Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain.

Background: Gliomas are frequent tumors of brain parenchyma, which have histology similar to that of glial cells. Accurate glioma grading is required for determining clinical management. The background of this study is to investigate the accuracy of magnetic resonance imaging (MRI)-based radiomic features extracted from multiple MRI sequences in differentiating low and high-grade gliomas. Materials and Methods: This is a retrospective study. It includes two groups. Group A includes patients with confirmed histopathological diagnosis of low (23) and high-grade (58) gliomas from 2012 to 2020 were included. The MRI images were acquired using a Signa HDxt 1.5 Tesla MRI (GE Healthcare, Milwaukee, USA). Group B includes an external test set consisting of low- (20) and high-grade gliomas (20) obtained from The Cancer Genome Atlas (TCGA). The radiomic features were extracted from axial T2, apparent diffusion coefficient map, axial T2 fluid-attenuated inversion recovery, and axial T1 post-contrast sequences for both the groups. The Mann - Whitney U test was performed to assess the significant radiomic features useful for distinguishing the glioma grades for Group A. To determine the accuracy of radiomic features for differentiating gliomas, AUC was calculated from receiver operating characteristic curve analysis for both groups. Results: Our study noticed in Group A, fourteen MRI-based radiomic features from four MRI sequences showed a significant difference (p < 0.001) in differentiating gliomas. In Group A, we noticed T1 post-contrast radiomic features such as first-order variance (FOV) (sensitivity - 94.56%, specificity - 97.51%, AUC - 0.969) and GLRLM long-run gray-level emphasis (sensitivity - 97.54%), specificity - 96.53%, AUC - 0.972) had the highest discriminative power for distinguishing the histological subtypes of gliomas. Our study noticed no statistical significant difference between ROC curves of significant radiomic features for both groups. In Group B, the T1 post-contrast radiomic features such as FOV (AUC-0.933) and GLRLM long-run gray-level emphasis (AUC-0.981) had also shown high discriminative power for distinguishing the gliomas. Conclusion: Our study concludes that MRI-based radiomic features extracted from multiple MRI sequences provide a non-invasive diagnosis of low- and high-grade gliomas and can be implemented in clinical settings for diagnosing the glioma grades.

Magnetic resonance imaging spectrum of perinatal hypoxic-ischemic brain injury.

Perinatal hypoxic-ischemic brain injury results in neonatal hypoxic-ischemic encephalopathy and serious long-term neurodevelopmental sequelae. Magnetic resonance imaging (MRI) of the brain is an ideal and safe imaging modality for suspected hypoxic-ischemic injury. The pattern of injury depends on brain maturity at the time of insult, severity of hypotension, and duration of insult. Time of imaging after the insult influences the imaging findings. Mild to moderate hypoperfusion results in germinal matrix hemorrhages and periventricular leukomalacia in preterm neonates and parasagittal watershed territory infarcts in full-term neonates. Severe insult preferentially damages the deep gray matter in both term and preterm infants. However, associated frequent perirolandic injury is seen in term neonates. MRI is useful in establishing the clinical diagnosis, assessing the severity of injury, and thereby prognosticating the outcome. Familiarity with imaging spectrum and insight into factors affecting the injury will enlighten the radiologist to provide an appropriate diagnosis.