Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity

Abstract

Purpose: To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma.

Materials and methods: Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score.

Results: The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score.

Conclusion: Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.

Figure 1:

Graphs show perfusion time series and calculated principle components. Left: Graph shows the mean perfusion signal (DSC MR imaging time series) of all voxels for a given ROI. Right: The calculated principal components for each tissue type, based on the perfusion signal, are demonstrated. CSF = cerebrospinal fluid, ED = edema, ET = enhancing tumor, GM = gray matter, NCR = nonenhancing core, PC1 = first principal component, PC2 = second principal component, PC3 = third principal component, WM = white matter.

Figure 2:

Graphs show separability of voxels within the peritumoral region. The figures demonstrate the separability of the near (likely infiltrated) and far (not infiltrated) ROIs within the peritumoral region. Red represents the probability density function of the near voxels, while blue represents the far voxels. Left: Graph, based on our method, shows two completely separable histograms (groups). Right: Graph, based on rCBV intensity values, shows overlapping histograms (the x-axis shows the intensity in arbitrary units scaled between 0 and 255, and the y-axis is the number of voxels).

Figure 3:

Maps of peritumoral heterogeneity demonstrate images obtained in two representative patients after principal components analysis. Left to right, the first column shows the preoperative T1-weighted gadolinium-enhanced image; the second column shows the rCBV map for each subject, in which the peritumoral region demonstrates relatively low, homogeneous perfusion. The third column shows the map of heterogeneity generated from the SVM scores within the peritumoral region. Red regions are areas most similar to highly infiltrated tissue, while blue regions are more similar to tissue with low infiltration. The last column depicts the T1-weighted gadolinium-enhanced image, at the same section, for each subject after tumor recurrence.

Figure 4:

Graph shows the Kaplan-Meier survival curve for the three subject groups, with low (red), medium (orange), and high (green) heterogeneity, respectively. The analysis was based on 79 subjects that were dichotomized according to the heterogeneity score. The calculated hazard ratio for the low-heterogeneity group is 2.23 (95% confidence interval: 1.4, 3.6; P < .01).

Figure 5:

Principal component images and plots demonstrate the first four principal components of an MR image (left), along with the plot of the corresponding principal eigenvector (right), shown to illustrate the breadth of information contained within the perfusion time-series. The plots have been constructed from the perfusion signal of all voxels, with the error bar representing ±2 standard deviations of the respective principal component. Red and blue lines represent the negative and positive parts of principal components, respectively. PC1 = first principal component, PC2 = second principal component, PC3 = third principal component, PC4 = fourth principal component.