Literature DB >> 29757362

Is a 500-Cell Count Necessary for Bone Marrow Differentials?: A Proposed Analytical Method for Validating a Lower Cutoff.

Ahmed A Abdulrahman1, Kirtesh H Patel1, Tong Yang1, David D Koch1, Sarah M Sivers1, Geoffrey H Smith1, David L Jaye1.   

Abstract

OBJECTIVES: By convention, 500 cells are counted for bone marrow aspirate differentials. Evidence supporting such a cutoff is lacking. We hypothesized that 300-cell counts could be sufficient.
METHODS: Cell count results from 165 cases, for which values were recorded at 300 and 500 cells, were analyzed. We tested for statistical differences and changes in diagnostic classification between the two cutoffs.
RESULTS: Three hundred cell counts did not produce diagnostically different results, particularly for myeloblasts and plasma cells, where cell percentages are critical for disease classification. Method comparison analysis did not reach statistical significance for any cell type when comparing the two methods. Bias plots showed narrow, even spread about the mean bias. Contingency table analysis yielded no significant diagnostic discrepancies.
CONCLUSIONS: Performing differential counts on 300 cells would produce clinically and statistically similar results to 500 cells. Reducing the cell number counted has potential cost/labor reductions without affecting quality of care.

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Year:  2018        PMID: 29757362     DOI: 10.1093/ajcp/aqy034

Source DB:  PubMed          Journal:  Am J Clin Pathol        ISSN: 0002-9173            Impact factor:   2.493


  3 in total

1.  Efficient and Highly Accurate Diagnosis of Malignant Hematological Diseases Based on Whole-Slide Images Using Deep Learning.

Authors:  Chong Wang; Xiu-Li Wei; Chen-Xi Li; Yang-Zhen Wang; Yang Wu; Yan-Xiang Niu; Chen Zhang; Yi Yu
Journal:  Front Oncol       Date:  2022-06-10       Impact factor: 5.738

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

Authors:  Ramraj Chandradevan; Ahmed A Aljudi; Bradley R Drumheller; Nilakshan Kunananthaseelan; Mohamed Amgad; David A Gutman; Lee A D Cooper; David L Jaye
Journal:  Lab Invest       Date:  2019-09-30       Impact factor: 5.662

3.  Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells.

Authors:  Iori Nakamura; Haruhi Ida; Mayu Yabuta; Wataru Kashiwa; Maho Tsukamoto; Shigeki Sato; Syuichi Ota; Naoki Kobayashi; Hiromi Masauzi; Kazunori Okada; Sanae Kaga; Keiko Miwa; Hiroshi Kanai; Nobuo Masauzi
Journal:  Sci Rep       Date:  2022-10-06       Impact factor: 4.996

  3 in total

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