000304500 001__ 304500
000304500 005__ 20250911114929.0
000304500 0247_ $$2doi$$a10.3389/fonc.2025.1587745
000304500 0247_ $$2pmid$$apmid:40927524
000304500 0247_ $$2pmc$$apmc:PMC12416087
000304500 037__ $$aDKFZ-2025-01889
000304500 041__ $$aEnglish
000304500 082__ $$a610
000304500 1001_ $$0P:(DE-He78)d3970476eaefe6c7002e9eb4041ea68f$$aYun, Yeong Chul$$b0$$eFirst author$$udkfz
000304500 245__ $$aRadiomics features from the peritumoral region can be associated with the epilepsy status of glioblastoma patients.
000304500 260__ $$aLausanne$$bFrontiers Media$$c2025
000304500 3367_ $$2DRIVER$$aarticle
000304500 3367_ $$2DataCite$$aOutput Types/Journal article
000304500 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1757580782_26501
000304500 3367_ $$2BibTeX$$aARTICLE
000304500 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000304500 3367_ $$00$$2EndNote$$aJournal Article
000304500 500__ $$a#EA:E010#LA:E010#
000304500 520__ $$aIdentifying radiomics features that help predict whether glioblastoma patients are prone to developing epilepsy may contribute to an improvement of preventive treatment and a better understanding of the underlying pathophysiology.In this retrospective study, 3-T MRI data of 451 pretreatment glioblastoma patients (mean age: 61.2 ± 11.8 years; 268 men, 183 women) were analyzed. Three hundred thirty-six patients reported no epilepsy, while 115 patients were diagnosed with symptomatic epilepsy. A total of 1,546 radiomics features were extracted from contrast-enhancing tumor, peritumoral regions, and normal-appearing white matter as regions of interest using PyRadiomics. The dataset was initially split into a training (70%) and a validation (30%) cohort. The training cohort was used for feature selection with ElasticNet and model optimization. Various machine learning models, including logistic regression (LR), were used to predict epilepsy status. The models' performances were evaluated with the validation cohort, and the area under the curve of the receiver operating characteristics (AUC) was used as a measure. For identifying relevant features, permutation feature importance was applied.The performance of LR using radiomics features from only a single ROI in the validation cohort was AUC = 0.83 (95% CI: 0.76-0.91) and AUC = 0.77 (95% CI: 0.69-0.85) for the peritumoral and white matter regions, respectively. The most important features in peritumoral regions were shape features, while for the white matter region, higher-order features from FLAIR were most relevant.Radiomics features from peritumoral and normal-appearing white matter can be associated with epilepsy status at diagnosis, suggesting an important role of these regions for the development of epilepsy in glioblastoma patients.
000304500 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000304500 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000304500 650_7 $$2Other$$aMRI
000304500 650_7 $$2Other$$aepilepsy
000304500 650_7 $$2Other$$aglioblastoma
000304500 650_7 $$2Other$$amachine learning
000304500 650_7 $$2Other$$aradiomics
000304500 650_7 $$2Other$$aradiomics features from peritumoral
000304500 7001_ $$0P:(DE-He78)af2fba3014dac56ccf4188c9879dce19$$aJende, Johann M E$$b1$$udkfz
000304500 7001_ $$aHolz, Katharina$$b2
000304500 7001_ $$aWolf, Sabine$$b3
000304500 7001_ $$aGarhöfer, Freya$$b4
000304500 7001_ $$aHohmann, Anja$$b5
000304500 7001_ $$aVollmuth, Philipp$$b6
000304500 7001_ $$aBendszus, Martin$$b7
000304500 7001_ $$0P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aSchlemmer, Heinz-Peter$$b8$$udkfz
000304500 7001_ $$0P:(DE-He78)a1f4b408b9155beb2a8f7cba4d04fe88$$aSahm, Felix$$b9$$udkfz
000304500 7001_ $$aHeiland, Sabine$$b10
000304500 7001_ $$aWick, Wolfgang$$b11
000304500 7001_ $$aVenkataramani, Varun$$b12
000304500 7001_ $$0P:(DE-He78)ea7f20e71e3cb1a864c23f2f09f0b0b9$$aKurz, Felix Tobias$$b13$$eLast author$$udkfz
000304500 773__ $$0PERI:(DE-600)2649216-7$$a10.3389/fonc.2025.1587745$$gVol. 15, p. 1587745$$p1587745$$tFrontiers in oncology$$v15$$x2234-943X$$y2025
000304500 909CO $$ooai:inrepo02.dkfz.de:304500$$pVDB
000304500 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)d3970476eaefe6c7002e9eb4041ea68f$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000304500 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)af2fba3014dac56ccf4188c9879dce19$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000304500 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000304500 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)a1f4b408b9155beb2a8f7cba4d04fe88$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000304500 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)ea7f20e71e3cb1a864c23f2f09f0b0b9$$aDeutsches Krebsforschungszentrum$$b13$$kDKFZ
000304500 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000304500 9141_ $$y2025
000304500 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFRONT ONCOL : 2022$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-08-08T17:01:20Z
000304500 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-08-08T17:01:20Z
000304500 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2024-08-08T17:01:20Z
000304500 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2024-08-08T17:01:20Z
000304500 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2024-12-18
000304500 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2024-12-18
000304500 9202_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x0
000304500 9201_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x0
000304500 9201_ $$0I:(DE-He78)B300-20160331$$kB300$$lKKE Neuropathologie$$x1
000304500 9201_ $$0I:(DE-He78)HD01-20160331$$kHD01$$lDKTK HD zentral$$x2
000304500 9200_ $$0I:(DE-He78)E010-20160331$$kE010$$lE010 Radiologie$$x0
000304500 980__ $$ajournal
000304500 980__ $$aVDB
000304500 980__ $$aI:(DE-He78)E010-20160331
000304500 980__ $$aI:(DE-He78)B300-20160331
000304500 980__ $$aI:(DE-He78)HD01-20160331
000304500 980__ $$aUNRESTRICTED