Observer training for computer-aided detection of pulmonary nodules in chest radiography
- PMID: 22447377
- PMCID: PMC3387360
- DOI: 10.1007/s00330-012-2412-7
Observer training for computer-aided detection of pulmonary nodules in chest radiography
Abstract
Objectives: To assess whether short-term feedback helps readers to increase their performance using computer-aided detection (CAD) for nodule detection in chest radiography.
Methods: 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.
Results: 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.
Conclusion: Short-term feedback does not increase the ability of readers to differentiate true- from false-positive candidate lesions and to use CAD more effectively.
Key points: • 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.
Figures
![Fig. 1](https://cdn.statically.io/img/www.ncbi.nlm.nih.gov/pmc/articles/instance/3387360/bin/330_2012_2412_Fig1_HTML.gif)
Similar articles
-
Is there an advantage to using computer aided detection for the early detection of pulmonary nodules within chest X-Ray imaging?Radiography (Lond). 2020 Aug;26(3):e170-e178. doi: 10.1016/j.radi.2020.01.002. Epub 2020 Jan 29. Radiography (Lond). 2020. PMID: 32052750 Review.
-
Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images.Radiology. 2014 Jul;272(1):252-61. doi: 10.1148/radiol.14131315. Epub 2014 Mar 12. Radiology. 2014. PMID: 24635675
-
New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.Br J Radiol. 2014 Apr;87(1036):20140015. doi: 10.1259/bjr.20140015. Epub 2014 Feb 17. Br J Radiol. 2014. PMID: 24625084 Free PMC article.
-
Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study.Acad Radiol. 2011 Dec;18(12):1507-14. doi: 10.1016/j.acra.2011.08.008. Epub 2011 Oct 2. Acad Radiol. 2011. PMID: 21963532
-
A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective.Korean J Radiol. 2011 Mar-Apr;12(2):145-55. doi: 10.3348/kjr.2011.12.2.145. Epub 2011 Mar 3. Korean J Radiol. 2011. PMID: 21430930 Free PMC article. Review.
Cited by
-
The diagnostic value of grey-scale inversion technique in chest radiography.Radiol Med. 2022 Mar;127(3):294-304. doi: 10.1007/s11547-022-01453-0. Epub 2022 Jan 18. Radiol Med. 2022. PMID: 35041136 Free PMC article.
-
Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph.J Clin Imaging Sci. 2017 Feb 20;7:8. doi: 10.4103/jcis.JCIS_75_16. eCollection 2017. J Clin Imaging Sci. 2017. PMID: 28299236 Free PMC article.
-
[Diagnosis and management of solitary pulmonary nodules].Zhongguo Fei Ai Za Zhi. 2013 Sep;16(9):499-508. doi: 10.3779/j.issn.1009-3419.2013.09.11. Zhongguo Fei Ai Za Zhi. 2013. PMID: 24034999 Free PMC article. Review. Chinese.
References
-
- Li F, Engelmann R, Metz CE, Doi K, MacMahon H. Lung cancers missed on chest radiographs: results obtained with a commercial computer-aided detection program. Radiology. 2008;246:273–280. - PubMed
MeSH terms
LinkOut - more resources
Full Text Sources
Miscellaneous