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Counting mitoses with digital pathology in breast phyllodes tumors


Chow, ZL and Thike, AA and Li, HH and Md Nasir, NDM and Yeong, JPS and Tan, PH, Counting mitoses with digital pathology in breast phyllodes tumors, Archives of Pathology and Laboratory Medicine, 144, (11) pp. 1397-1400. ISSN 0003-9985 (2020) [Refereed Article]

Copyright Statement

2020 College of American Pathologists

DOI: doi:10.5858/arpa.2019-0435-OA


Context.-Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification. Objective.-To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis. Design.-Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted. Results.-Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size. Conclusions.-Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm.

Item Details

Item Type:Refereed Article
Research Division:Physical Sciences
Research Group:Medical and biological physics
Research Field:Medical and biological physics not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biomedical and clinical sciences
UTAS Author:Chow, ZL (Mr Zi Long Chow)
ID Code:152393
Year Published:2020
Web of Science® Times Cited:3
Deposited By:Mathematics
Deposited On:2022-08-18
Last Modified:2022-09-19

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