Keywords: Diagnosis/Prediction, Radiomics, bone invasion,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.
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