Paper Info
Title
Can model observers be developed to reproduce radiologists' diagnostic performances? Our study says not so fast!
Abstract
The purpose of this study was to determine radiologists' diagnostic performances on different image reconstruction algorithms that could be used to optimize image-based model observers. We included a total of 102 pathology proven breast computed tomography (CT) cases (62 malignant). An iterative image reconstruction (IIR) algorithm was used to obtain 24 reconstructions with different image appearance for each image. Using quantitative image feature analysis, three IIRs and one clinical reconstruction of 50 lesions (25 malignant) were selected for a reader study. The reconstructions spanned a range of smooth-low noise to sharp-high noise image appearance. The trained classifiers' AUCs on the above reconstructions ranged from 0.61 (for smooth reconstruction) to 0.95 (for sharp reconstruction). Six experienced MQSA radiologists read 200 cases (50 lesions times 4 reconstructions) and provided the likelihood of malignancy of each lesion. Radiologists' diagnostic performances (AUC) ranged from 0.7 to 0.89. However, there was no agreement among the six radiologists on which image appearance was the best, in terms of radiologists' having the highest diagnostic performances. Specifically, two radiologists indicated sharper image appearance was diagnostically superior, another two radiologists indicated smoother image appearance was diagnostically superior, and another two radiologists indicated all image appearances were diagnostically similar to each other. Due to the poor agreement among radiologists on the diagnostic ranking of images, it may not be possible to develop a model observer for this particular imaging task.
Year
DOI
Venue
2016
10.1117/12.2216253
Proceedings of SPIE
Keywords
Field
DocType
Breast cancer,model observers,breast computed tomography,diagnostic performance,reader study
Iterative reconstruction,Computer vision,Image reconstruction algorithm,Computed tomography,Artificial intelligence,Image restoration,Pattern recognition (psychology),Physics
Conference
Volume
ISSN
Citations 
9787
0277-786X
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Juhun Lee122.45
Robert M Nishikawa259958.25
Ingrid Reiser3175.33
John M. Boone44310.07