%0 Journal Article
%A Bickelhaupt, Sebastian
%A Jaeger, Paul Ferdinand
%A Laun, Frederik
%A Lederer, Wolfgang
%A Daniel, Heidi
%A Kuder, Tristan Anselm
%A Wuesthof, Lorenz
%A Paech, Daniel
%A Bonekamp, David
%A Radbruch, Alexander
%A Delorme, Stefan
%A Schlemmer, Heinz-Peter
%A Steudle, Franziska Hildegard
%A Maier-Hein, Klaus
%T Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer.
%J Radiology
%V 287
%N 3
%@ 1527-1315
%C Oak Brook, Ill.
%I Soc.
%M DKFZ-2018-00685
%P 761 - 770
%D 2018
%X Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0-1500 sec/mm2) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:29461172
%R 10.1148/radiol.2017170273
%U https://inrepo02.dkfz.de/record/135948