TY  - JOUR
AU  - Kickingereder, Philipp
AU  - Neuberger, Ulf
AU  - Bonekamp, David
AU  - Piechotta, Paula L
AU  - Götz, Michael
AU  - Wick, Antje
AU  - Sill, Martin
AU  - Kratz, Annekathrin
AU  - Shinohara, Russell T
AU  - Jones, David
AU  - Radbruch, Alexander
AU  - Muschelli, John
AU  - Unterberg, Andreas
AU  - Debus, Jürgen
AU  - Schlemmer, Heinz-Peter
AU  - Herold-Mende, Christel
AU  - Pfister, Stefan
AU  - von Deimling, Andreas
AU  - Wick, Wolfgang
AU  - Capper, David
AU  - Maier-Hein, Klaus
AU  - Bendszus, Martin
TI  - Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma.
JO  - Neuro-Oncology
VL  - 20
IS  - 6
SN  - 1523-5866
CY  - Oxford
PB  - Oxford Univ. Press
M1  - DKFZ-2018-00643
SP  - 848 - 857
PY  - 2018
AB  - The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma.Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves).The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ≤ 0.01). Prediction errors decreased by 36
LB  - PUB:(DE-HGF)16
C6  - pmid:29036412
C2  - pmc:PMC5961168
DO  - DOI:10.1093/neuonc/nox188
UR  - https://inrepo02.dkfz.de/record/134853
ER  -