Noninvasive prediction of meningioma brain invasion via multiparametric MRI⁃based brain⁃tumor interface radiomics

Abstract

Objective To develop and validate a preoperative prediction model for meningioma brain invasion using radiomics features derived from multiparametric magnetic resonance imaging (MRI)⁃based brain⁃tumor interface (BTI). Methods A total of 656 meningioma patients diagnosed and treated were included at The First Affiliated Hospital of Nanjing Medical University from September 2014 to April 2023. Using stratified random sampling, patients were randomly divided in a 4∶1 ratio into training set (524 cases) and testing set (132 cases). The training set was used for model construction and optimization, and the testing set for evaluating generalization ability. All patients underwent preoperative MRI examination including axial T1WI, enhanced T1WI and T2WI. After image preprocessing and segmentation, the meningioma region of interest was identified, and BTI with thicknesses of 0.80, 1.00 and 1.20 cm were constructed. Radiomics features were extracted from the regions of interest (ROI) across the 3 sequences. Following single⁃value elimination and interclass correlation coefficient [ICC (2, k) > 0.90] stability screening, features were selected using five⁃fold cross⁃validated least absolute shrinkage and selection operator (LASSOCV). Six machine learning (ML) algorithms, including light gradient boosting machine (LightGBM), Logistic regression (LR), multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) were utilized to build predictive models. The performance of each model was assessed using receiver operating characteristic (ROC) curve and the area under the curve (AUC). The significance of differences between ROC curves were compared using the Delong test. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the models across different threshold probabilities. Results Among the 656 meningioma patients, 152 cases (23.17%) exhibited brain invasion, with 123 cases (23.47%) in the training set and 29 cases (21.97%) in the testing set. Through five⁃fold cross⁃validation in the training set and evaluation in the testing set, comparative analysis of the predictive performance of 18 model⁃thickness combinations (6 ML algorithms × 3 BTI thicknesses) showed that the XGBoost model constructed with a 1.00 cm BTI thickness demonstrated exceptional performance. This model achieved an AUC of 0.913 (95%CI: 0.886-0.937, P = 0.000), accuracy of 0.86, sensitivity of 0.77, and specificity of 0.88 in the training set; and an AUC of 0.897 (95%CI: 0.821- 0.961, P = 0.000), accuracy of 0.90, sensitivity of 0.72, and specificity of 0.95 in the testing set. Further Delong test showed that this model's AUC was significantly higher than all other models (P < 0.05, for all). DCA showed that this model demonstrated the best clinical utility with the highest net benefit area in both the training set (0.087) and the testing set (0.094). Conclusions The XGBoost model based on 1.00 cm BTI exhibited outstanding predictive performance, providing an accurate and reliable non⁃invasive method for preoperative evaluation of meningioma brain invasion. This method offers substantial clinical utility in facilitating personalized surgical planning, risk assessment, and prognosis evaluation.

doi：10.3969/j.issn.1672⁃6731.2025.03.003