Meningioma typing model construction using radiomics-based multi-parameter magnetic resonance imaging

Abstract Purpose: To investigate the value of a cli-radiomics model based on multi-parameter magnetic resonance imaging (MRI) in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas.Methods: Clinical, imaging, and postoperative pathological data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n=296) and validation (n=127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic (ROC) curves, ACC, SEN, and SPE were drawn to evaluate the performance. The optimal radiomics model was selected, calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors.Results: There were thirteen radiomic features selected from T1C and T2WI after dimensionality reduction. The prediction performance of RF radiomics model is slightly lower than that of the cli-radiomics model. The area under the curve (AUC), SEN, SPE, and ACC of the cli-radiomics model training set are 0.836 (95% confidence interval [CI], 0.795-0.878), 0.922, 0.583, and 0.686; the AUC, SEN, SPE, and ACC of the validation set were 0.756 (95% CI, 0.660-0.846), 0.816, 0.596, and 0.661, respectively.Conclusion: The diagnostic efficacy of the cli-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone, and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.