Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis

Abstract

Purpose: To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features.

Methods: This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T₁-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL.

Results: Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM.

Conclusions: Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.

Keywords: glioblastoma; magnetic resonance imaging; primary central nervous system lymphoma; texture analysis.

Fig. 1.

Flow chart of subject enrollment. GBM, glioblastoma; CNS, central nervous system; PCNSL, primary central nervous system lymphoma.

Fig. 2.

Pretreatment post-contrast T₁-weighted image of a 68-year-old man subsequently diagnosed with glioblastoma. Post-contrast T₁-weighted image shows a heterogeneously enhanced tumor in the left medial temporal lobe. A rectangular region of interest is placed on the tumor.

Fig. 3.

The box-whisker plot of the intraclass correlation coefficients. The box indicates interquartile range, and the whiskers indicate range, excluding outliers. The circle represents an outlier, defined as having a distance greater than 1.5-times the interquartile range below the first quartile or above the third quartile. The horizontal line in the box represents the median.

Fig. 4.

Hierarchical clustering between the texture features and the cases. The heatmap presentation with dendrograms shows how a texture feature expresses itself among the cases. Red indicates that the texture feature demonstrates a large positive z-score and green indicates that the texture feature demonstrates a large negative z-score. Each row of the heat map represents a specific texture feature across patients, and each column represents all features for a tumor. GBM, glioblastoma; LYM, primary central nervous system lymphoma.