Preoperative Prediction of Meningioma Consistency Machine Learning-Based Radiomics.

Objectives: The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.

Methods: A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada's consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination.

Results: A total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts.

Conclusions: The presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.