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This article, published in the Journal of Neurological Surgery, explores the use of machine learning and 3D MRI-based analysis to predict the pathologic grade of skull base meningiomas. The study analyzed preoperative MRIs from 38 patients and used a custom machine learning pipeline to predict the grade of the tumor. The results showed that the machine learning model had an area under the receiver operating characteristic curve (AUROC) of 0.75, indicating its potential for predicting the pathologic grade. However, further research with a larger sample size is needed to validate these findings and improve prognostication for patients with skull base meningiomas. [Extracted from the article]

This article discusses the development of a new technology called "TumorID" that uses machine learning and noncontact fluorescence spectroscopy to analyze meningioma tissue in real-time during surgery. The aim of this technology is to differentiate between Grade 1 and Grade 2 meningiomas, which have different prognostic and management implications. The study found that the multi-layer perceptron and logistic regression models performed the best in predicting the grade of meningiomas, with high accuracy. However, further research is needed to validate these findings on larger sample sizes. The authors hope that this technology will eventually improve the efficiency and accuracy of prognostication for patients with meningiomas. [Extracted from the article]