Literature DB >> 27061872

The relationship between cancer detection in mammography and image quality measurements.

Alistair Mackenzie1, Lucy M Warren2, Matthew G Wallis3, Rosalind M Given-Wilson4, Julie Cooke5, David R Dance6, Dev P Chakraborty7, Mark D Halling-Brown8, Padraig T Looney9, Kenneth C Young10.   

Abstract

PURPOSE: To investigate the relationship between image quality measurements and the clinical performance of digital mammographic systems.
METHODS: Mammograms containing subtle malignant non-calcification lesions and simulated malignant calcification clusters were adapted to appear as if acquired by four types of detector. Observers searched for suspicious lesions and gave these a malignancy score. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). Images of a CDMAM contrast-detail phantom were adapted to appear as if acquired using the same four detectors as the clinical images. The resultant threshold gold thicknesses were compared to the FoMs using a linear regression model and an F-test was used to find if the gradient of the relationship was significantly non-zero.
RESULTS: The detectors with the best image quality measurement also had the highest FoM values. The gradient of the inverse relationship between FoMs and threshold gold thickness for the 0.25mm diameter disk was significantly different from zero for calcification clusters (p=0.027), but not for non-calcification lesions (p=0.11). Systems performing just above the minimum image quality level set in the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis resulted in reduced cancer detection rates compared to systems performing at the achievable level.
CONCLUSIONS: The clinical effectiveness of mammography for the task of detecting calcification clusters was found to be linked to image quality assessment using the CDMAM phantom. The European Guidelines should be reviewed as the current minimum image quality standards may be too low.
Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cancer detection; Contrast detail; Digital detector; Mammography

Mesh:

Year:  2016        PMID: 27061872      PMCID: PMC4856544          DOI: 10.1016/j.ejmp.2016.03.004

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  16 in total

1.  The effect of background structure on the detection of low contrast objects in mammography.

Authors:  C J Kotre
Journal:  Br J Radiol       Date:  1998-11       Impact factor: 3.039

2.  Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol.

Authors:  D R Dance; C L Skinner; K C Young; J R Beckett; C J Kotre
Journal:  Phys Med Biol       Date:  2000-11       Impact factor: 3.609

3.  Observer studies involving detection and localization: modeling, analysis, and validation.

Authors:  Dev P Chakraborty; Kevin S Berbaum
Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

4.  How do lesion size and random noise affect detection performance in digital mammography?

Authors:  Walter Huda; Kent M Ogden; Ernest M Scalzetti; David R Dance; Elizabeth A Bertrand
Journal:  Acad Radiol       Date:  2006-11       Impact factor: 3.173

5.  Digital mammography: effects of reduced radiation dose on diagnostic performance.

Authors:  Ehsan Samei; Robert S Saunders; Jay A Baker; David M Delong
Journal:  Radiology       Date:  2007-03-13       Impact factor: 11.105

6.  Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system.

Authors:  Alistair Mackenzie; David R Dance; Adam Workman; Mary Yip; Kevin Wells; Kenneth C Young
Journal:  Med Phys       Date:  2012-05       Impact factor: 4.071

7.  Pathological and mammographic prognostic factors for screen detected cancers in a multi-centre randomised, controlled trial of mammographic screening in women from age 40 to 48 years.

Authors:  R L Bennett; A J Evans; E Kutt; C Record; L G Bobrow; I O Ellis; A Hanby; S M Moss
Journal:  Breast       Date:  2011-06-21       Impact factor: 4.380

8.  Effect of image quality on calcification detection in digital mammography.

Authors:  Lucy M Warren; Alistair Mackenzie; Julie Cooke; Rosalind M Given-Wilson; Matthew G Wallis; Dev P Chakraborty; David R Dance; Hilde Bosmans; Kenneth C Young
Journal:  Med Phys       Date:  2012-06       Impact factor: 4.071

9.  Does image quality matter? Impact of resolution and noise on mammographic task performance.

Authors:  Robert S Saunders; Jay A Baker; David M Delong; Jeff P Johnson; Ehsan Samei
Journal:  Med Phys       Date:  2007-10       Impact factor: 4.071

10.  Evaluation of clinical image processing algorithms used in digital mammography.

Authors:  Federica Zanca; Jurgen Jacobs; Chantal Van Ongeval; Filip Claus; Valerie Celis; Catherine Geniets; Veerle Provost; Herman Pauwels; Guy Marchal; Hilde Bosmans
Journal:  Med Phys       Date:  2009-03       Impact factor: 4.071

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  7 in total

1.  An investigation into the validity of utilising the CDRAD 2.0 phantom for optimisation studies in digital radiography.

Authors:  Sadeq Al-Murshedi; Peter Hogg; Andrew England
Journal:  Br J Radiol       Date:  2018-07-05       Impact factor: 3.039

Review 2.  A review of mammographic positioning image quality criteria for the craniocaudal projection.

Authors:  Rhonda-Joy I Sweeney; Sarah J Lewis; Peter Hogg; Mark F McEntee
Journal:  Br J Radiol       Date:  2017-12-05       Impact factor: 3.039

3.  On the relevance of modulation transfer function measurements in digital mammography quality control.

Authors:  Kristina T Wigati; Nicholas W Marshall; Kim Lemmens; Joke Binst; Annelies Jacobs; Lesley Cockmartin; Guozhi Zhang; Liesbeth Vancoillie; Dimitar Petrov; Dirk A N Vandenbroucke; Djarwani S Soejoko; Hilde Bosmans
Journal:  J Med Imaging (Bellingham)       Date:  2021-04-27

4.  Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms.

Authors:  Joana Boita; Alistair Mackenzie; Ruben E van Engen; Mireille Broeders; Ioannis Sechopoulos
Journal:  J Med Imaging (Bellingham)       Date:  2021-05-20

5.  Quality assurance target for community-based breast cancer screening in China: a model simulation.

Authors:  Lan Yang; Jing Wang; Juan Cheng; Yuan Wang; Wenli Lu
Journal:  BMC Cancer       Date:  2018-03-07       Impact factor: 4.430

6.  Image Quality Comparison between Digital Breast Tomosynthesis Images and 2D Mammographic Images Using the CDMAM Test Object.

Authors:  Ioannis A Tsalafoutas; Angeliki C Epistatou; Konstantinos K Delibasis
Journal:  J Imaging       Date:  2022-08-21

7.  Automated Assessment of Breast Positioning Quality in Screening Mammography.

Authors:  Mouna Brahim; Kai Westerkamp; Louisa Hempel; Reiner Lehmann; Dirk Hempel; Patrick Philipp
Journal:  Cancers (Basel)       Date:  2022-09-27       Impact factor: 6.575

  7 in total

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