Keywords: Tumors (Pre-Treatment), Machine Learning/Artificial Intelligence
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.
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