Marianne Schell1,2, Irada Pflüger1,2, Gianluca Brugnara1,2, Fabian Isensee3, Ulf Neuberger1,3,2, Martha Foltyn1,2, Tobias Kessler4,5, Felix Sahm6,7, Antje Wick4, Martha Nowosielski4,7,8, Sabine Heiland1, Michael Weller9, Michael Platten10, Klaus H Maier-Hein3,10, Andreas Von Deimling6,7, Martin J Van Den Bent11, Thierry Gorlia12, Wolfgang Wick4,5,7, Martin Bendszus1, Philipp Kickingereder1,2. 1. Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany. 2. Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany. 3. Medical Image Computing, German Cancer Research Center, Heidelberg, Germany. 4. Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany. 5. Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany. 6. Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany. 7. Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany. 8. Department of Neurology, Medical University, Innsbruck, Austria. 9. Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland. 10. Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany. 11. Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, Netherlands. 12. European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Abstract
BACKGROUND: This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS: A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). RESULTS:ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). CONCLUSIONS:ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
RCT Entities:
BACKGROUND: This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS: A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). RESULTS: ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEVpatients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). CONCLUSIONS: ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
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