TY  - JOUR
AU  - Zhang, Kevin Sun
AU  - Neelsen, Christian Jan Oliver
AU  - Wennmann, Markus
AU  - Hielscher, Thomas
AU  - Kovacs, Balint
AU  - Glemser, Philip Alexander
AU  - Görtz, Magdalena
AU  - Stenzinger, Albrecht
AU  - Maier-Hein, Klaus H
AU  - Huber, Johannes
AU  - Schlemmer, Heinz-Peter
AU  - Bonekamp, David
TI  - In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning.
JO  - Scientific reports
VL  - 15
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Springer Nature
M1  - DKFZ-2025-01689
SP  - 29703
PY  - 2025
N1  - #EA:E010#LA:E010#
AB  - Despite academic success, radiomics-based machine learning algorithms have not reached clinical practice, partially due to limited repeatability/reproducibility. To address this issue, this work aims to identify a stable subset of radiomics features in prostate MRI for radiomics modelling. A prospective study was conducted in 43 patients who received a clinical MRI examination and a research exam with repetition of T2-weighted and two different diffusion-weighted imaging (DWI) sequences with repositioning in between. Radiomics feature (RF) extraction was performed from MRI segmentations accounting for intra-rater and inter-rater effects, and three different image normalization methods were compared. Stability of RFs was assessed using the concordance correlation coefficient (CCC) for different comparisons: rater effects, inter-scan (before and after repositioning) and inter-sequence (between the two diffusion-weighted sequences) variability. In total, only 64 out of 321 (  20
KW  - Magnetic resonance imaging (Other)
KW  - Observer variation (Other)
KW  - Prostate (Other)
KW  - Radiomics (Other)
KW  - Reproducibility of results (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40804076
DO  - DOI:10.1038/s41598-025-09989-7
UR  - https://inrepo02.dkfz.de/record/303498
ER  -