Comparison of Perfusion- and Diffusion-weighted Imaging Parameters in Brain Tumor Studies Processed Using Different Software Platforms

Abstract

Rationale and Objectives To compare quantitative imaging parameter measures from diffusion- and perfusion-weighted imaging magnetic resonance imaging (MRI) sequences in subjects with brain tumors that have been processed with different software platforms. Materials and Methods Scans from 20 subjects with primary brain tumors were selected from the Comprehensive Neuro-oncology Data Repository at Washington University School of Medicine (WUSM) and the Swedish Neuroscience Institute. MR images were coregistered, and each subject's data set was processed by three software packages: 1) vendor-specific scanner software, 2) research software developed at WUSM, and 3) a commercially available, Food and Drug Administration–approved, processing platform (Nordic Ice). Regions of interest (ROIs) were chosen within the brain tumor and normal nontumor tissue. The results obtained using these methods were compared. Results For diffusion parameters, including mean diffusivity and fractional anisotropy, concordance was high when comparing different processing methods. For perfusion-imaging parameters, a significant variance in cerebral blood volume, cerebral blood flow, and mean transit time (MTT) values was seen when comparing the same raw data processed using different software platforms. Correlation was better with larger ROIs (radii ≥ 5 mm). Greatest variance was observed in MTT. Conclusions Diffusion parameter values were consistent across different software processing platforms. Perfusion parameter values were more variable and were influenced by the software used. Variation in the MTT was especially large suggesting that MTT estimation may be unreliable in tumor tissues using current MRI perfusion methods.

Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression

“…While previous papers published revealed differences in mean rCBV measurements from clinical images between software, 17,18 this work makes the additional contributions of analysis of the other previously published metrics of 95th % and % voxels above NAWM. While the other metrics did not prove to be more resistant to intersoftware variability, they had different, large effects on the intersoftware variability without eliminating it.…”

“…The potential for variability has been recognized, 12,16 with recent reports of variability in measurements of mean rCBV between FDA-cleared software packages using clinical DSC-MR images. 17,18 The purpose of this study was to determine whether there were significant differences in multiple rCBV metrics from the same DSC-MR images between three FDA-cleared software packages, and if so, how much disagreement there exists at various thresholds of rCBV used to predict tumor progression. Then, using clinical or outcome-based information to classify whether the analyzed tumors were progressing or not, we investigated whether one software performed better than others for distinguishing between GBM progression and pseudoprogression.…”

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research

“…Here, HOF output is viewed as uniformly formatted CARS (UCARS) ready for repeatable analysis. For instance, UCARS generated by MGA were used to evaluate the performance of various perfusion software packages (Milchenko et al 2014) and machine learning method of tumor segmentation (Prior et al 2013), as well as in clinical research that employed Kaplan-Meier analysis evaluating the connection of blood flow and diffusion imaging metrics with survival of high grade glioma patients (LaMontagne et al 2013). …”

“…In CONDR CARS, about 2–3% of images with large primary brain tumors were mis-registered. Regarding DSC modeling, it should be noted that DSC perfusion measures computed by different algorithms or different software packages may not be directly comparable (Orsingher et al 2014); (Milchenko et al 2014), and therefore only relative comparisons of measures computed by the same software may be valid.…”

MRI biomarkers in neuro-oncology

“…In terms of acquisition, high reliability and reproducibility has been reported on various techniques [54][55][56][57] . Several studies have shown that differences in software or applied algorithms are a large source of variability of measured values [57][58][59] . At present the QIBA profile doesn't provide a claim for rCBV, due to the lack of existing supporting literature 60 .…”