How experience and training influence mammography expertise.
RATIONALE AND OBJECTIVES: The authors evaluated the influence of perceptual and cognitive skills in mammography detection and interpretation by testing three groups representing different levels of mammography expertise in terms of experience, training, and talent with a mammography screening-diagnostic task. MATERIALS AND METHODS: One hundred fifty mammograms, composed of unilateral cranial-caudal and mediolateral oblique views, were displayed in pairs on a digital workstation to 19 radiology residents, three experienced mammographers, and nine mammography technologists. One-third of the mammograms showed malignant lesions; two-thirds were malignancy-free. Observers interacted with the display to indicate whether each image contained no malignant lesions or suspicious lesions indicating malignancy. Decision time was measured as the lesions were localized, classified, and rated for decision confidence. RESULTS: Compared with performance of experts, alternative free response operating characteristic performance for residents was significantly lower and equivalent to that of technologists. Analysis of overall performance showed that, as level of expertise decreased, false-positive results exerted a greater effect on overall decision accuracy over the time course of image perception. This defines the decision speed-accuracy relationship that characterizes mammography expertise. CONCLUSION: Differences in resident performance resulted primarily from lack of perceptual-learning experience during mammography training, which limited object recognition skills and made it difficult to determine differences between malignant lesions, benign lesions, and normal image perturbations. A proposed solution is systematic mentor-guided training that links image perception to feedback about the reasons underlying decision making.
Nodine, CF; Kundel, HL; Mello-Thoms, C; Weinstein, SP; Orel, SG; Sullivan, DC; Conant, EF
Volume / Issue
Start / End Page
International Standard Serial Number (ISSN)