Association of Recall Rates with Sensitivity and Positive Predictive Values of Screening Mammography
- 1 September 2001
- journal article
- Published by American Roentgen Ray Society in American Journal of Roentgenology
- Vol. 177 (3), 543-549
- https://doi.org/10.2214/ajr.177.3.1770543
Abstract
OBJECTIVE. The performance of screening mammography is measured mainly by its sensitivity, positive predictive value, and cancer detection rate. Recall rates are also suggested as a surrogate measure. The main objective of this study was to measure the effect on sensitivity and positive predictive value as recall rates increase in the community practice of mammography. MATERIALS AND METHODS. Mammography and pathology data are linked in the Carolina Mammography Registry, a population-based registry of screening mammography. Our mammography database is created from prospectively collected data from mammography facilities; the data include information on the woman and the imaging studies. Our pathology database is created from prospectively collected breast pathology data received from pathology sites and the Central Cancer Registry. Women in the registry who were 40 years old and older and who underwent screening mammography between January 1994 and June 1998 were included. “Recall rate” was defined as the percentage of screening studies for which further workup was recommended by the radiologist. RESULTS. The study included 215,665 screening mammograms. The mean age of the women was 56 years. The recall rates of the average practice ranged from 1.9% to 13.4%. Sensitivity rose from a mean of 65% in the lowest recall rates to 80.2% at the highest level of recall rates. The positive predictive value of screening decreased from 7.2% in the lowest level of recall to 3.3% in the highest. As recall rates increased, sensitivity increased very little beyond a recall rate of 4.8%, and positive predictive value began decreasing significantly at a recall rate of 5.9%. CONCLUSION. Practices with recall rates between 4.9% and 5.5% achieve the best trade-off of sensitivity and positive predictive value.Keywords
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