S12
• Page End: S12
• Publication Date: 2022-11-09
• Subjects: Diagnosis, Laboratory -- Periodicals; Pathology -- Periodicals; 616.07
• DOI: 10.1093/ajcp/aqac126.020
• Languages: English
• ISSNs: 0002-9173

Abstract

Abstract: Current pathologic diagnosis of benign and neoplastic bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of nucleated cell populations to obtain a cell differential. This manual process has significant limitations, including the limited sample of cells analyzed by a conventional 500-cell differential compared to the thousands of nucleated cells present, as well as the inter-observer variability seen between differentials on single samples due to differences in cell selection and classification. To address these shortcomings, we developed an automated computational platform for obtaining cell differentials from scanned whole-slide BMAs at 40x magnification. This pipeline utilizes a sequential process of identifying BMA regions with high proportions of marrow nucleated cells that are ideal for cell counting, detecting individual cells within these optimal regions, and classifying cells into one of 11 types within the differential. Training of convolutional neural network models for region and cell classification, as well as a region-based convolutional neural network for cell detection, involved the generation of an annotated training data set containing 10, 948 BMA regions, 28, 914 cell boundaries, and 23, 609 cell classifications from 73 BMA slides. Among 44 testing BMA slides, an average of 19, 209 viable cells per slide were identified and used in automated cell differentials, with a range of 237 to 126, 483 cells. In comparing these automated cell differential percentages with corresponding manual differentials, cell type-specific correlation coefficients ranged from 0.913 for blast cells to 0.365 for myelocytes, with an average coefficient of 0.654 among all 11 cell types. A statistically significant concordance was observed among slides with blast percentages less or greater than 20% (p=1.0x10 -5 ) and with plasma cell percentages less or greater than 10% (p=5.9x10 -6 ) between automated and manual differentials, suggesting potential diagnostic utility of this automated pipeline for malignancies such as acute myeloid leukemia and multiple myeloma. Additionally, by simulating the manual counting of 500 cells within localized areas of a BMA slide and iterating over all optimal slide locations, we quantified the inter-observer variability associated with limited sample size in traditional BMA cell counting. Localized differentials exemplify an average variance ranging from 24.1% for erythroid precursors to 1.8% for basophils. Variance in localized differentials of up to 44.8% for blast cells and 36.9% for plasma cells was observed, demonstrating that sample classification based on diagnostic thresholds of cell populations is variable even between different areas within a single slide. Finally, pipeline outputs of region classification, cell detection, cell classification, and localized cell differentials can be visualized using whole-slide image analysis software. By improving cell sampling and reducing inter-observer variability, this automated pipeline has potential to improve the current standard of practice for utilizing BMA smears in the diagnosis of hematologic disorders.