PURPOSE: To develop an interpretation model based on architectural features of suspicious breast findings on magnetic resonance (MR) images. MATERIALS AND METHODS: One hundred ninety-two patients with mammographically visible or palpable findings underwent T1- and fat-saturated T2-weighted spin-echo and contrast agent-enhanced fat-saturated gradient-echo MR imaging. Patients underwent subsequent excisional biopsy for histopathologic confirmation. An interpretation model was constructed by using 98 cases and was tested prospectively and expanded by using 94 different cases. Sensitivity, specificity, predictive values, and receiver operating characteristic curves were computed for all models. RESULTS: Individual features with high predictive values were MR visibility, enhancement degree and pattern, focal mass border characteristics, and focal mass internal septations. Feature combinations with high negative predictive values for malignancy were absence of an MR-visible abnormality, focal masses with smooth borders, lobulated or irregular masses with nonenhancing internal septations, and focal masses with no (or minimal) enhancement. The validated- and revised-model performance characteristics were, respectively, as follows: sensitivity, 100% and 96%; specificity, 69% and 79%; positive predictive value, 75% and 76%; negative predictive value, 100% and 97%; and overall accuracy, 83% and 86%. CONCLUSION: An interpretation model that incorporates breast MR architectural features can achieve high sensitivity and improve specificity for diagnosing breast cancer.
PURPOSE: To develop an interpretation model based on architectural features of suspicious breast findings on magnetic resonance (MR) images. MATERIALS AND METHODS: One hundred ninety-two patients with mammographically visible or palpable findings underwent T1- and fat-saturated T2-weighted spin-echo and contrast agent-enhanced fat-saturated gradient-echo MR imaging. Patients underwent subsequent excisional biopsy for histopathologic confirmation. An interpretation model was constructed by using 98 cases and was tested prospectively and expanded by using 94 different cases. Sensitivity, specificity, predictive values, and receiver operating characteristic curves were computed for all models. RESULTS: Individual features with high predictive values were MR visibility, enhancement degree and pattern, focal mass border characteristics, and focal mass internal septations. Feature combinations with high negative predictive values for malignancy were absence of an MR-visible abnormality, focal masses with smooth borders, lobulated or irregular masses with nonenhancing internal septations, and focal masses with no (or minimal) enhancement. The validated- and revised-model performance characteristics were, respectively, as follows: sensitivity, 100% and 96%; specificity, 69% and 79%; positive predictive value, 75% and 76%; negative predictive value, 100% and 97%; and overall accuracy, 83% and 86%. CONCLUSION: An interpretation model that incorporates breast MR architectural features can achieve high sensitivity and improve specificity for diagnosing breast cancer.
Authors: Maria Adele Marino; Paola Clauser; Ramona Woitek; Georg J Wengert; Panagiotis Kapetas; Maria Bernathova; Katja Pinker-Domenig; Thomas H Helbich; Klaus Preidler; Pascal A T Baltzer Journal: Eur Radiol Date: 2015-10-29 Impact factor: 5.315
Authors: A C Schmitz; N H G M Peters; W B Veldhuis; A M Fernandez Gallardo; P J van Diest; G Stapper; R van Hillegersberg; W P Th M Mali; M A A J van den Bosch Journal: Eur Radiol Date: 2007-09-20 Impact factor: 5.315
Authors: Matthias Dietzel; Pascal A T Baltzer; Tibor Vag; Aimee Herzog; Mieczyslaw Gajda; Oumar Camara; Werner A Kaiser Journal: Korean J Radiol Date: 2010-02-22 Impact factor: 3.500
Authors: Hanaa Al-Khawari; Reji Athyal; Agnes Kovacs; Mervat Al-Saleh; John Patrick Madda Journal: Ann Saudi Med Date: 2009 Jul-Aug Impact factor: 1.526