Machine learning based MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma

Motivation: Prediction radiomics analysis of postoperative progressive cerebral edema and hemorrhage which are the most common complications after meningioma resection, is limited. Goal(s): To develop and validate a machine learning model to predict progressive cerebral edema and hemorrhage after meningioma resection. Approach: Reviewing the preoperative MRI of 148 pathology-confirmed meningiomas, extracting radiomics features of tumor enhancement and peritumoral edema regions, and combining clinical characteristics to build machine learning multiparametric MRI radiomics predictive models. Results: The combining model including both enhancement and edema radiomics features, and clinical characteristics including systolic blood pressure, showed the best predictive performance with AUC of 0.94 for the validation set. Impact: We proposed a novel model that included clinical indicators and multi-parameter radiomics features, which can accurately and non-invasively predict progressive cerebral edema and hemorrhage after meningioma resection, enabling improving clinical management and quality of life of patients with meningioma.