Bogdana Suchorska1, Ulrich Schüller2, Annamaria Biczok3, Markus Lenski3, Nathalie L Albert4, Armin Giese5, Friedrich-Wilhelm Kreth3, Birgit Ertl-Wagner6, Jörg-Christian Tonn3, Michael Ingrisch6. 1. Department of Neurosurgery, Ludwig-Maximilians-University Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: bogdana.suchorska@med.uni-muenchen.de. 2. Center for Neuropathology and Prion Research, Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Neuropathology, University Medical Center, Hamburg-Eppendorf, Germany; Research Institute Children's Cancer Center, Hamburg, Germany; Department of Pediatric Hematology and Oncology, University Medical Center, Hamburg-Eppendorf, Germany. 3. Department of Neurosurgery, Ludwig-Maximilians-University Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany. 4. German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Nuclear Medicine, Ludwig-Maximilians-University Munich, Munich, Germany. 5. German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany; Center for Neuropathology and Prion Research, Ludwig-Maximilians-University Munich, Munich, Germany. 6. Department of Radiology, Ludwig-Maximilians-University Munich, Munich, Germany.
Abstract
BACKGROUND: Mutation of the isocitrate dehydrogenase (IDH) gene and co-deletion on chromosome 1p/19q is becoming increasingly relevant for the evaluation of clinical outcome in glioma. Among the imaging parameters, contrast enhancement (CE) in WHO II/III glioma has been reported to indicate poor outcome in the past. We aimed at reassessing the prognostic value of CE in these tumours within the framework of molecular markers using a machine learning approach (random survival forests [RSF]) as well as conventional Cox regression modelling. METHODS: 301 patients with WHO grade II (n = 181) or grade III glioma (n = 120) were stratified according to their molecular profile. Pre-operative magnetic resonance imaging (MRI) was reviewed and volumetric analyses of CE and T2 volumes were performed followed by conventional univariate and multivariate Cox analyses. Furthermore, the dataset was split into discovery and validation datasets, and RSFs were trained on the discovery dataset to predict the individual risk of each patient. Concordance indices for Cox and RSF models were determined and the variable importance of explanatory variables was assessed using the minimal-depth concept. RESULTS: In IDH mut tumours only, both conventional Cox regression modelling and RSF analyses showed that CE on initial MRI is a prognostic factor for survival with dependence on volume (p < 0.05). In contrast, presence of CE on initial MRI was not associated with outcome in IDH wt tumours. CONCLUSIONS: In patients with diffuse IDH wt gliomas WHO grade II/III, CE is not associated with survival, whereas in tumours with an IDH mutation, presence of CE on initial MRI is linked to inferior survival.
BACKGROUND: Mutation of the isocitrate dehydrogenase (IDH) gene and co-deletion on chromosome 1p/19q is becoming increasingly relevant for the evaluation of clinical outcome in glioma. Among the imaging parameters, contrast enhancement (CE) in WHO II/III glioma has been reported to indicate poor outcome in the past. We aimed at reassessing the prognostic value of CE in these tumours within the framework of molecular markers using a machine learning approach (random survival forests [RSF]) as well as conventional Cox regression modelling. METHODS: 301 patients with WHO grade II (n = 181) or grade III glioma (n = 120) were stratified according to their molecular profile. Pre-operative magnetic resonance imaging (MRI) was reviewed and volumetric analyses of CE and T2 volumes were performed followed by conventional univariate and multivariate Cox analyses. Furthermore, the dataset was split into discovery and validation datasets, and RSFs were trained on the discovery dataset to predict the individual risk of each patient. Concordance indices for Cox and RSF models were determined and the variable importance of explanatory variables was assessed using the minimal-depth concept. RESULTS: In IDH mut tumours only, both conventional Cox regression modelling and RSF analyses showed that CE on initial MRI is a prognostic factor for survival with dependence on volume (p < 0.05). In contrast, presence of CE on initial MRI was not associated with outcome in IDH wt tumours. CONCLUSIONS: In patients with diffuse IDH wt gliomas WHO grade II/III, CE is not associated with survival, whereas in tumours with an IDH mutation, presence of CE on initial MRI is linked to inferior survival.
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