TY - JOUR
AU - Bickelhaupt, Sebastian
AU - Jaeger, Paul Ferdinand
AU - Laun, Frederik
AU - Lederer, Wolfgang
AU - Daniel, Heidi
AU - Kuder, Tristan Anselm
AU - Wuesthof, Lorenz
AU - Paech, Daniel
AU - Bonekamp, David
AU - Radbruch, Alexander
AU - Delorme, Stefan
AU - Schlemmer, Heinz-Peter
AU - Steudle, Franziska Hildegard
AU - Maier-Hein, Klaus
TI - Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer.
JO - Radiology
VL - 287
IS - 3
SN - 1527-1315
CY - Oak Brook, Ill.
PB - Soc.
M1 - DKFZ-2018-00685
SP - 761 - 770
PY - 2018
AB - 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
LB - PUB:(DE-HGF)16
C6 - pmid:29461172
DO - DOI:10.1148/radiol.2017170273
UR - https://inrepo02.dkfz.de/record/135948
ER -