Keywords: Tumors, Machine Learning/Artificial IntelligenceThis study investigate the feasibility of a cli-radiomics model in preoperative noninvasive prediction of meningioma grade. This study attempted to construct a radiomics model for predicting meningioma grade based on T1C and T2WI sequences using different classifiers, select the optimal radiomics model, and combine it with clinical labels to construct a nomogram. Decision curve analysis was used to verify the clinical validity of the nomogram, which provides a non-invasive and convenient alternative method for clinicians to predict meningioma grades.
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