%0 Journal Article
%A Yun, Yeong Chul
%A Jende, Johann M E
%A Holz, Katharina
%A Wolf, Sabine
%A Garhöfer, Freya
%A Hohmann, Anja
%A Vollmuth, Philipp
%A Bendszus, Martin
%A Schlemmer, Heinz-Peter
%A Sahm, Felix
%A Heiland, Sabine
%A Wick, Wolfgang
%A Venkataramani, Varun
%A Kurz, Felix Tobias
%T Radiomics features from the peritumoral region can be associated with the epilepsy status of glioblastoma patients.
%J Frontiers in oncology
%V 15
%@ 2234-943X
%C Lausanne
%I Frontiers Media
%M DKFZ-2025-01889
%P 1587745
%D 2025
%Z #EA:E010#LA:E010#
%X Identifying radiomics features that help predict whether glioblastoma patients are prone to developing epilepsy may contribute to an improvement of preventive treatment and a better understanding of the underlying pathophysiology.In this retrospective study, 3-T MRI data of 451 pretreatment glioblastoma patients (mean age: 61.2 ± 11.8 years; 268 men, 183 women) were analyzed. Three hundred thirty-six patients reported no epilepsy, while 115 patients were diagnosed with symptomatic epilepsy. A total of 1,546 radiomics features were extracted from contrast-enhancing tumor, peritumoral regions, and normal-appearing white matter as regions of interest using PyRadiomics. The dataset was initially split into a training (70
%K MRI (Other)
%K epilepsy (Other)
%K glioblastoma (Other)
%K machine learning (Other)
%K radiomics (Other)
%K radiomics features from peritumoral (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40927524
%2 pmc:PMC12416087
%R 10.3389/fonc.2025.1587745
%U https://inrepo02.dkfz.de/record/304500