TY  - JOUR
AU  - Bonekamp, David
AU  - Kohl, Simon
AU  - Wiesenfarth, Manuel
AU  - Schelb, Patrick
AU  - Radtke, Jan Philipp
AU  - Götz, Michael
AU  - Kickingereder, Philipp
AU  - Yaqubi, Kaneschka
AU  - Hitthaler, Bertram
AU  - Gählert, Nils
AU  - Kuder, Tristan Anselm
AU  - Deister, Fenja
AU  - Freitag, Martin
AU  - Hohenfellner, Markus
AU  - Hadaschik, Boris A
AU  - Schlemmer, Heinz-Peter
AU  - Maier-Hein, Klaus
TI  - Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.
JO  - Radiology
VL  - 289
IS  - 1
SN  - 1527-1315
CY  - Oak Brook, Ill.
PB  - Soc.
M1  - DKFZ-2018-02218
SP  - 128 - 137
PY  - 2018
AB  - Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis. Global and zone-specific random forest RML and mean ADC models for classification of clinically significant prostate cancer (Gleason grade group ≥ 2) were developed on the training set and the fixed models tested on an independent test set. Clinical readings, mean ADC, and radiomics were compared by using the McNemar test and receiver operating characteristic (ROC) analysis. Results In the test set, radiologist interpretation had a per-lesion sensitivity of 88
LB  - PUB:(DE-HGF)16
C6  - pmid:30063191
DO  - DOI:10.1148/radiol.2018173064
UR  - https://inrepo02.dkfz.de/record/141988
ER  -