Keywords: Tumors, Machine Learning/Artificial IntelligenceThis study evaluated the feasibility of noninvasive preoperative differentiation fibroblastic meningioma (FM) from non-fibroblastic meningiomas (nFM) based a clini-radiomics model. A total of thirteen radiomics features were included after Selectpercentile and Lasso feature screening. Our results showed that Random forest is the most efficient among the six radiomics models in differentiating FM from nFM, and the diagnostic efficacy of clini-radiomics models in training and validation group is further improved. Therefore, we believe that the clini-radiomics model is of great value in noninvasive preoperative differentiation of FM and nFM and contributes to the selection of individualized treatment options for meningioma patients.
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