Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging

Purpose: To explore the clinical value of a clinical radiomics model nomogram based on magnetic resonance imaging (MRI) for preoperative meningioma grading. Materials and methods: We collected retrospectively 544 patients with pathological diagnosis of meningiomas were categorized into training (n = 380) and validation (n = 164) groups at the ratio of 7 : 3. There were 3,376 radiomics features extracted from T2WI and T1C by shukun technology platform after manual segmentation using an independent blind method by two radiologists. The Selectpercentile and Lasso are used to filter the most strongly correlated features. Random forest (RF) radiomics model and clinical radiomics model nomogram were constructed respectively. The calibration, discrimination, and clinical validity were evaluated by using the calibration curve and decision analysis curve (DCA). Results: The RF radiomics model based on T1C and T2WI was the most effective to predict meningioma grade before surgery among the six different classifiers. The predictive ability of clinical radiomics model was slightly higher than that of RF model alone. The AUC, SEN, SPE, and ACC of the training set were 0.949, 0.976, 0.785, and 0.826, and the AUC, SEN, SPE, and ACC of the validation set were 0.838, 0.829, 0.783, and 0.793, respectively. The calibration curve and Hosmer-Lemeshow test showed the predictive probability of the fusion model was similar to the actual differentiated LGM and HGM. The analysis of the decision curve showed that the clinical radiomics model could obtain the best clinical net profit. Conclusions: The clinical radiomics model nomogram based on T1C and T2WI has high accuracy and sensitivity for predicting meningioma grade.