Effectiveness of computer-aided detection in community mammography practice

Abstract

Background: Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists.

Methods: We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million film-screen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998-2002 vs 2003-2006). All statistical tests were two-sided.

Results: Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size, or lymph node status of invasive breast cancer.

Conclusion: CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer.

Figure 1

Adjusted association between Computer-Aided Detection (CAD) and performance, biopsy recommendation, and breast cancer prognostic characteristics. Odds ratios (ORs) and 95% confidence intervals (CIs) of CAD use vs non-CAD use as estimated using random-effects logistic regression are presented. The 95% confidence intervals are represented by error bars. Odds ratios were adjusted for mammography registry, patient age (40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, and ≥75 years), breast density (almost entirely fat, scattered fibroglandular tissue, and heterogeneously and extremely dense), time since prior mammography (no prior mammogram, 9–15 months, 16–20 months, 21–27 months, and ≥28 months), current hormone replacement therapy, and year of examination (1998–2002 or 2003–2006). Analyses for each outcome include the following numbers of mammograms and women: specificity (n = 1 613 384 mammograms among 681 421 women without breast cancer); sensitivity (n = 7722 mammograms among 7722 women with breast cancer); positive predictive value (PPV₁) (n = 145 293 positive mammograms among 127 647 women); biopsy recommendations and breast cancer detection (n = 1 621 106 mammograms among 684 956 women); invasive cancer outcomes (n = 6177 mammograms among 6177 women with invasive cancers, excluding from 332 [5.4%] to 673 [10.9%] because of missing stage, size, or node involvement data). The model for advanced cancer includes both invasive cancers and ductal carcinomas in situ (DCIS). Advanced cancer (*) was defined as invasive breast cancers with either stage II, III, or IV; tumor size greater than 15 mm; or positive node involvement. Variance of facility-level random-effects was statistically significantly different from zero in all models (P < .01), except for models of stage I invasive breast cancer (P = .02) and negative node involvement (P = .99).