Literature DB >> 20719545

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

Warren M Reed1, Warwick B Lee, Jennifer N Cawson, Patrick C Brennan.   

Abstract

RATIONALE AND
OBJECTIVES: To determine the relationship between heightened levels of reader performance and reader practice in terms of number of cases read and previous experience.
MATERIALS AND METHODS: A test set of mammograms was developed comprising 50 cases. These cases consisted of 15 abnormals (biopsy proven) and 35 normals (confirmed at subsequent rescreen). Sixty-nine breast image readers reviewed these cases independently and their performance was measured by recording their individual receiver operating characteristic score (area under the curve), sensitivity, and specificity. These measures of performance were then compared to a range of factors relating to the reader such as years of certification and reporting, number of cases read per year, previous experiences, and satisfaction levels. Correlation analyses using Spearman methods were performed along with the Mann-Whitney test to detect differences in performance between specific reader groups.
RESULTS: Improved reader performance was found for years certified (P = .004), years of experience (P = .0001), and hours reading per week (P = .003) shown by positive statistical significant relationships with Az values (area under receiver operating characteristic curve). Statistical comparisons of Az values scored for individuals who read varying number of cases per year showed that those individuals whose annual mammographic case load was 5000 or more (P = .03) or between 2000 and 4999 (P = .05), had statistically significantly higher scores than those who read less than 1000 cases per year.
CONCLUSION: The results of this study have shown variations in reader performance relating to parameters of reader practice and experience. Levels of variance are shown and potential acceptance levels for diagnostic efficacy are proposed which may inform policy makers, judicial systems and public debate.
Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2010        PMID: 20719545     DOI: 10.1016/j.acra.2010.06.016

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  14 in total

1.  Experiences with a self-test for Dutch breast screening radiologists: lessons learnt.

Authors:  J M H Timmers; A L M Verbeek; R M Pijnappel; M J M Broeders; G J den Heeten
Journal:  Eur Radiol       Date:  2013-09-22       Impact factor: 5.315

2.  Number of mammography cases read per year is a strong predictor of sensitivity.

Authors:  Wasfi I Suleiman; Sarah J Lewis; Dianne Georgian-Smith; Michael G Evanoff; Mark F McEntee
Journal:  J Med Imaging (Bellingham)       Date:  2014-05-07

3.  Comparable prediction of breast cancer risk from a glimpse or a first impression of a mammogram.

Authors:  E M Raat; I Farr; J M Wolfe; K K Evans
Journal:  Cogn Res Princ Implic       Date:  2021-11-06

4.  Mammography self-evaluation online test for screening readers: an Italian Society of Medical Radiology (SIRM) initiative.

Authors:  Beniamino Brancato; Francesca Peruzzi; Calogero Saieva; Simone Schiaffino; Sandra Catarzi; Gabriella Gemma Risso; Andrea Cozzi; Serena Carriero; Massimo Calabrese; Stefania Montemezzi; Chiara Zuiani; Francesco Sardanelli
Journal:  Eur Radiol       Date:  2021-09-04       Impact factor: 5.315

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

Authors:  Yanpeng Li; Patrick C Brennan; Warwick Lee; Carolyn Nickson; Mariusz W Pietrzyk; Elaine A Ryan
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

6.  A machine learning model based on readers' characteristics to predict their performances in reading screening mammograms.

Authors:  Ziba Gandomkar; Sarah J Lewis; Tong Li; Ernest U Ekpo; Patrick C Brennan
Journal:  Breast Cancer       Date:  2022-02-05       Impact factor: 3.307

7.  Personal and Network Dynamics in Performance of Knowledge Workers: A Study of Australian Breast Radiologists.

Authors:  Seyedamir Tavakoli Taba; Liaquat Hossain; Robert Heard; Patrick Brennan; Warwick Lee; Sarah Lewis
Journal:  PLoS One       Date:  2016-02-26       Impact factor: 3.240

8.  Social networks and expertise development for Australian breast radiologists.

Authors:  Seyedamir Tavakoli Taba; Liaquat Hossain; Karen Willis; Sarah Lewis
Journal:  BMC Health Serv Res       Date:  2017-02-11       Impact factor: 2.655

9.  Reader characteristics and mammogram features associated with breast imaging reporting scores.

Authors:  Phuong Dung Yun Trieu; Sarah J Lewis; Tong Li; Karen Ho; Kriscia A Tapia; Patrick C Brennan
Journal:  Br J Radiol       Date:  2020-08-05       Impact factor: 3.039

10.  Evaluating radiographers' diagnostic accuracy in screen-reading mammograms: what constitutes a quality study?

Authors:  Josephine C Debono; Ann E Poulos
Journal:  J Med Radiat Sci       Date:  2014-08-14
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