Recall and detection rates in screening mammography

Cancer. 2004 Apr 15;100(8):1590-4. doi: 10.1002/cncr.20053.

Abstract

Background: The authors investigated the correlation between recall and detection rates in a group of 10 radiologists who had read a high volume of screening mammograms in an academic institution.

Methods: Practice-related and outcome-related databases of verified cases were used to compute recall rates and tumor detection rates for a group of 10 Mammography Quality Standard Act (MQSA)-certified radiologists who interpreted a total of 98,668 screening mammograms during the years 2000, 2001, and 2002. The relation between recall and detection rates for these individuals was investigated using parametric Pearson (r) and nonparametric Spearman (rho) correlation coefficients. The effect of the volume of mammograms interpreted by individual radiologists was assessed using partial correlations controlling for total reading volumes.

Results: A wide variability of recall rates (range, 7.7-17.2%) and detection rates (range, 2.6-5.4 per 1000 mammograms) was observed in the current study. A statistically significant correlation (P < 0.05) between recall and detection rates was observed in this group of 10 experienced radiologists. The results remained significant (P < 0.05) after accounting for the volume of mammograms interpreted by each radiologist.

Conclusions: Optimal performance in screening mammography should be evaluated quantitatively. The general pressure to reduce recall rates through "practice guidelines" to below a fixed level for all radiologists should be assessed carefully.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Breast Neoplasms / diagnostic imaging*
  • Databases, Factual
  • Female
  • Humans
  • Mammography*
  • Mass Screening*
  • Observer Variation
  • Practice Guidelines as Topic*
  • Practice Patterns, Physicians' / statistics & numerical data
  • Quality Assurance, Health Care
  • Radiology / statistics & numerical data
  • Sensitivity and Specificity