Abstract
BACKGROUND: Postoperative progressive cerebral edema and hemorrhage (PPCEH) are major complications after meningioma resection, yet their preoperative predictive studies are limited. The aim is to develop and validate a multiparametric MRI machine learning model to predict PPCEH after meningioma resection. METHODS: This retrospective study included 148 patients with meningioma. A stratified three-fold cross-validation was used to split the dataset into training and validation sets. Radiomics features from the tumor enhancement (TE) and peritumoral brain edema (PTBE) regions were extracted from T1WI, T2WI, and ADC maps. Support vector machine constructed different radiomics models, and logistic regression explored clinical risk factors. Prediction models, integrating clinical and radiomics features, were evaluated using the area under the curve (AUC), visualized in a nomogram. RESULTS: The radiomics model based on TE and PTBE regions (training set mean AUC: 0.85 (95% CI: 0.78-0.93), validation set mean AUC: 0.77 (95%CI: 0.63-0.90)) outperformed the model with TE region solely (training set mean AUC: 0.83 (95% CI: 0.76-0.91), validation set mean AUC: 0.73 (95% CI: 0.58-0.87)). Furthermore, the combined model incorporating radiomics features, and clinical features of preoperative peritumoral edema and tumor boundary adhesion, had the best predictive performance, with AUC values of 0.87 (95% CI: 0.80-0.94) and 0.84 (95% CI: 0.72-0.95) for the training and validation set. CONCLUSIONS: We developed a novel model based on clinical characteristics and multiparametric radiomics features derived from TE and PTBE regions, which can accurately and non-invasively predict PPCEH after meningioma resection. Additionally, our findings suggest the crucial role of PTBE radiomics features in understanding the potential mechanisms of PPCEH.