A fusion model based on preoperative MRI radiomics features can predict potential bone invasion of meningiomas

Motivation: Bone invasion is a common problem in meningioma surgery and is associated with patient prognosis. However, 10-26% of patients with potential bone invasion are difficult to identify by preoperative imaging. Goal(s): To develop an artificial intelligence based preoperative diagnostic model. Approach: Radiomics features were extracted from preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) MR images of 296 patients. Candidate radiomics were selected by applying feature reduction and 5-fold cross validation. Results: A more accurate and robust fusion radiomics model was built based on T1C and T2 MR images with AUC of 0.755. Impact: Our results have demonstrated that radiomics features extracted from T1C and T2 MR images may be employed as effective preoperative biomarkers for predicting potential bone invasion in meningiomas.