Apparent diffusion coefficient and tumor volume measurements help stratify progression-free survival of bevacizumab-treated patients with recurrent glioblastoma multiforme

Abstract

Background The aim of this study was to determine whether apparent diffusion coefficient (ADC) bi-component curve-fitting histogram analysis and volume percentage change (VPC) prior to bevacizumab treatment can stratify progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma multiforme (GBM) on first recurrence. Methods We retrospectively evaluated 17 patients with recurrent GBM who received bevacizumab and fotemustine ( n = 13) or only bevacizumab ( n = 4) on first recurrence at our institution between December 2009 and July 2015. Both T2/FLAIR abnormalities and enhancing tumor on T1 images were mapped to the ADC images. ADC-L and ADC-M values were obtained trough bi-Gaussian curve fitting histogram analysis. Furthermore, the study population was dichotomized into two subgroups: patients displaying a reduction in enhancing tumor volume of either >55% or <55% between the mean value calculated at baseline and first follow-up. Subsequently, a second dichotomization was performed according to a reduction in the T2 / FLAIR volume >41% or <41% at first check after treatment. OS and PFS were assessed using volume parameters in a Cox regression model adjusted for significant clinical parameters. Results In univariate analysis, contrast-enhanced (CE)-ADC-L was significantly predictive of PFS ( p = 0.01) and OS ( p = 0.03). When we dichotomized our sample using the 55% cut-off for enhancing tumor volume, CE-VPC was able to predict PFS ( p = 0.01) but not OS ( p = 0.08). In multivariate analysis, only the CE-ADC-L was predictive of PFS ( p = 0.01), albeit not predictive of OS ( p = 0.14). CE-ADC-M, T2/FLAIR-ADC-L, T2/FLAIR-ADC, and T2/FLAIR VPC were not significantly predictive of PFS and OS ( p > 0.05) in both univariate and multivariate analysis. Conclusions CE-ADC and CE-VPC can stratify PFS for patients with recurrent glioblastoma prior to bevacizumab treatment.

Preliminary results using PET/MR in glioblastoma patients treated with regorafenib: an 18F-FET and DWI-ADC comparison

“…However, the post-regorafenib variation in the mean ADC thus calculated did not signi cantly correlate with the corresponding change in FET-positive volume, nor with the RANO response categories. A possible explanation for this discrepancy may lie in the different methodological approaches used here and in the previously published studies [13,34,[43][44][45][35][36][37][38][39][40][41][42]. This highlights the importance of future efforts towards standardizing the analysis of ADC maps before considering the inclusion of this methodology in the response assessment criteria.…”

“…In most of the published studies, the tumor volume was outlined and the VOI constructed on contrast-enhanced T1-weighted images, which were subsequently transferred to the corresponding DWI-ADC images. Buemi et al [34], for example, manually drew the VOIs encompassing the areas of tumor-related contrast enhancement, and T2-weighted/FLAIR abnormalities were mapped onto the corresponding ADC images, thus deriving the CE-ADC and T2/FLAIR-ADC volumes, respectively. Histogram analysis and curve tting using a two-mixture normal distribution model were carried out to calculate the mean ADC of the lowest ADC values in these areas (CE-ADC-L and T2/FLAIR-ADC-L) [35].…”

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics—A Systematic Review

“…Similar results were obtained by Ellingson et al [ 52 ]. Differently, Buemi et al [ 77 ] found ADCL in T1 contrast-enhancing tumour regions useful only for stratifying PFS, but not OS in R-GBM patients prior to BV treatment. In the same study, ADCL in T2/FLAIR abnormalities was unable to stratify both PFS and OS.…”

“…Only two studies [ 39 , 40 ] investigated the ADC metrics survival prediction power for R-GBM patients undergoing a second surgery. Most of them used ADC metrics arising from the histogram analysis based on fitting a double-component Gaussian mixture model to the obtained ADC values [ 40 , 45 , 52 , 70 , 77 ]. In particular, Pope et al [ 45 ] found that low ADCL within tumour regions placed on pre-treatment contrast-enhancing T1-weighted images at baseline was associated with worse OS and PFS in BV-treated R-GBM patients.…”

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

“…Moreover, the correlation threshold in this analysis suggests that these clinical variables offer complementary data to the radiomics, which may lead to the elaboration of other advanced models using different sources of data. Various studies have shown that the apparent diffusion coefficient (ADC) from diffusion MR could represent a good predictor of survival in GBM patients treated with bevacizumab [ 36 , 37 , 38 , 39 ]. In addition, perfusion-derived biomarkers were explored [ 40 , 41 , 42 ], but it should be mentioned that these methods depend largely on the type of the MRI machine, the sequence processing software, and the calculation algorithms and, therefore, are more difficult to apply to different centres in routine clinical practice.…”