Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas

Abstract

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma.

Textural features of dynamic contrast‐enhanced MRI derived model‐free and model‐based parameter maps in glioma grading

“…Among these radiomic features, textural features are one of the most commonly used, which could quantify spatial variation of gray‐level intensity and characterize the underlying heterogeneity of the image under evaluation . Until recently, textural features, extracted from routine MR images, have successfully been associated with glioma grade, overall survival, and isocitrate dehydrogenase (IDH) genotype . However, dynamic contrast‐enhanced MRI (DCE‐MRI), which makes the heterogeneity of vascular physiology and structure quantifiable data and reveals dramatic imaging heterogeneity, was seldom analyzed by texture analysis.…”

“…16 Until recently, textural features, extracted from routine MR images, have successfully been associated with glioma grade, overall survival, and isocitrate dehydrogenase (IDH) genotype. [17][18][19][20] However, dynamic contrast-enhanced MRI (DCE-MRI), which makes the heterogeneity of vascular physiology and structure quantifiable data and reveals dramatic imaging heterogeneity, was seldom analyzed by texture analysis. However, heterogeneity of microvascular proliferation has been recognized as one of the most important signatures of glioma.…”

Reliability of 2D Magnetic Resonance Imaging Texture Analysis in Cerebral Gliomas: Influence of Slice Selection Bias on Reproducibility of Radiomic Features

“…We did not consider pixel rescaling because it was homogeneous in all patients. Third, we included only T2-weighted MRI images, because they are widely used in radiomic works (26,27). This study can be expanded using other sequences in future studies.…”

Pretreatment texture analysis of routine MR images and shape analysis of the diffusion tensor for prediction of volumetric response after radiosurgery for meningioma

“…Pre-GKRS histology results were available in only 6 of our 32 cases, so we could not draw any conclusions related to grading, subtypes, or their specific radiosensitivity. Another disadvantage of this retrospective study was the lack of a more sophisticated second-level tissue pattern analysis, such as the co-occurrence of anisotropic gradient orientations or calculation of Shannon entropy, 12,18 which was not available to us, or perfusion data, which we did not acquire during the pretreatment MR examination to assess tumor vascularity. 21…”