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Characterizing Diagnostic Search Patterns in Digital Breast Pathology: Scanners and Drillers

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Abstract

Following a baseline demographic survey, 87 pathologists interpreted 240 digital whole slide images of breast biopsy specimens representing a range of diagnostic categories from benign to atypia, ductal carcinoma in situ, and invasive cancer. A web-based viewer recorded pathologists’ behaviors while interpreting a subset of 60 randomly selected and randomly ordered slides. To characterize diagnostic search patterns, we used the viewport location, time stamp, and zoom level data to calculate four variables: average zoom level, maximum zoom level, zoom level variance, and scanning percentage. Two distinct search strategies were confirmed: scanning is characterized by panning at a constant zoom level, while drilling involves zooming in and out at various locations. Statistical analysis was applied to examine the associations of different visual interpretive strategies with pathologist characteristics, diagnostic accuracy, and efficiency. We found that females scanned more than males, and age was positively correlated with scanning percentage, while the facility size was negatively correlated. Throughout 60 cases, the scanning percentage and total interpretation time per slide decreased, and these two variables were positively correlated. The scanning percentage was not predictive of diagnostic accuracy. Increasing average zoom level, maximum zoom level, and zoom variance were correlated with over-interpretation.

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Acknowledgements

Research reported in this publication was supported by the National Cancer Institute awards R01 CA172343, R01 CA140560, and KO5 CA104699. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Cancer Institute or the National Institutes of Health. We thank Ventana Medical Systems, Inc. (Tucson, AZ, USA), a member of the Roche Group, for the use of iScan Coreo Au™ whole slide imaging system, and HD View SL for the source code used to build our digital viewer. For a full description of HD View SL, please see http://hdviewsl.codeplex.com/.

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Authors and Affiliations

  1. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Ezgi Mercan & Linda G. Shapiro

  2. Department of Psychology, Tufts University, Medford, MA, USA

    Tad T. Brunyé

  3. Department of Pathology and UVM Cancer Center, University of Vermont, Burlington, VT, USA

    Donald L. Weaver

  4. Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

    Joann G. Elmore

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  1. Ezgi Mercan
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  2. Linda G. Shapiro
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  3. Tad T. Brunyé
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  4. Donald L. Weaver
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  5. Joann G. Elmore
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Correspondence to Ezgi Mercan.

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Mercan, E., Shapiro, L.G., Brunyé, T.T. et al. Characterizing Diagnostic Search Patterns in Digital Breast Pathology: Scanners and Drillers. J Digit Imaging 31, 32–41 (2018). https://doi.org/10.1007/s10278-017-9990-5

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  • DOI: https://doi.org/10.1007/s10278-017-9990-5

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