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Rationale and objectivesAs breast magnetic resonance imaging (MRI) use grows, benchmark performance parameters are needed for auditing and quality assurance purposes. We describe the variation in breast MRI abnormal interpretation rates (AIRs) by clinical indication among a large sample of US community practices.Materials and methodsWe analyzed data from 41 facilities across five Breast Cancer Surveillance Consortium imaging registries. Each registry obtained institutional review board approval for this Health Insurance Portability and Accountability Act compliant analysis. We included 11,654 breast MRI examinations conducted in 2005-2010 among women aged 18-79 years. We categorized clinical indications as 1) screening, 2) extent of disease, 3) diagnostic (eg, breast symptoms), and 4) other (eg, short-interval follow-up). We characterized assessments as positive (ie, Breast Imaging Reporting and Data System [BI-RADS] 0, 4, and 5) or negative (ie, BI-RADS 1, 2, and 6) and provide results with BI-RADS 3 categorized as positive and negative. We tested for differences in AIRs across clinical indications both unadjusted and adjusted for patient characteristics and registry and assessed for changes in AIRs by year within each clinical indication.ResultsWhen categorizing BI-RADS 3 as positive, AIRs were 21.0% (95% confidence interval [CI], 19.8-22.3) for screening, 31.7% (95% CI, 29.6-33.8) for extent of disease, 29.7% (95% CI, 28.3-31.1) for diagnostic, and 27.4% (95% CI, 25.0-29.8) for other indications (P < .0001). When categorizing BI-RADS 3 as negative, AIRs were 10.5% (95% CI, 9.5-11.4) for screening, 21.8% (95% CI, 19.9-23.6) for extent of disease, 17.7% (95% CI, 16.5-18.8) for diagnostic, and 13.3% (95% CI, 11.6-15.2) for other indications (P < .0001). The significant differences in AIRs by indication persisted even after adjusting for patient characteristics and registry (P < .0001). In addition, for most indications, there were no significant changes in AIRs over time.ConclusionsBreast MRI AIRs differ significantly by clinical indication. Practices should stratify breast MRI examinations by indication for quality assurance and auditing purposes.

Rationale and Objectives: MRI has shown promise in assessing breast cancer patients undergoing neoadjuvant chemotherapy. Computer-aided detection (CAD) for MRI can automatically display tumor enhancement parameters. This study was performed to determine the utility of CAD applied to breast MRI in this patient population. Materials and Methods: Fifteen patients with 16 newly diagnosed locally advanced breast cancers were evaluated with MRI before and after neoadjuvant chemotherapy. CAD assessments, including presence or absence of significant enhancement, enhancement profiles, and maximum sizes, were recorded. Pre-chemotherapy and post-chemotherapy enhancement profiles were compared. Sizes were compared to those measured by the radiologist and at final pathology. Results: Prior to chemotherapy, all tumors demonstrated CAD-assessed significant enhancement. Following chemotherapy, 7/16 tumors showed no residual significant enhancement, but all had residual disease at pathology. In those patients with residual enhancement, comparison of the post-chemotherapy to pre-chemotherapy CAD enhancement profiles showed a significant decrease in percentage of washout enhancement (P = 0.0147) in patients with less than 5 mm of residual microscopic disease. Radiologist-measured tumor sizes demonstrated better correlation with sizes at pathology (r = 0.60) than did CAD-generated tumor sizes (r = 0.32). Conclusion: CAD may be helpful in assessing changes in MRI enhancement profiles of tumors following chemotherapy. However, CAD-assessed significant enhancement following chemotherapy can be falsely negative for residual malignancy, and CAD tumor sizes are less accurate than those measured by the radiologist in predicting size of residual malignancy. CAD may complement but should not replace the radiologist’s assessment of tumors in this patient population. [Copyright &y& Elsevier]