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. Issue 2 (17th June 2019)

• Record Type: Journal Article
• Authors: Bell, Laura C.; Stokes, Ashley M.; Quarles, C. Chad
• Is Part Of: Journal of magnetic resonance imaging. Volume 51:Issue 2(2020)
• Page Start: 547
• Page End: 553
• Publication Date: 2019-06-17
• Subjects: DSC‐MRI -- CTH -- CBF -- MTT -- postprocessing -- GBM tumor grading; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26837
• Languages: English
• ISSNs: 1053-1807

Abstract

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.