Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)‐MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T

Abstract

Background Dynamic susceptibility contrast (DSC)‐MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers. Purpose/Hypothesis To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading. Study Type Retrospective study from The Cancer Imaging Archive (TCIA). Population Forty‐nine subjects with low‐ and high‐grade gliomas. Field Strength/Sequence 1.5 and 3.0T clinical systems using a single‐echo echo planar imaging (EPI) acquisition. Assessment Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k‐means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal‐to‐noise ratio (SNR)‐adapted eigenvalue thresholding (Method 1) and Volterra discretization with L‐curve‐based Tikhonov regularization (Method 2). Statistical Tests Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests. Results MTT alone was unable to statistically differentiate glioma grade (P > 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P > 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74–0.84; rCBV AUROC increases 0.78–0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P < 0.007), but not if processed by current conventional DSC methods (Method 1) (P > 0.577). Lastly, higher‐order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone. Data Conclusion With optimized analysis pipelines, higher‐order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:547–553.

Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies

“…Methodology for automatic segmentation of NAWM has recently been reported, demonstrating improvement over manually defined NAWM in the application of tumor grading. 34 Future studies would be helpful for comparing such methodology with standardization of rCBV, particularly for differentiating tumor from PTRE in the posttreatment setting. It will also be important to evaluate standardization and normalization methods with evolving consensus recommendations for DSC acquisition, such as with low-flip angle techniques.…”

Microvasculature Features Derived from Hybrid EPI MRI in Non-Enhancing Adult-Type Diffuse Glioma Subtypes

“…Signal–time curves were plotted for each voxel for both HEPI-GRE and HEPI-SE data separately. We excluded voxels exhibiting a drop of fewer than 2 standard deviations of the baseline signal due to the contrast agent passing through, in both HEPI-GRE and HEPI-SE data, to minimize voxels with erroneous results [ 23 ]. Then, HEPI-GRE and HEPI-SE signal–time curves were converted to transverse relaxation rates of and ∆R 2 (t), respectively, using the following equations [ 13 ]: where TE is the echo time; S GRE (t) and S SE (t) are the signal–time curves of HEPI-GRE and HEPI-SE.…”

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

“…However, multi-echo instead of single-echo acquisitions and the use of a low flip angle without a preload dose have been proposed for leakage correction ( 72 , 93 ). Additionally, variation in post-processing methods and software modeling result in inconsistencies in rCBV calculation ( 88 , 94 ). A recently published set of consensus recommendations for a DSC protocol in HGG by Boxerman et al is a step toward increased standardization of the DSC technique in post-treatment glioma imaging ( 90 ).…”