Publication year
2012Source
European Radiology, 22, (2012), pp. 1659-1664ISSN
Publication type
Article / Letter to editor

Display more detailsDisplay less details
Organization
Radiology
Journal title
European Radiology
Volume
vol. 22
Page start
p. 1659
Page end
p. 1664
Subject
ONCOL 5: Aetiology, screening and detectionAbstract
To assess whether short-term feedback helps readers to increase their performance using computer-aided detection (CAD) for nodule detection in chest radiography.The 140 CXRs (56 with a solitary CT-proven nodules and 84 negative controls) were divided into four subsets of 35; each were read in a different order by six readers. Lesion presence, location and diagnostic confidence were scored without and with CAD (IQQA-Chest, EDDA Technology) as second reader. Readers received individual feedback after each subset. Sensitivity, specificity and area under the receiver-operating characteristics curve (AUC) were calculated for readings with and without CAD with respect to change over time and impact of CAD.CAD stand-alone sensitivity was 59 \% with 1.9 false-positives per image. Mean AUC slightly increased over time with and without CAD (0.78 vs. 0.84 with and 0.76 vs. 0.82 without CAD) but differences did not reach significance. The sensitivity increased (65 \% vs. 70 \% and 66 \% vs. 70 \%) and specificity decreased over time (79 \% vs. 74 \% and 80 \% vs. 77 \%) but no significant impact of CAD was found.Short-term feedback does not increase the ability of readers to differentiate true- from false-positive candidate lesions and to use CAD more effectively.• Computer-aided detection (CAD) is increasingly used as an adjunct for many radiological techniques. • Short-term feedback does not improve reader performance with CAD in chest radiography. • Differentiation between true- and false-positive CAD for low conspicious possible lesions proves difficult. • CAD can potentially increase reader performance for nodule detection in chest radiography.
This item appears in the following Collection(s)
- Academic publications [229037]
- Faculty of Medical Sciences [87745]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.