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@ARTICLE{Yun:304500,
      author       = {Y. C. Yun$^*$ and J. M. E. Jende$^*$ and K. Holz and S.
                      Wolf and F. Garhöfer and A. Hohmann and P. Vollmuth and M.
                      Bendszus and H.-P. Schlemmer$^*$ and F. Sahm$^*$ and S.
                      Heiland and W. Wick and V. Venkataramani and F. T. Kurz$^*$},
      title        = {{R}adiomics features from the peritumoral region can be
                      associated with the epilepsy status of glioblastoma
                      patients.},
      journal      = {Frontiers in oncology},
      volume       = {15},
      issn         = {2234-943X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2025-01889},
      pages        = {1587745},
      year         = {2025},
      note         = {#EA:E010#LA:E010#},
      abstract     = {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 $(70\%)$ and a
                      validation $(30\%)$ cohort. The training cohort was used for
                      feature selection with ElasticNet and model optimization.
                      Various machine learning models, including logistic
                      regression (LR), were used to predict epilepsy status. The
                      models' performances were evaluated with the validation
                      cohort, and the area under the curve of the receiver
                      operating characteristics (AUC) was used as a measure. For
                      identifying relevant features, permutation feature
                      importance was applied.The performance of LR using radiomics
                      features from only a single ROI in the validation cohort was
                      AUC = 0.83 $(95\%$ CI: 0.76-0.91) and AUC = 0.77 $(95\%$ CI:
                      0.69-0.85) for the peritumoral and white matter regions,
                      respectively. The most important features in peritumoral
                      regions were shape features, while for the white matter
                      region, higher-order features from FLAIR were most
                      relevant.Radiomics features from peritumoral and
                      normal-appearing white matter can be associated with
                      epilepsy status at diagnosis, suggesting an important role
                      of these regions for the development of epilepsy in
                      glioblastoma patients.},
      keywords     = {MRI (Other) / epilepsy (Other) / glioblastoma (Other) /
                      machine learning (Other) / radiomics (Other) / radiomics
                      features from peritumoral (Other)},
      cin          = {E010 / B300 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)B300-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40927524},
      pmc          = {pmc:PMC12416087},
      doi          = {10.3389/fonc.2025.1587745},
      url          = {https://inrepo02.dkfz.de/record/304500},
}