Literature DB >> 19886719

Mitotic index of invasive breast carcinoma. Achieving clinically meaningful precision and evaluating tertial cutoffs.

John S Meyer1, Eric Cosatto, Hans Peter Graf.   

Abstract

CONTEXT: Mitotic figure counts are related to breast cancer behavior but have not been sufficiently reproducible to be accepted for clinical decision-making.
OBJECTIVE: To improve reproducibility and accuracy of the mitotic count.
DESIGN: Mitotic index (MI) was defined as the mitotic cell count per 10 high-power fields (HPFs), an area 0.183 mm(2). Two to 6 replicate sets of 10 HPFs were counted from 328 invasive breast carcinomas. Standard errors and coefficients of variation for mean MI were compared with expected results predicted by the binomial distribution.
RESULTS: The boundaries for MI that separated the data into equal thirds (tertials) were 1.14 and 5.33. Standard errors and coefficients of variation for MI followed distributions predicted by binomial probability. Mean coefficient of variation was 147% for the low tertial, 72% for the midtertial, and 34.6% for the upper tertial.
CONCLUSIONS: Standard errors for MI based on a single count of 10 HPFs are too broad and coefficients of variation too large to be acceptable for clinical use. This is explained as a binomial probability effect, possibly with a contribution from tumor heterogeneity. Errors can be reduced in proportion to the square root of the number of sets of 10 HPFs counted. Tertial cutoffs of MI of the Nottingham system currently used in breast carcinoma grading are too high to be applicable to the population we studied. We recommend validation of cutoffs before they are applied to a particular population of breast carcinomas. Counting 5 sets of 10 HPFs is necessary to accurately rank carcinomas with low MIs.

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Mesh:

Year:  2009        PMID: 19886719     DOI: 10.5858/133.11.1826

Source DB:  PubMed          Journal:  Arch Pathol Lab Med        ISSN: 0003-9985            Impact factor:   5.534


  7 in total

1.  Standardization of whole slide image morphologic assessment with definition of a new application: Digital slide dynamic morphometry.

Authors:  Giacomo Puppa; Mauro Risio; Kieran Sheahan; Michael Vieth; Inti Zlobec; Alessandro Lugli; Sara Pecori; Lai Mun Wang; Cord Langner; Hiroyuki Mitomi; Takatoshi Nakamura; Masahiko Watanabe; Hideki Ueno; Jacques Chasle; Carlo Senore; Stephen A Conley; Paulette Herlin; Gregory Y Lauwers
Journal:  J Pathol Inform       Date:  2011-10-29

Review 2.  Regulation of mammalian nucleotide metabolism and biosynthesis.

Authors:  Andrew N Lane; Teresa W-M Fan
Journal:  Nucleic Acids Res       Date:  2015-01-27       Impact factor: 16.971

3.  Mitotic counts in breast cancer should be standardized with a uniform sample area.

Authors:  Michael Bonert; Angela J Tate
Journal:  Biomed Eng Online       Date:  2017-02-16       Impact factor: 2.819

4.  Defining the area of mitoses counting in invasive breast cancer using whole slide image.

Authors:  Asmaa Ibrahim; Ayat G Lashen; Ayaka Katayama; Raluca Mihai; Graham Ball; Michael S Toss; Emad A Rakha
Journal:  Mod Pathol       Date:  2021-12-11       Impact factor: 8.209

5.  A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor.

Authors:  Christof A Bertram; Marc Aubreville; Christian Marzahl; Andreas Maier; Robert Klopfleisch
Journal:  Sci Data       Date:  2019-11-21       Impact factor: 6.444

6.  A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research.

Authors:  Marc Aubreville; Christof A Bertram; Taryn A Donovan; Christian Marzahl; Andreas Maier; Robert Klopfleisch
Journal:  Sci Data       Date:  2020-11-27       Impact factor: 6.444

7.  Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region.

Authors:  Marc Aubreville; Christof A Bertram; Christian Marzahl; Corinne Gurtner; Martina Dettwiler; Anja Schmidt; Florian Bartenschlager; Sophie Merz; Marco Fragoso; Olivia Kershaw; Robert Klopfleisch; Andreas Maier
Journal:  Sci Rep       Date:  2020-10-05       Impact factor: 4.379

  7 in total

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