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

Abstract

11Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and 12 prognosis of patients. The purpose of this work was to develop a low cost and easy to 13 implement classification model which distinguishes low grade gliomas (LGGs) from high 14 grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. 15Different combinations between MRI contrasts (T 1Gd and T 2 ) and one segmented 16 glioma region (necrotic and non-enhancing tumor core (NCR/NET)) were studied. 17 Texture features obtained from the Gray Level Size Zone Matrix (GLSZM) were 18 calculated. An under-samplig method was proposed to divide the data into different 19 training subsets and subsequently extract complementary information for the creation 20 of distinct classification models. The sensitivity, specificity and accuracy of the models 21 were calculated. The best model was explicitly reported. The best model included only 22 three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 23 88.24% and 91.18% respectively. According to the features of the model, when the 24 NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in 25 the T 1Gd images and LGGs had a more heterogeneous texture than HGGs in the T 2 26 images. These novel results partially contrast with results from literature. The best 27 model proved to be useful for the classification of gliomas. Complementary results 28 showed that heterogeneity of gliomas depended on the studied MRI contrast. The 29 model presented stands out as a simple, low cost, easy to implement, reproducible and 30 highly accurate glioma classifier. What is more important, it should be accessible to 31 populations with reduced economic and scientific resources.32

Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients

“…12 A recent study used texture analysis with different combinations of MRI images on post-contrast T1-weighted images and T2-weighted images and found a sensitivity of 94% to differentiate low-grade gliomas from high-grade gliomas. 30 In our study, the maximum ADC of 2.026 or less in tumour ROI had a sensitivity of 95% to differentiate GBM from PCNSL and the ADC mean of 0.943 or less in the tumour region had a specificity of 91.2%. The sensitivity and specificity in our study were comparable to previous studies, 9,12,14 although there is deference in the cut-off values among the studies, which is likely to be due to the differences in MRI machine, software used, ROI selection technique and image analysis tools.…”

Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features

“…Texture analysis is a postprocessing method for extracting information by quantifying the spatial distribution of pixels or voxels with different gray intensities and counting the variables, that is, calculating and extracting texture features based on the texture matrix of images. This is one of the more commonly used methods in radiomics research [ 7 , 8 ]. The first-order feature describes the gray distribution of individual pixel values in ROIs.…”

Accuracy of magnetic resonance imaging diagnosis and grading of gliomas

“…Suárez-García et al from texture features obtained from the gray level size zone matrix calculated that the best model reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18% respectively, providing a simple, low cost, easy to implement, reproducible and highly accurate glioma classifier, accessible to populations with reduced economic and scientific resources. 12 Similarly, Nakamoto et al concluded that the grade III and IV glioma scan be accurately and easily predicted by radiomic analysis of contrast-enhanced T1 and T2 weighted images. 39 Addition of advanced sequences would have enriched this study by allowing assessment of their impact on the predictive accuracy of conventional MRI, but cost constraints limited it to conventional sequences of MRI only.…”