Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers

Abstract

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 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 semiquantitative analysis of elastograms was used to define the meningioma consistency. MRIs were preprocessed before extracting radiomic features. Predictive models were built using a combination of feature selection filters and machine learning algorithms: logistic regression, Naive Bayes, k-nearest neighbors, Random Forest, Support Vector Machine, and Neural Network. A stratified 5-fold cross-validation was performed. Then, models were evaluated using the area under the curve and classification accuracy.

Results: Eighteen patients were available for analysis. Meningiomas were classified as hard or soft according to a mean tissue elasticity threshold of 120. The best-ranked radiomic features were obtained from T1-weighted post-contrast, apparent diffusion coefficient map, and T2-weighted images. The combination of Information Gain and ReliefF filters with the Naive Bayes algorithm resulted in an area under the curve of 0.961 and classification accuracy of 94%.

Conclusions: We have developed a high-precision classification model that is capable of predicting consistency of meningiomas based on the radiomic features in preoperative magnetic resonance imaging (T2-weighted, T1-weighted post-contrast, and apparent diffusion coefficient map).

Keywords: Brain tumor; Elastography; Intraoperative ultrasound; MRI; Machine learning; Meningiomas.