BACKGROUND: It has been proposed that magnetic resonance imaging (MRI) be used to guide breast cancer surgery by differentiating residual tumor from pathologic complete response (pCR) after neoadjuvant chemotherapy. This meta-analysis examines MRI accuracy in detecting residual tumor, investigates variables potentially affecting MRI performance, and compares MRI with other tests. METHODS: A systematic literature search was undertaken. Hierarchical summary receiver operating characteristic (HSROC) models were used to estimate (relative) diagnostic odds ratios ([R]DORs). Summary sensitivity (correct identification of residual tumor), specificity (correct identification of pCR), and areas under the SROC curves (AUCs) were derived. All statistical tests were two-sided. RESULTS: Forty-four studies (2050 patients) were included. The overall AUC of MRI was 0.88. Accuracy was lower for "standard" pCR definitions (referent category) than "less clearly described" (RDOR = 2.41, 95% confidence interval [CI] = 1.11 to 5.23) or "near-pCR" definitions (RDOR = 2.60, 95% CI = 0.73 to 9.24; P = .03.) Corresponding AUCs were 0.83, 0.90, and 0.91. Specificity was higher when negative MRI was defined as contrast enhancement less than or equal to normal tissue (0.83, 95% CI = 0.64 to 0.93) vs no enhancement (0.54, 95% CI = 0.39 to 0.69; P = .02), with comparable sensitivity (0.83, 95% CI = 0.69 to 0.91; vs 0.87, 95% CI = 0.80 to 0.92; P = .45). MRI had higher accuracy than mammography (P = .02); there was only weak evidence that MRI had higher accuracy than clinical examination (P = .10). No difference in MRI and ultrasound accuracy was found (P = .15). CONCLUSIONS: MRI accurately detects residual tumor after neoadjuvant chemotherapy. Accuracy was lower when pCR was more rigorously defined, and specificity was lower when test negativity thresholds were more stringent; these definitions require standardization. MRI is more accurate than mammography; however, studies comparing MRI and ultrasound are required.
BACKGROUND: It has been proposed that magnetic resonance imaging (MRI) be used to guide breast cancer surgery by differentiating residual tumor from pathologic complete response (pCR) after neoadjuvant chemotherapy. This meta-analysis examines MRI accuracy in detecting residual tumor, investigates variables potentially affecting MRI performance, and compares MRI with other tests. METHODS: A systematic literature search was undertaken. Hierarchical summary receiver operating characteristic (HSROC) models were used to estimate (relative) diagnostic odds ratios ([R]DORs). Summary sensitivity (correct identification of residual tumor), specificity (correct identification of pCR), and areas under the SROC curves (AUCs) were derived. All statistical tests were two-sided. RESULTS: Forty-four studies (2050 patients) were included. The overall AUC of MRI was 0.88. Accuracy was lower for "standard" pCR definitions (referent category) than "less clearly described" (RDOR = 2.41, 95% confidence interval [CI] = 1.11 to 5.23) or "near-pCR" definitions (RDOR = 2.60, 95% CI = 0.73 to 9.24; P = .03.) Corresponding AUCs were 0.83, 0.90, and 0.91. Specificity was higher when negative MRI was defined as contrast enhancement less than or equal to normal tissue (0.83, 95% CI = 0.64 to 0.93) vs no enhancement (0.54, 95% CI = 0.39 to 0.69; P = .02), with comparable sensitivity (0.83, 95% CI = 0.69 to 0.91; vs 0.87, 95% CI = 0.80 to 0.92; P = .45). MRI had higher accuracy than mammography (P = .02); there was only weak evidence that MRI had higher accuracy than clinical examination (P = .10). No difference in MRI and ultrasound accuracy was found (P = .15). CONCLUSIONS: MRI accurately detects residual tumor after neoadjuvant chemotherapy. Accuracy was lower when pCR was more rigorously defined, and specificity was lower when test negativity thresholds were more stringent; these definitions require standardization. MRI is more accurate than mammography; however, studies comparing MRI and ultrasound are required.
Authors: Daniel I Golden; Jafi A Lipson; Melinda L Telli; James M Ford; Daniel L Rubin Journal: J Am Med Inform Assoc Date: 2013-06-19 Impact factor: 4.497
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