Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

Abstract

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75-87.5% sensitivity, 53.85-88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

Keywords: computer-aided diagnosis; glioblastoma; magnetic resonance imaging; texture analysis.

Figure 1

Patients. RIS, radiology information system; MRI, magnetic resonance imaging.

Figure 2

Axial contrast-enhanced T1-weighted image of a 56-year-old patient with pathologically proven glioblastoma (A) and the region of interest (red) overlapping the peritumoral area (B) on a postcontrast T1-weighted image, which was consequentially transferred on to a synchronized slice on the T2-weighted sequence (C).

Figure 3

Comparison of receiver operating characteristic (ROC) curves between (A) the six texture parameters that showed the highest area under the curve, and (B) independent parameters and the predictive model for the diagnosis of high-grade gliomas. Mean, histogram mean; Perc01/50, 1%/50% percentile; ShrtREmp, short-run emphasis; LngREmph, long-run emphasis; WavEn, wavelet energy.

Figure 4

Axial T2-weighted image of a 61-year-old patient with glioblastoma (A) and the region of interest (red) used for texture analysis; (B) generated map based on short-run emphasis parameter (blue arrow pointing to the peritumoral zone); (C) generated map based on long-run emphasis parameter (orange arrow pointing to the peritumoral zone; axial T2-weighted image of a 68-year-old patient with brain metastases (D) and the region of interest (red) used for texture analysis; (E) generated map based on short-run emphasis parameter (blue arrow pointing to the peritumoral zone); (F) generated map based on long-run emphasis parameter (orange arrow pointing to the peritumoral zone.

Figure 5

Axial T2-weighted image of a 72-year-old patient with glioblastoma (A) and the region of interest (green) used for texture analysis; (B) five-level wavelet decomposition diagram; (C) five-level wavelet decomposition of (A). The numbers represent the decomposition levels. Frequency bands are noted: LL, low–low; HL, high–low; LH, low–high; HH, high–high.