E A M Hauth1, H Jaeger, S Maderwald, A Mühler, R Kimmig, M Forsting. 1. Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Hufelandstr. 55, 45122, Essen. elke.hauth@uni-essen.de
Abstract
PURPOSE: The aim of the study was an evaluation of the quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography. MATERIAL AND METHODS: In 137 patients, 183 contrast-enhanced lesions were identified in dynamic MR mammography. In 82 lesions histopathology was performed and in 101 lesions follow-up MR mammography was carried out. The contrast kinetics of lesions was analyzed quantitatively, on a pixel-by-pixel basis. The initial signal enhancement was coded by color intensity (bright, medium, dark), the post-initial signal enhancement was coded by color hue (blue, green, red). ROC analysis and logistic regression were performed. RESULTS: Malignant lesions showed a significantly higher number of bright red, medium red and dark red, bright green and medium green pixels than benign lesions. Benign lesions showed a significantly higher number of bright blue, medium blue and dark blue pixels than malignant lesions. The highest areas under the ROC curves (AUC) were found for medium red (AUC = 0.782) and medium green pixels (AUC = 0.733). A regression model with medium red and medium green pixels allows diagnosis of malignant lesions with a sensitivity of 60.7% and a specificity of 83.6%. CONCLUSIONS: The quantification of contrast-enhanced lesions allows objective analysis of the signal intensities in malignant and benign lesions. Therefore, this method might increase the specificity of MR mammography. Further developments are necessary before this method can be used for routine analysis of contrast-enhancing lesions in MR mammography.
PURPOSE: The aim of the study was an evaluation of the quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography. MATERIAL AND METHODS: In 137 patients, 183 contrast-enhanced lesions were identified in dynamic MR mammography. In 82 lesions histopathology was performed and in 101 lesions follow-up MR mammography was carried out. The contrast kinetics of lesions was analyzed quantitatively, on a pixel-by-pixel basis. The initial signal enhancement was coded by color intensity (bright, medium, dark), the post-initial signal enhancement was coded by color hue (blue, green, red). ROC analysis and logistic regression were performed. RESULTS:Malignant lesions showed a significantly higher number of bright red, medium red and dark red, bright green and medium green pixels than benign lesions. Benign lesions showed a significantly higher number of bright blue, medium blue and dark blue pixels than malignant lesions. The highest areas under the ROC curves (AUC) were found for medium red (AUC = 0.782) and medium green pixels (AUC = 0.733). A regression model with medium red and medium green pixels allows diagnosis of malignant lesions with a sensitivity of 60.7% and a specificity of 83.6%. CONCLUSIONS: The quantification of contrast-enhanced lesions allows objective analysis of the signal intensities in malignant and benign lesions. Therefore, this method might increase the specificity of MR mammography. Further developments are necessary before this method can be used for routine analysis of contrast-enhancing lesions in MR mammography.
Authors: Riham H El Khouli; Katarzyna J Macura; Ihab R Kamel; Michael A Jacobs; David A Bluemke Journal: AJR Am J Roentgenol Date: 2011-12 Impact factor: 3.959
Authors: Monique D Dorrius; Marijke C Jansen-van der Weide; Peter M A van Ooijen; Ruud M Pijnappel; Matthijs Oudkerk Journal: Eur Radiol Date: 2011-03-15 Impact factor: 5.315