Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging.

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIFDSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIFDCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIFDSC and AIFDCE were obtained from each image. Volume transfer constant (Ktrans), volume of vascular plasma space (vp), and volume of extravascular extracellular space (ve) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIFDSC-driven mean Ktrans and ve were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIFDCE (area under the ROC curve: mean Ktrans, 0.796 vs 0.645, P = .038; mean ve, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIFDSC than with AIFDCE (Ktrans, 0.737 vs 0.095; vp, 0.848 vs 0.728; ve, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIFDSC than AIFDCE. Conclusion AIFDSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIFDCE. © RSNA, 2017 Online supplemental material is available for this article.

[1]  Wei Huang,et al.  Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling. , 2016, Journal of magnetic resonance.

[2]  Kerryanne V. Winters,et al.  Effect of $$ T_{2}^{*} $$T2∗ correction on contrast kinetic model analysis using a reference tissue arterial input function at 7 T , 2015, Magnetic Resonance Materials in Physics, Biology and Medicine.

[3]  B. Rosen,et al.  Perfusion imaging with NMR contrast agents , 1990, Magnetic resonance in medicine.

[4]  Lucie Yang,et al.  Nephrogenic systemic fibrosis and class labeling of gadolinium-based contrast agents by the Food and Drug Administration. , 2012, Radiology.

[5]  Jan J W Lagendijk,et al.  Improving the arterial input function in dynamic contrast enhanced MRI by fitting the signal in the complex plane , 2016, Magnetic resonance in medicine.

[6]  Jiandong Yin,et al.  Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms , 2015, Neuroradiology.

[7]  Areen K. Al-Bashir,et al.  New algorithm for quantifying vascular changes in dynamic contrast‐enhanced MRI independent of absolute T1 values , 2007, Magnetic Resonance in Medicine.

[8]  S Sourbron,et al.  Bolus‐tracking MRI with a simultaneous T1‐ and T  2* ‐measurement , 2009, Magnetic resonance in medicine.

[9]  S. Choi,et al.  Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. , 2015, Radiology.

[10]  F. Fennessy,et al.  Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation. , 2015, Magnetic resonance imaging.

[11]  F. Calamante.  Arterial input function in perfusion MRI: a comprehensive review. , 2013, Progress in nuclear magnetic resonance spectroscopy.

[12]  J. Verhoeff,et al.  Reproducibility of the gadolinium concentration measurements and of the fitting parameters of the vascular input function in the superior sagittal sinus in a patient population. , 2010, Magnetic resonance imaging.

[13]  T. Yankeelov,et al.  Arterial input functions determined from MR signal magnitude and phase for quantitative dynamic contrast‐enhanced MRI in the human pelvis , 2011, Magnetic resonance in medicine.

[14]  A. Ouzzane,et al.  Impact of arterial input function selection on the accuracy of dynamic contrast‐enhanced MRI quantitative analysis for the diagnosis of clinically significant prostate cancer , 2016, Journal of magnetic resonance imaging : JMRI.

[15]  S. Filice,et al.  Dynamic Contrast‐Enhanced Perfusion MRI of High Grade Brain Gliomas Obtained with Arterial or Venous Waveform Input Function , 2016, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[16]  P. Tofts.  Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[17]  Andreas Makris,et al.  Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response? , 2016, The British journal of radiology.

[18]  Konstantin Nikolaou,et al.  Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance. , 2016, Magnetic resonance imaging.

[19]  A. Ferrer,et al.  Automatic individual arterial input functions calculated from PCA outperform manual and population‐averaged approaches for the pharmacokinetic modeling of DCE‐MR images , 2015, Journal of magnetic resonance imaging : JMRI.

[20]  Michael Bock,et al.  Quantification of renal perfusion using an intravascular contrast agent (part 1): Results in a canine model , 2003, Magnetic resonance in medicine.

[21]  Suk-Tak Chan,et al.  Early time points perfusion imaging , 2011, NeuroImage.

[22]  S. Sourbron.  Technical aspects of MR perfusion. , 2010, European journal of radiology.

[23]  D. Collins,et al.  Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions , 2015, European Radiology.

[24]  Seong Ho Park,et al.  Glioma: Application of Histogram Analysis of Pharmacokinetic Parameters from T1-Weighted Dynamic Contrast-Enhanced MR Imaging to Tumor Grading , 2014, American Journal of Neuroradiology.

[25]  C K Kuhl,et al.  Breast neoplasms: T2* susceptibility-contrast, first-pass perfusion MR imaging. , 1997, Radiology.

[26]  M. Knopp,et al.  Dynamic contrast‐enhanced MRI using Gd‐DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors , 2001, Magnetic resonance in medicine.

[27]  S. Choi,et al.  Dynamic contrast-enhanced MR imaging in predicting progression of enhancing lesions persisting after standard treatment in glioblastoma patients: a prospective study , 2017, European Radiology.

[28]  Matthew S Davenport,et al.  Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. , 2013, Radiology.