Classification of breast abnormalities under different detection cueing environments

Bin Zheng; Richard G. Swensson; Amy H. Klym; Lara A. Hardesty; Ratan Shah; Luisa Wallace; Christiane M. Hakim; David Gur

doi:10.1117/12.533805

4 May 2004 Classification of breast abnormalities under different detection cueing environments

Bin Zheng, Richard G. Swensson, Amy H. Klym, Lara A. Hardesty, Ratan Shah, Luisa Wallace, Christiane M. Hakim, David Gur

Author Affiliations +

Proceedings Volume 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment; (2004) https://doi.org/10.1117/12.533805
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Eight radiologists interpreted 110 subtle cases in an observer performance study. The database includes 51 verified masses and 44 microcalcification clusters. Of these, 35 masses and 29 clusters were associated with malignancy. Two computer-aided detection (CAD) cueing conditions involving the same case-based sensitivity of 73% and two false-positive rates (0.8 or 2 per image) were applied. In each condition, radiologists interpreted 110 cases twice. In one reading mode radiologists first interpreted images without viewing CAD cues and then they could revise their initial interpretation after reviewing CAD cues. In another reading mode, radiologists viewed cues as soon as the images were displayed. Abnormalities were first detected by the radiologists and then classified as benign or malignant. The results demonstrated that these two cueing modes had little impact on radiologists performance after they have already made initial interpretation. However, displaying a large number of false-positive cues simultaneously with the images significantly reduced radiologists' performance in the classification of masses (p < 0.05). As false-positive cueing rate decreased the negative effect on classification performance decreased as well. Hence, inappropriate use of or reliance on CAD results with high false-positive rate could interfere with radiologists' attention to the classification task.

Citation Download Citation

Bin Zheng, Richard G. Swensson, Amy H. Klym, Lara A. Hardesty, Ratan Shah, Luisa Wallace, Christiane M. Hakim, and David Gur "Classification of breast abnormalities under different detection cueing environments", Proc. SPIE 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, (4 May 2004); https://doi.org/10.1117/12.533805

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