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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
Author(s) -
Bell Laura C.,
Stokes Ashley M.,
Quarles C. Chad
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26837
Subject(s) - glioma , receiver operating characteristic , grading (engineering) , mean transit time , nuclear medicine , neuroradiology , mathematics , medicine , statistics , neurology , radiology , perfusion scanning , biology , perfusion , psychiatry , ecology , cancer research
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.