• Michael Ingrisch, Moritz Jörg Schneider, Dominik Nörenberg, Giovanna Negrao de Figueiredo, Klaus Maier-Hein, Bogdana Suchorska, Ulrich Schüller, Nathalie Albert, Hartmut Brückmann, Maximilian Reiser, Jörg-Christian Tonn, and Birgit Ertl-Wagner.
• From the *Josef Lissner Laboratory for Biomedical Imaging, and †Institute for Clinical Radiology, Ludwig-Maximilians University Hospital Munich, Munich; ‡Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg; §Department of Neurosurgery, and ∥Center for Neuropathology and Prion Research, Ludwig-Maximilian University Hospital Munich, Munich, Germany; ¶Research Institute Children's Cancer Center, Hamburg; Departments of #Nuclear Medicine, and **Neuroradiology, Ludwig-Maximilian University Hospital, Munich, Germany.
• Invest Radiol. 2017 Jun 1; 52 (6): 360-366.

ObjectivesThe aim of this study was to investigate whether radiomic analysis with random survival forests (RSFs) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging (MRI) scans in a cohort of glioblastoma multiforme (GBM) patients with uniform treatment.Materials And MethodsThis retrospective study was approved by the institutional review board and informed consent was waived. The MRI scans from 66 patients with newly diagnosed GBM from a previous prospective study were analyzed. Tumors were segmented manually on contrast-enhanced 3-dimensional T1-weighted images. Using these segmentations, P = 208 quantitative image features characterizing tumor shape, signal intensity, and texture were calculated in an automated fashion. On this data set, an RSF was trained using 10-fold cross validation to establish a link between image features and overall survival, and the individual risk for each patient was predicted. The mean concordance index was assessed as a measure of prediction accuracy. Association of individual risk with overall survival was assessed using Kaplan-Meier analysis and a univariate proportional hazards model.ResultsMean overall survival was 14 months (range, 0.8-85 months). Mean concordance index of the 10-fold cross-validated RSF was 0.67. Kaplan-Meier analysis clearly distinguished 2 patient groups with high and low predicted individual risk (P = 5.5 × 10). Low predicted individual mortality was found to be a favorable prognostic factor for overall survival in a univariate Cox proportional hazards model (hazards ratio, 1.038; 95% confidence interval, 1.015-1.062; P = 0.0059).ConclusionsThis study demonstrates that baseline MRI in GBM patients contains prognostic information, which can be accessed by radiomic analysis using RSFs.

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