Investig Magn Reson Imaging.  2022 Jun;26(2):104-116. 10.13104/imri.2022.26.2.104.

Percentile-Based Analysis of Non-Gaussian Diffusion Parameters for Improved Glioma Grading

• Karaman MM
• Zhou CY
• Zhang J
• Zhong Z
• Wang K
• Zhu W

• KMID: 2533879
• DOI: http://doi.org/10.13104/imri.2022.26.2.104

Abstract

The purpose of this study is to systematically determine an optimal percentile cutoff in histogram analysis for calculating the mean parameters obtained from a nonGaussian continuous-time random-walk (CTRW) diffusion model for differentiating individual glioma grades. This retrospective study included 90 patients with histopathologically proven gliomas (42 grade II, 19 grade III, and 29 grade IV). We performed diffusion-weighted imaging using 17 b-values (0-4000 s/mm²) at 3T, and analyzed the images with the CTRW model to produce an anomalous diffusion coefficient (D m ) along with temporal (α) and spatial (β) diffusion heterogeneity parameters. Given the tumor ROIs, we created a histogram of each parameter; computed the P-values (using a Student’s t-test) for the statistical differences in the mean D m , α, or β for differentiating grade II vs. grade III gliomas and grade III vs. grade IV gliomas at different percentiles (1% to 100%); and selected the highest percentile with P < 0.05 as the optimal percentile. We used the mean parameter values calculated from the optimal percentile cut-offs to do a receiver operating characteristic (ROC) analysis based on individual parameters or their combinations. We compared the results with those obtained by averaging data over the entire region of interest (i.e., 100th percentile). We found the optimal percentiles for D m , α, and β to be 68%, 75%, and 100% for differentiating grade II vs. III and 58%, 19%, and 100% for differentiating grade III vs. IV gliomas, respectively. The optimal percentile cut-offs outperformed the entire-ROI-based analysis in sensitivity (0.761 vs. 0.690), specificity (0.578 vs. 0.526), accuracy (0.704 vs. 0.639), and AUC (0.671 vs. 0.599) for grade II vs. III differentiations and in sensitivity (0.789 vs. 0.578) and AUC (0.637 vs. 0.620) for grade III vs. IV differentiations, respectively. Percentile-based histogram analysis, coupled with the multi-parametric approach enabled by the CTRW diffusion model using high b-values, can improve glioma grading.

Keyword

Glioma; Glioma grading; Non-Gaussian diffusion-weighted imaging; Continuous-time random-walk model; Histogram analysis; High b value