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Classification of normal screening mammograms is strongly influenced by perceived mammographic breast density

Authors

  • Zoey ZY Ang,

    Corresponding author
    1. Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia
    2. National Healthcare Group Diagnostics (NHGD), Singapore City, Singapore
    • Correspondence

      Miss Zoey ZY Ang, Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, 75 East Street, Lidcombe, NSW 2141, Australia.

      Email: zang6391@uni.sydney.edu.au

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  • Mohammad A Rawashdeh,

    1. Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia
    2. Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
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  • Rob Heard,

    1. Health Systems and Global Populations Research Group, Faculty of Health Sciences, Discipline of Behavioural and Social Sciences in Health, The University of Sydney, Lidcombe, New South Wales, Australia
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  • Patrick C Brennan,

    1. Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia
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  • Warwick Lee,

    1. Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia
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  • Sarah J Lewis

    1. Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia
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  • ZZY Ang BAppSci (Hons); MA Rawashdeh PhD; R Heard PhD, BA (Hons); PC Brennan PhD; W Lee MBBS (Hons), BSc (Med), FRANZCR, DDU; SJ Lewis PhD, MEd, BAppSci (Hons).
  • Conflict of interest: None.

Abstract

Introduction

To investigate how breast screen readers classify normal screening cases using descriptors of normal mammographic features and to assess test cases for suitability for a single reading strategy.

Methods

Fifteen breast screen readers interpreted a test set of 29 normal screening cases and classified them by firstly rating their perceived difficulty to reach a ‘normal’ decision, secondly identifying the cases' salient normal mammographic features and thirdly assessing the cases' suitability for a single reading strategy.

Results

The relationship between the perceived difficulty in making ‘normal’ decisions and the normal mammographic features was investigated. Regular ductal pattern (Tb = −0.439, P = 0.001), uniform density (Tb = −0.527, P < 0.001), non-dense breasts (Tb = −0.736, P < 0.001), symmetrical mammographic features (Tb = −0.474, P = 0.001) and overlapped density (Tb = 0.630, P < 0.001) had a moderate to strong correlation with the difficulty to make ‘normal’ decisions. Cases with regular ductal pattern (Tb = 0.447, P = 0.002), uniform density (Tb = 0.550, P < 0.001), non-dense breasts (Tb = 0.748, P < 0.001) and symmetrical mammographic features (Tb = 0.460, P = 0.001) were considered to be more suitable for single reading, whereas cases with overlapped density were not (Tb = −0.679, P < 0.001).

Conclusion

The findings suggest that perceived mammographic breast density has a major influence on the difficulty for readers to classify cases as normal and hence their suitability for single reading.

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