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One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor or cyst texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lowergrade gliomas; and 2)a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation ofthe classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers.

Endovascular thrombectomy has proven efficacy for a wide range of patients with large-vessel occlusion stroke and in selected cases up to 24 hours from onset. While primary stroke centers have increased the proportion of patients withstroke receiving thrombolytic therapy, delays can be encountereduntil patients with LVO are identified and transferred from the primary stroke center to acomprehensive stroke center. Therefore, any extra steps need to be carefullyweighed. The use of CTA (especially multiphase) at the primary stroke center levelhas many advantages in expediting the transfer of appropriate patients to a comprehensive center.