Meningiomas are most often benign primary intracranial tumors that are frequently found incidentally on imaging. Larger sized meningiomas may present with symptoms such as seizures and headaches. Smaller meningiomas are commonly asymptomatic and usually observed with serial imaging. We present two female patients, both of whom were found to have very small left frontal meningiomas that marginated Broca’s area. The first patient in this case series experienced episodes resembling seizures which consisted of weakness, vision loss, and slurred speech, as well as subtle language dysfunction in her day-to-day conversations. The second patient presented with headaches and an enlarging meningioma. Both meningiomas were surgically resected and the patients’ symptoms resolved. Small meningiomas should not be overlooked as they may very well be the source of neurologic symptoms.

BackgroundBleb presence in intracranial aneurysms (IAs) is a known indication of instability and vulnerability.ObjectiveTo develop and evaluate predictive models of bleb development in IAs based on hemodynamics, geometry, anatomical location, and patient population.MethodsCross-sectional data (one time point) of 2395 IAs were used for training bleb formation models using machine learning (random forest, support vector machine, logistic regression, k-nearest neighbor, and bagging). Aneurysm hemodynamics and geometry were characterized using image-based computational fluid dynamics. A separate dataset with 266 aneurysms was used for model evaluation. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), true positive rate (TPR), false positive rate (FPR), precision, and balanced accuracy.ResultsThe final model retained 18 variables, including hemodynamic, geometrical, location, multiplicity, and morphology parameters, and patient population. Generally, strong and concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns together with larger, more elongated, and more distorted shapes were associated with bleb formation. The best performance on the validation set was achieved by the random forest model (AUC=0.82, TPR=91%, FPR=36%, misclassification error=27%).ConclusionsBased on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.