Strategies for digital mammography interpretation in a clinical patient population

Mammography is the basic imaging modality for early detection of breast cancer. The aim of this prospective study was to evaluate the impact of different mammogram reading strategies on the diagnostic yield in a consecutive patient population referred for digital mammography to a hospital. First, the effect of using computer-aided detection (CAD) software on the performance of mammogram readers was studied. Furthermore, the impact of employing technologists as either prereaders or double readers was assessed, as compared to the conventional strategy of single reading by a radiologist. Digital mammograms of 1,048 consecutive patients were evaluated by a radiologist and 3 technologists with and without the use of CAD software. ROC analysis was used to study the effects of the different strategies. In the conventional strategy, an overall area under the curve (AUC) of 0.92 was found, corresponding to a sensitivity of 84% and specificity of 94%. When applying CAD software, the AUCs were similar before and after CAD for all readers (mean of 0.95). Employing technologists in prereading and double reading of mammograms resulted in a mean AUC of 0.91 and 0.96, respectively. In the prereading strategy, the corresponding sensitivity and specificity were 81 and 96%; in the double reading strategy they were 96 and 79%, respectively. Concluding, in this clinical population, systematic application of CAD software by either radiologist or technologists failed to improve the diagnostic yield. Furthermore, employing technologists as double readers of mammograms was the most effective strategy in improving breast cancer detection in daily clinical practice.