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MET, AXL, RET). the OS in all anti-VEGF therapeutic scenarios tested. Training and validation data confirmed baseline ADCL was an independent predictive biomarker for OS in anti-VEGF therapies, but not lomustine, after accounting for age and baseline enhancing tumor volume. Conclusions Pre-treatment diffusion MRI is usually a predictive imaging biomarker for OS in patients with recurrent GBM treated with anti-VEGF monotherapy at first or second relapse. cytotoxic chemotherapies, we examined a total of 81 recurrent GBM patients treated with either bevacizumab monotherapy (N=42; 10mg/kg every 2 weeks) or lomustine (N=39; 110mg/m2 every 6 weeks) as part of a multicenter (14 sites), Dutch investigator initiated phase II trial exploring bevacizumab, lomustine, or bevacizumab plus lomustine in patients with GBM at first recurrence (BELOB; NTR1929). Patients treated with combination bevacizumab and lomustine were not included in the current study. For more information around the trial, therapeutic dosing information, and details on inclusion and exclusion criteria, see the final published clinical trial information from Taal examinations, approximately 1C7 days apart, prior to treatment initiation for the purposes of reproducibility testing. Contrast-Enhanced T1-Weighted Digital Subtraction Maps (T1 Subtraction Maps) Contrast-enhanced T1-weighted digital Metyrosine subtraction maps were created by registration, normalization, and subtraction of pre-contrast from post-contrast T1-weighted images as layed out previously (29). Firstly, affine registration was performed between pre- and post-contrast T1-weighted images by using a 12 degree-of-freedom transformation and a correlation coefficient cost function TCF10 in FSL (FLIRT; FMRIB Software Library, Oxford, England; Secondly, the image intensities for both pre- and post-contrast T1-weighted images were intensity normalized using custom (is raw image signal intensity, is usually normalized, are voxel coordinates, and stand for whole brain. Thirdly, voxel-wise subtraction was performed between intensity normalized pre-contrast and post-contrast T1-weighted images (Fig 2A). Lastly, voxels with T1 subtraction values greater than zero were isolated, then manual corrections were made to exclude vessels or erroneous voxels, resulting in final T1 subtraction maps used to extract tumor volumes and act as continuous volumes of interest (VOIs) for ADC histogram analysis (Fig 2C), described below. Any satellite enhancing lesions were pooled together into a single VOI for subsequent analysis. Open in a separate windows Fig 2 Contrast enhanced T1-weighted subtraction maps and apparent diffusion coefficient (ADC) histogram analysis in a patient with recurrent GBMA) Pre-treatment pre-contrast, post-contrast, and T1 subtraction maps in a patient with recurrent GBM. B) T1-subtraction defined tumor segmentation overlaid on ADC map. C) Resulting ADC histogram analysis results in the same patient. Note: Black packed circles indicate ADC measurements extracted from contrast enhancing tumor regions. Red line indicates double Gaussian mixed model fit to the underlying ADC histogram. ADC Histogram Analysis T1 subtraction-defined enhancing tumor volumes were used to extract ADC values Metyrosine Metyrosine for ADC histogram analysis. Nonlinear regression of a double Gaussian mixed model was then performed for the extracted ADC histograms using GraphPad Prism, Version 4.0c (GraphPad Software, San Diego, California). The model used for the double Gaussian was defined by the following equation: is the relative proportion of voxels represented by the lower histogram, represents the lower and represents the higher of the two mixed Gaussian distributions (Fig 2D). Resulting model fits were visually inspected and rerun with different initial conditions until adequate convergence was obtained. Goodness of fit was decided to be adequate if the adjusted R2 0.7. This approach is similar to those used previously (18C20, 30). Statistical Analyses and Interpretation Reproducibility in ADCL measurements within the enhancing tumor were evaluated by calculating the coefficient of variance (COV) of pre-treatment, double baseline MR examinations acquired as part of the cediranib trial. Also, a paired t-test was used to determine whether a significant difference in ADCL was observed between these two baseline time points. To determine whether this variability results in meaningful changes in the classification of ADCL phenotypes for individual patients, the proportion of patients with the same ADCL categorization (e.g. high vs. low ADCL) were calculated as a function of different ADCL thresholds. Next, optimal ADCL thresholds were determined by Metyrosine calculating the Mantel-Haenszel hazard ratio and corresponding 0.05 was considered statistically significant. No corrections for multiple comparisons were performed. Statistical analyses were performed with Stata 12 (2011; College Station, TX) or GraphPad Prism v6.0h (GraphPad Software, Inc., La Jolla, CA). All errors are presented in standard error of the mean (S.E.M.). RESULTS.