22 May 2003 Effect of radiologists' variability on the performance of computer classification of malignant and benign clustered microcalcifications in mammograms
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Abstract
In developing a computer technique to classify clustered microcalcifications as malignant or benign, we previously indicated manually the location of all individual calcifications to the computer and found the computer to be more accurate than radiologists. In this study, we investigate whether radiologists can be asked to provide minimal input to the computer and obtain consistent computer classification results. Radiologists were instructed to draw a rectangle that enclosed all calcifications, and indicate the approximate number of the calcifications (either <6, 6-10, 10-30, or >30). The computer used these two pieces of information to detect the individual calcifications and, subsequently, to classify the calcifications as malignant or benign based on only those calcifications detected by the computer. We showed at the 2002 RSNA conference 18 cases together with standard and magnification view mammograms to 38 self-reported breast-imaging radiologists (12 of whom read all 18 cases). The standard deviation in the location of their rectangles (averaged over all cases) was approximately 3 mm, the standard deviation in the linear dimension of the rectangles was 6 mm, and the standard deviation in the computer-estimated likelihood of malignancy was 17%. These results indicate that radiologists are able to provide consistent input to the computer, which in turn produces reasonably consistent computer classification results.
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Yulei Jiang, Yulei Jiang, M. Fernanda Salfity, M. Fernanda Salfity, Vicky Chen, Vicky Chen, Robert M. Nishikawa, Robert M. Nishikawa, John Papaioannou, John Papaioannou, Alexandra V. Edwards, Alexandra V. Edwards, Sophie Paquerault, Sophie Paquerault, } "Effect of radiologists' variability on the performance of computer classification of malignant and benign clustered microcalcifications in mammograms", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.480360; https://doi.org/10.1117/12.480360
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