Radiomic profiling of glioblastoma

• Verfasst von: Vollmuth, Philipp [VerfasserIn]
• Verfasst von: Burth, Sina [VerfasserIn]
• Verfasst von: Wick, Antje [VerfasserIn]
• Verfasst von: Götz, Michael [VerfasserIn]
• Verfasst von: Eidel, Oliver [VerfasserIn]
• Verfasst von: Schlemmer, Heinz-Peter [VerfasserIn]
• Verfasst von: Maier-Hein, Klaus H. [VerfasserIn]
• Verfasst von: Wick, Wolfgang [VerfasserIn]
• Verfasst von: Bendszus, Martin [VerfasserIn]
• Verfasst von: Radbruch, Alexander [VerfasserIn]
• Verfasst von: Bonekamp, David [VerfasserIn]
• Titel: Radiomic profiling of glioblastoma
• Titelzusatz: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models
• Verf.angabe: Philipp Kickingereder, Sina Burth, Antje Wick, Michael Götz, Oliver Eidel, Heinz-Peter Schlemmer, Klaus H. Maier-Hein, Wolfgang Wick, Martin Bendszus, Alexander Radbruch, David Bonekamp
• E-Jahr: 2016
• Jahr: [September 2016]
• Umfang: 10 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 11.05.2020
• Titel Quelle: Enthalten in: Radiology
• Ort Quelle: Oak Brook, Ill. : Soc., 1923
• Jahr Quelle: 2016
• Band/Heft Quelle: 280(2016), 3, Seite 880-889
• ISSN Quelle: 1527-1315
• DOI: doi:10.1148/radiol.2016160845
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1697840612

Abstract

PurposeTo evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models.Materials and MethodsRetrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters.ResultsSPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637).ConclusionAn 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated.© RSNA, 2016Online supplemental material is available for this article.