RT Journal Article SR Electronic T1 Dynamic Contrast-Enhanced Perfusion Processing for Neuroradiologists: Model-Dependent Analysis May Not Be Necessary for Determining Recurrent High-Grade Glioma versus Treatment Effect JF American Journal of Neuroradiology JO Am. J. Neuroradiol. FD American Society of Neuroradiology SP 686 OP 693 DO 10.3174/ajnr.A4190 VO 36 IS 4 A1 Hamilton, J.D. A1 Lin, J. A1 Ison, C. A1 Leeds, N.E. A1 Jackson, E.F. A1 Fuller, G.N. A1 Ketonen, L. A1 Kumar, A.J. YR 2015 UL http://www.ajnr.org/content/36/4/686.abstract AB BACKGROUND AND PURPOSE: Dynamic contrast-enhanced perfusion MR imaging has proved useful in determining whether a contrast-enhancing lesion is secondary to recurrent glial tumor or is treatment-related. In this article, we explore the best method for dynamic contrast-enhanced data analysis. MATERIALS AND METHODS: We retrospectively reviewed 24 patients who met the following conditions: 1) had at least an initial treatment of a glioma, 2) underwent a half-dose contrast agent (0.05-mmol/kg) diagnostic-quality dynamic contrast-enhanced perfusion study for an enhancing lesion, and 3) had a diagnosis by pathology within 30 days of imaging. The dynamic contrast-enhanced data were processed by using model-dependent analysis (nordicICE) using a 2-compartment model and model-independent signal intensity with time. Multiple methods of determining the vascular input function and numerous perfusion parameters were tested in comparison with a pathologic diagnosis. RESULTS: The best accuracy (88%) with good correlation compared with pathology (P = .005) was obtained by using a novel, model-independent signal-intensity measurement derived from a brief integration beginning after the initial washout and by using the vascular input function from the superior sagittal sinus for normalization. Modeled parameters, such as mean endothelial transfer constant > 0.05 minutesāˆ’1, correlated (P = .002) but did not reach a diagnostic accuracy equivalent to the model-independent parameter. CONCLUSIONS: A novel model-independent dynamic contrast-enhanced analysis method showed diagnostic equivalency to more complex model-dependent methods. Having a brief integration after the first pass of contrast may diminish the effects of partial volume macroscopic vessels and slow progressive enhancement characteristic of necrosis. The simple modeling is technique- and observer-dependent but is less time-consuming. AUCarea under the curveDCEdynamic contrast-enhanced perfusion MRIKtransendothelial transfer constantSSSsuperior sagittal sinusVIFvascular input functionvpvascular plasma volume fraction