Keywords: Tumors, Machine Learning/Artificial IntelligenceMeningiomas are the most common primary extra-axial intracranial tumors in adults. Grade of meningioma helps to predict the patient prognosis. Sixty-two patients with preoperative MRI were included in this IRB approved study. The whole tumor volumes were segmented from FLAIR, followed by co-registration onto SWI. A pretrained convolutional neural network (CNN) was employed to classify meningiomas into high and low-grades based on SWI, CE-T1W and FLAIR MRI. The pretrained CNN with data augmentation resulted in an accuracy of 80.2% (sensitivity=82.6% and specificity=78.1%) for identifying grades in meningiomas.
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