Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments 1

Abstract

Rationale and objectives The authors evaluated the impact of different computer-aided detection (CAD) cueing conditions on radiologists’ performance levels in detecting and classifying masses depicted on mammograms. Materials and methods In an observer performance study, eight radiologists interpreted 110 subtle cases six times under different display conditions to detect depicted masses and classify them as benign or malignant. Forty-five cases depicted biopsy-proven masses and 65 were negative. One mass-based cueing sensitivity of 80% and two false-positive cueing rates of 1.2 and 0.5 per image were used in this study. In one mode, radiologists first interpreted images without CAD results, followed by the display of cues and reinterpretation. In another mode, radiologists viewed CAD cues as images were presented and then interpreted images. Free-response receiver operating characteristic method was used to analyze and compare detection performance. The receiver operating characteristic method was used to evaluate classification performance. Results At these performance levels, providing cues after initial interpretation had little effect on the overall performance in detecting masses. However, in the mode with the highest false-positive cueing rate, viewing CAD cues immediately upon display of images significantly reduced average performance for both detection and classification tasks ( P < .05). Viewing CAD cues during the initial display consistently resulted in fewer abnormalities being identified in noncued regions. Conclusion CAD systems with low sensitivity (≤80% on mass-based detection) and high false-positive rate (≥0.5 per image) in a dataset with subtle abnormalities had little effect on radiologists’ performance in the detection and classification of mammographic masses.