24 March 2016 Can model observers be developed to reproduce radiologists' diagnostic performances? Our study says not so fast!
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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.
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Juhun Lee, Robert M. Nishikawa, Ingrid Reiser, John M. Boone, "Can model observers be developed to reproduce radiologists' diagnostic performances? Our study says not so fast!", Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978707 (24 March 2016); doi: 10.1117/12.2216253; https://doi.org/10.1117/12.2216253
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