%0 Journal Article
%A Bickelhaupt, Sebastian
%A Paech, Daniel
%A Kickingereder, Philipp
%A Steudle, Franziska
%A Lederer, Wolfgang
%A Daniel, Heidi
%A Götz, Michael
%A Gählert, Nils
%A Tichy, Diana
%A Wiesenfarth, Manuel
%A Laun, Frederik
%A Maier-Hein, Klaus
%A Schlemmer, Heinz-Peter
%A Bonekamp, David
%T Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
%J Journal of magnetic resonance imaging
%V 46
%N 2
%@ 1053-1807
%C New York, NY
%I Wiley-Liss
%M DKFZ-2017-01507
%P 604 - 616
%D 2017
%X To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences.From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC.The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:28152264
%R 10.1002/jmri.25606
%U https://inrepo02.dkfz.de/record/125374