Literature DB >> 26259522

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

Yanpeng Li1, Patrick C Brennan2, Warwick Lee3, Carolyn Nickson4, Mariusz W Pietrzyk5, Elaine A Ryan2.   

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.

Entities:  

Keywords:  Density segmentation; Mammographic density (MD); Mammography; Observer variation

Mesh:

Year:  2015        PMID: 26259522      PMCID: PMC4570902          DOI: 10.1007/s10278-015-9814-4

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  22 in total

1.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment.

Authors:  W A Berg; C Campassi; P Langenberg; M J Sexton
Journal:  AJR Am J Roentgenol       Date:  2000-06       Impact factor: 3.959

2.  A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification.

Authors:  Stefano Ciatto; Daniela Bernardi; Massimo Calabrese; Manuela Durando; Maria Adalgisa Gentilini; Giovanna Mariscotti; Francesco Monetti; Enrica Moriconi; Barbara Pesce; Antonella Roselli; Carmen Stevanin; Margherita Tapparelli; Nehmat Houssami
Journal:  Breast       Date:  2012-01-27       Impact factor: 4.380

3.  A novel automated mammographic density measure and breast cancer risk.

Authors:  John J Heine; Christopher G Scott; Thomas A Sellers; Kathleen R Brandt; Daniel J Serie; Fang-Fang Wu; Marilyn J Morton; Beth A Schueler; Fergus J Couch; Janet E Olson; V Shane Pankratz; Celine M Vachon
Journal:  J Natl Cancer Inst       Date:  2012-07-03       Impact factor: 13.506

4.  Malignancy detection in digital mammograms: important reader characteristics and required case numbers.

Authors:  Warren M Reed; Warwick B Lee; Jennifer N Cawson; Patrick C Brennan
Journal:  Acad Radiol       Date:  2010-08-17       Impact factor: 3.173

5.  Mammography: interobserver variability in breast density assessment.

Authors:  E A Ooms; H M Zonderland; M J C Eijkemans; M Kriege; B Mahdavian Delavary; C W Burger; A C Ansink
Journal:  Breast       Date:  2007-12       Impact factor: 4.380

6.  Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one.

Authors:  Alberto Tagliafico; Giulio Tagliafico; Simona Tosto; Fabio Chiesa; Carlo Martinoli; Lorenzo E Derchi; Massimo Calabrese
Journal:  Breast       Date:  2008-11-17       Impact factor: 4.380

7.  Breast density: comparison of chest CT with mammography.

Authors:  Mary Salvatore; Laurie Margolies; Minal Kale; Juan Wisnivesky; Sean Kotkin; Claudia I Henschke; David F Yankelevitz
Journal:  Radiology       Date:  2013-10-28       Impact factor: 11.105

8.  Comparison of mammographic density estimation by Volpara software with radiologists' visual assessment: analysis of clinical-radiologic factors affecting discrepancy between them.

Authors:  Han Na Lee; Yu-Mee Sohn; Kyung Hwa Han
Journal:  Acta Radiol       Date:  2014-10-22       Impact factor: 1.990

Review 9.  Mammographic density. Measurement of mammographic density.

Authors:  Martin J Yaffe
Journal:  Breast Cancer Res       Date:  2008-06-19       Impact factor: 6.466

10.  Breast cancer risk factors and a novel measure of volumetric breast density: cross-sectional study.

Authors:  M Jeffreys; R Warren; R Highnam; G Davey Smith
Journal:  Br J Cancer       Date:  2007-12-18       Impact factor: 7.640

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