Identifying NF-2 Mutations in Meningiomas Based on Susceptibility Weighted Imaging for Patient Prognosis Using Machine Learning

Motivation: Molecular markers, like neurofibromatosis type-2 (NF-2) mutations, highly impact patient outcomes in meningiomas, but they could only be assessed in excised tissue. Goal(s): To develop a non-invasive approach for preoperatively identifying NF-2 mutations using susceptibility-weighted MRI (SWI) with radiomics and deep learning. Approach: Preoperative SWI of 92 meningiomas with NF-2 status data were analyzed. Radiomics and deep learning were used to extract features of SWI, which were classified using traditional machine learning. Results: Reduced tumor signal intensity, "en plaque" growth pattern, and intratumoral calcification were markers of NF2 mutation, which was identified with an accuracy of 74%. Impact: This study employed SWI to predict NF-2 mutation through radiomics and deep learning features with 74% accuracy. Preoperative identification of NF-2 mutations might allow for personalized treatment planning resulting in better patient outcomes.