Alis D, Bagcilar O, Senli YD, et al. - In this investigation involving 181 individuals, researchers examined the value of quantitative texture analysis of conventional MRI sequences utilizing artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG). They divided the cohort into training (n = 121) and test partitions (n = 60). Data reported that the ANN models utilizing texture data of T2W-FLAIR and contrast-enhanced T1W images achieved an area under the receiver operating characteristic curve (AUC) of 0.87 and 0.86, respectively, in the test cohort. It was noted that the combined ANN model with selected texture characteristics obtained the highest diagnostic accuracy equating 88.3% with an AUC of 0.92. ANN-enhanced quantitative texture analysis of T2W-FLAIR and contrast-enhanced T1W can accurately discriminate HGG from LGG and could be of clinical value in tailoring the management strategies in glioma patients.