Literature DB >> 26602930

In the digital era, architectural distortion remains a challenging radiological task.

W I Suleiman1, M F McEntee2, S J Lewis2, M A Rawashdeh3, D Georgian-Smith4, R Heard2, K Tapia2, P C Brennan2.   

Abstract

AIM: To compare readers' performance in detecting architectural distortion (AD) compared with other breast cancer types using digital mammography.
MATERIALS AND METHODS: Forty-one experienced breast screen readers (20 US and 21 Australian) were asked to read a single test set of 30 digitally acquired mammographic cases. Twenty cases had abnormal findings (10 with AD, 10 non-AD) and 10 cases were normal. Each reader was asked to locate and rate any abnormalities. Lesion and case-based performance was assessed. For each collection of readers (US; Australian; combined), jackknife free-response receiver operating characteristic (JAFROC), figure of merit (FOM), and inferred receiver operating characteristic (ROC), area under curve (Az) were calculated using JAFROC v.4.1 software. Readers' sensitivity, location sensitivity, JAFROC, FOM, ROC, Az scores were compared between cases groups using Wilcoxon's signed ranked test statistics.
RESULTS: For lesion-based analysis, significantly lower location sensitivity (p=0.001) was shown on AD cases compared with non-AD cases for all reader collections. The case-based analysis demonstrated significantly lower ROC Az values (p=0.02) for the first collection of readers, and lower sensitivity for the second collection of readers (p=0.04) and all-readers collection (p=0.008), for AD compared with non-AD cases.
CONCLUSIONS: The current work demonstrates that AD remains a challenging task for readers, even in the digital era.
Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26602930     DOI: 10.1016/j.crad.2015.10.009

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  7 in total

1.  Comparison of digital mammography and digital breast tomosynthesis in the detection of architectural distortion.

Authors:  Elizabeth H Dibble; Ana P Lourenco; Grayson L Baird; Robert C Ward; A Stanley Maynard; Martha B Mainiero
Journal:  Eur Radiol       Date:  2017-07-14       Impact factor: 5.315

2.  An investigation into the mammographic appearances of missed breast cancers when recall rates are reduced.

Authors:  Norhashimah Mohd Norsuddin; Claudia Mello-Thoms; Warren Reed; Mary Rickard; Sarah Lewis
Journal:  Br J Radiol       Date:  2017-06-16       Impact factor: 3.039

3.  Variations in breast cancer detection rates during mammogram-reading sessions: does experience have an impact?

Authors:  Abdulaziz S Alshabibi; Moayyad E Suleiman; Salman M Albeshan; Robert Heard; Patrick C Brennan
Journal:  Br J Radiol       Date:  2021-11-04       Impact factor: 3.039

4.  Positive Predictive Value of Tomosynthesis-guided Biopsies of Architectural Distortions Seen on Digital Breast Tomosynthesis and without an Ultrasound Correlate.

Authors:  Gopal R Vijayaraghavan; Adrienne Newburg; Srinivasan Vedantham
Journal:  J Clin Imaging Sci       Date:  2019-11-18

5.  A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography.

Authors:  Osmando Pereira Junior; Helder Cesar Rodrigues Oliveira; Carolina Toledo Ferraz; José Hiroki Saito; Marcelo Andrade da Costa Vieira; Adilson Gonzaga
Journal:  J Digit Imaging       Date:  2020-11-11       Impact factor: 4.056

Review 6.  Errors in Mammography Cannot be Solved Through Technology Alone

Authors:  Ernest Usang Ekpo; Maram Alakhras; Patrick Brennan
Journal:  Asian Pac J Cancer Prev       Date:  2018-02-26

7.  Reading High Breast Density Mammograms: Differences in Diagnostic Performance between Radiologists from Hong Kong SAR/Guangdong Province in China and Australia.

Authors:  Tong Li; Seyedamir Tavakoli Taba; Pek-Lan Khong; Tom X-L Tan; Phuong Dung Yun Trieu; Edward Chan; Moayyad E Suleiman; Ying Li; Patrick Brennan; Sarah Lewis
Journal:  Asian Pac J Cancer Prev       Date:  2020-09-01
  7 in total

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