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An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance

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Abstract

The aim of this work is to investigate how radiologist expertise and image appearance may have an impact on inter-reader variability of mammographic density (MD) identification. Seventeen radiologists, divided into three expertise groups, were asked to manually segment the areas they consider to be MD in 40 clinical images. The variation in identification of MD for each image was quantified by finding the range of segmentation areas. The impact of radiologist expertise and image appearance on this variation was explored. The range of areas chosen by participating radiologists varied from 7 to 73 % across the 40 images, with a mean range of 35 ± 13 %. Participants with high expertise were more likely to choose similar areas to one another, compared to participants with medium and low expertise levels (mean range were 19 ± 10 %, 29 ± 13 % and 25 ± 14 %, respectively, p < 0.0001). There was a significantly higher average grey level for the area segmented by all radiologists as MD compared to the area of variation, with mean grey level value for 8-bit images being 146 ± 19 vs. 99 ± 14, respectively. MD segmentation borders were consistent in areas where there was a sharp intensity change within a short distance. In conclusion, radiologists with high expertise tend to have a higher agreement when identifying MD. Tissues which have a lower contrast and a less visually sharp gradient change at the interface between high density tissue and adipose background lead to inter-reader variation in choosing mammographic density.

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Abbreviations

MD:

Mammographic density

ROI:

Regions of interest

A%:

Percentage area

R :

Range of segmentation areas

A M :

Median segmentation area

A Comm :

Common segmentation area

A Comb :

Combined segmentation area

A Var :

Variation area

AGL:

Average grey level

D x :

Distance of perpendicular lines

D L :

The longest distance of perpendicular line

D S :

The shortest distance of perpendicular line

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Authors and Affiliations

  1. Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia

    Yanpeng Li, Patrick C. Brennan & Elaine A. Ryan

  2. Cancer Institute NSW, 8 Central Avenue, Eveleigh, NSW, 2015, Australia

    Warwick Lee

  3. Melbourne School of Population and Global Health, The University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia

    Carolyn Nickson

  4. Institute of Physics, 76 Portland Place, London, W1B 1NT, UK

    Mariusz W. Pietrzyk

Authors
  1. Yanpeng Li
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  2. Patrick C. Brennan
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  3. Warwick Lee
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  4. Carolyn Nickson
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  5. Mariusz W. Pietrzyk
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  6. Elaine A. Ryan
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Correspondence to Yanpeng Li.

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Li, Y., Brennan, P.C., Lee, W. et al. An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance. J Digit Imaging 28, 626–632 (2015). https://doi.org/10.1007/s10278-015-9814-4

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  • DOI: https://doi.org/10.1007/s10278-015-9814-4

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