Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging

Abstract

Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTR_asym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTR_asym values using PCs. Our predicted map correlated with MTR_asym values with Spearman's r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p < 0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.

Figure 1

An illustration of the perfusion time-series in tumorous subregions, i.e., ET, NC, and ED (A); and the clustering of each tissue type using PC analysis (B), signifying the potential of the PCs in capturing tissue characteristics. PC1, PC2, and PC3 represent the first, second, and third principal components, respectively. ET Enhancing tumor, NC Necrotic core, ED paeritumoral edema.

Figure 2

Conventional MRI, including T1, T1-Gd, T2, and T2-FLAIR, scans of a 58-year-old male patient included in our study. Map of a proxy to relative cerebral blood volume (ap-rCBV) derived from DSC-MRI scans with CaPTk software. Three principal components (PCs), PC1 to PC3, calculated using PCA of the hemodynamic perfusion curves, along with the MTR_asym image constructed using the seven PCs in association with the actual MTR_asym image. CaPTk version 1.8.1 (www.med.upenn.edu/cbica/captk/).

Figure 3

Demonstration of (A) bivariate histogram of the constructed in comparison with actual MTR_asym images; and (B) association of the clusters of tumor tissues in the constructed versus actual MTR_asym image.

Figure 4

(A) Perfusion curves calculated within regions of low and high MTR_asym (shown in blue and red colors, respectively), suggesting poor discrimination of the regions solely based on hemodynamic curves. (B) Discrimination of low and high MTR_asym regions based on PC analysis; PC1 = principal component 1; PC2 = principal component 2; PC3 = principal component 3. (C) The three principal components for high MTR_asym regions, yielding a marked differentiation of these regions based on the PCs.

Figure 5

The perfusion curves calculated form the regions with highest (red) and lowest (blue) values on individual Principal Component images: (left) Principal Component 1; (middle) Principal Component 2; and (right) Principal Component 3.