LAUSR

BACKGROUND The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic resonance imaging (MRI) and the tumor elasticity measured by intraoperative ultrasound elastography (IOUS-E) as a reference parameter. METHODS A retrospective analysis was performed on supratentorial meningiomas that were operated on between March 2018 and July 2020. Cases with IOUS-E studies were included. A semi-quantitative analysis of elastograms was used to define the meningioma consistency. MRIs were pre-processed before extracting radiomic features. Predictive models were built using a combination of feature selection filters and machine learning algorithms: logistic regression (LR), Naive Bayes, k-nearest neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). A stratified 5-fold cross-validation was performed. Then, models were evaluated using the area under the curve (AUC) and classification accuracy (CA). RESULTS Eighteen patients were available for analysis. Meningiomas were classified as hard or soft according to a mean tissue elasticity (MTE) threshold of 120. The best-ranked radiomic features were obtained from T1-weighted post-contrast (T1WC), Apparent Diffusion Coefficient (ADC) map, and T2-weighted (T2W) images. The combination of Information Gain and ReliefF filters with the NB algorithm resulted in an AUC of 0.961 and CA of 94%. CONCLUSION We have developed a high-precision classification model that is capable of predicting consistency of meningiomas based on the radiomic features in preoperative MRI (T2W, T1WC, and ADC map).