Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma.
| Autor: | Ingrisch M; 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., Schneider MJ, Nörenberg D, Negrao de Figueiredo G, Maier-Hein K, Suchorska B, Schüller U, Albert N, Brückmann H, Reiser M, Tonn JC, Ertl-Wagner B |
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| Jazyk: | angličtina |
| Zdroj: | Investigative radiology [Invest Radiol] 2017 Jun; Vol. 52 (6), pp. 360-366. |
| DOI: | 10.1097/RLI.0000000000000349 |
| Abstrakt: | Objectives: The 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 Methods: This 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. Results: Mean 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). Conclusions: This study demonstrates that baseline MRI in GBM patients contains prognostic information, which can be accessed by radiomic analysis using RSFs. |
| Databáze: | MEDLINE |
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