Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade

Abstract

Background and purpose: Numerous different parameters measured by perfusion MR imaging can be used for characterizing gliomas. Parameters derived from 3 different analyses were correlated with histopathologically confirmed grade in gliomas to determine which parameters best predict tumor grade.

Methods: Seventy-four patients with gliomas underwent dynamic susceptibility contrast-enhanced MR imaging (DSC MR imaging). Data were analyzed by 3 different algorithms. Analysis 1 estimated relative cerebral blood volume (rCBV) by using a single compartment model. Analysis 2 estimated fractional plasma volume (V(p)) and vascular transfer constant (K(trans)) by using a 2-compartment pharmacokinetic model. Analysis 3 estimated absolute cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) by using a single compartment model and an automated arterial input function. The Mann-Whitney U test was used make pairwise comparisons. Binary logistic regression was used to assess whether rCBV, V(p), K(trans), CBV, CBF, and MTT can discriminate high- from low-grade tumors.

Results: rCBV was the best discriminator of tumor grade ype, followed by CBF, CBV, and K(trans). Spearman rank correlation factors were the following: rCBV = 0.812 (P < .0001), CBF = 0.677 (P < .0001), CBV = 0.604 (P < .0001), K(trans) = 0.457 (P < .0001), V(p) = 0.301 (P =.009), and MTT = 0.089 (P = .448). rCBV was the best single predictor, and K(trans) with rCBV was the best set of predictors of high-grade glioma.

Conclusion: rCBV, CBF, CBV K(trans), and V(p) measurements correlated well with histopathologic grade. rCBV was the best predictor of glioma grade, and the combination of rCBV with K(trans) was the best set of metrics to predict glioma grade.

Figures

Fig 1.

The ROC curves associated with the model to diagnose high-grade tumors on the basis of rCBV, rCBV with Ktrans, and Ktrans alone. Diagnostic models based on Ktrans with rCBV and rCBV alone each had significantly higher (P < .01) area under the ROC curve (AUC = 0.94, 0.90, respectively) than did the model based on Ktrans alone (AUC = 0.63). Max indicates maximum.

Fig 2.

Scatterplot of rCBV versus Ktrans shows true low-grade gliomas as crosses and true high grade gliomas as black points. The figure demonstrates that rCBV and Ktrans together are good predictors of glioma grade. The performance of the diagnostic model to predict high-grade tumors using both Ktrans and Max rCBV when overall diagnostic accuracy is highest (sensitivity = 90.7%, specificity = 76.7%) is shown.

Fig 3.

A–H, Low-grade astrocytoma (grade II/IV). Top row, left to right. A, Axial FLAIR image (TR/TE/TI, 9000/110/2500 ms) demonstrates a lesion in the left midbrain with high signal intensity and minor mass effect. B, Axial T1-weighted postcontrast image (TR/TE, 600/14 ms; 1 excitation) demonstrates no evidence of contrast enhancement, in keeping with a low-grade astrocytoma. C and D, Gradient-echo (TR/TE, 1000/54 ms) axial DSC MR imaging and SD25 color map suggests low permeability throughout the lesion. E–G, Bottom row (left to right). rCBV, CBV, and CBF maps demonstrate a few foci of mildly elevated rCBV, CBV, and CBF within the glioma. H, MTT map demonstrates some prolongation in MTT within the tumor.

Fig 4.

A–H, Anaplastic astrocytoma (grade III/IV). Top row, left to right. A, Axial FLAIR image (TR/TE/TI, 9000/110/2500 ms) demonstrates a dominant lesion in the right thalamus with extension to the left thalamus and parietooccipital region. B, Axial T1-weighted postcontrast image (TR/TE, 600/14 ms; 1 excitation) demonstrates heterogeneous contrast enhancement in keeping with an anaplastic astrocytoma. C, Gradient-echo axial DSC MR image (TR/TE, 1000/54 ms). D, SD25 color map shows foci of increased permeability throughout the lesion. E–G, Bottom row (left to right). rCBV, CBV, and CBF maps demonstrate elevated rCBV, CBV, and CBF within the glioma. H, MTT map demonstrates some prolongation in MTT within the tumor.

Fig 5.

A–H, Glioblastoma multiforme (grade IV/IV). Top row, left to right. A, Axial FLAIR image (TR/TE/TI, 9000/110/2500 ms) demonstrates a lesion with surrounding increased signal intensity in the left parietooccipital region. B, Axial T1-weighted postcontrast image (TR/TE, 600/14 ms; 1 excitation) demonstrates heterogeneous contrast enhancement in keeping with a glioblastoma multiforme. C, Gradient-echo axial DSC MR image (TR/TE, 1000/54 ms). D, SD25 color map shows foci of increased permeability anteriorly in the lesion, which appears to “washout” on the axial postcontrast T1-weighted image (arrows), possibly indicating hyperpermeability during the first pass of contrast. The areas of enhancement more posteriorly may reflect more delayed permeability. E–G, Bottom row (left to right). rCBV, CBV, and CBF maps demonstrate elevated rCBV, CBV, and CBF within the glioblastoma multiforme. H, MTT map demonstrates some prolongation in MTT within the tumor.