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

Abstract

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111.

A simple model for glioma grading based on texture analysis applied to conventional brain MRI

“…From the 394 interpretation of the features and the results described above, it could be deduced that 395 LGGs had a more heterogeneous texture than HGGs, specifically in the T 2 contrasts; 396 and HGGs had a more heterogeneous texture than LGGs, specifically in the T 1Gd 397 contrasts; in both cases studying the NCR/NET region. Several works have reported 398 models whose main classification variable was heterogeneity of gliomas [18,23,25,[47][48][49]. 399 For example, through texture analysis applied on diffusion tensor imaging [25,49] and 400 diffusion kurtosis imaging [49] maps, diverse features that characterized the 401 heterogeneity of gliomas indicated an increased heterogeneity for higher grade gliomas 402 compared to lower grade gliomas.…”

“…LGGs and HGGs of 94.4% and 94% respectively when T1-weighted before and 453 after contrast-enhanced images were studied, and 96.5% and 97% when they studied 454 T2-weighted and FLAIR images. Therefore, in this work conventional MRI (T 1Gd and 455 T 2 contrasts) was studied, while others have analyzed advanced MRI or a combination 456 of both [5,[21][22][23][24][51][52][53][54]. The model was created from a simple mathematical method (a 457 multiple linear regression), in comparison to others in which mathematical tools of 458 higher complexity were utilized [7,[52][53][54]].…”

MR textural analysis on T2 FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification

“…Recently, texture analysis, which extracts a large amount of quantitative features from medical images using data‐characterization algorithms, has enabled development of several quantitative descriptors that reflect texture variations in gray‐level patterns, pixel interrelationships, and the spectral properties of an image . These texture features may quantitatively complement the macro‐image information, and have been used by neuroradiologists . Studies on glioblastoma using MR textural analysis (MRTA) on postcontrast T 1 ‐weighted imaging (T 1 WI) and T 2 fluid‐attenuated inversion recovery (FLAIR) imaging have achieved good diagnostic performance …”

“…10 These texture features may quantitatively complement the macro-image information, and have been used by neuroradiologists. [10][11][12][13] Studies on glioblastoma using MR textural analysis (MRTA) on postcontrast T 1 -weighted imaging (T 1 WI) and T 2 fluidattenuated inversion recovery (FLAIR) imaging have achieved good diagnostic performance. 14 For these WHO-classified grade-II diffuse gliomas without disruption of the blood-brain barrier and contrast enhancement, the FLAIR sequence is recommended by the American National Comprehensive Cancer Network because it can provide the best delineation and volumetric measurements of the tumor in region of interest (ROI) placement or intraoperative navigation.…”

re: Reliability of 3D Texture Analysis: A Multicenter MRI Study of the Brain

“…The process provides information regarding statistical patterns between voxels that cannot be detected by a mere visual inspection of the image. Texture analysis has been used to analyze magnetic resonance imaging (MRI) data collected in numerous diseases, including multiple sclerosis, Parkinson's disease, and brain tumors, among others.…”