The preoperative MRI scans of meningiomas were analyzed based on the 2021 World Health Organization (WHO) Central Nervous System (CNS) Guidelines, and the efficacy of MRI features in diagnosing WHO grades and brain invasion was analyzed.
The data of 675 patients with meningioma who underwent MRI in our hospital from 2006 to 2022, including 108 with brain invasion, were retrospectively analyzed. Referring to the WHO Guidelines for the Classification of Central Nervous System Tumors (Fifth Edition 2021), 17 features were analyzed, with age, sex and meningioma MRI features as risk factors for evaluating WHO grade and brain invasion. The risk factors were identified through multivariable logistic regression analysis, and their receiver operating characteristic (ROC) curves for predicting WHO grades and brain invasion were generated, and the area under the curve (AUC), sensitivity and specificity were calculated.
Univariate analysis showed that sex, tumor size, lobulated sign, peritumoral edema, vascular flow void, bone invasion, tumor-brain interface, finger-like protrusion and mushroom sign were significant for diagnosing meningioma WHO grades, while these features and ADC value were significant for predicting brain invasion (P < 0.05). Multivariable logistic regression analysis showed that the lobulated sign, tumor-brain interface, finger-like protrusion, mushroom sign and bone invasion were independent risk factors for diagnosing meningioma WHO grades, while the above features, tumor size and ADC value were independent risk factors for diagnosing brain invasion (P < 0.05). The tumor-brain interface had the highest efficacy in evaluating WHO grade and brain invasion, with AUCs of 0.779 and 0.860, respectively. Combined, the variables had AUCs of 0.834 and 0.935 for determining WHO grade and brain invasion, respectively.
Preoperative MRI has excellent performance in diagnosing meningioma WHO grade and brain invasion, while the tumor-brain interface serves as a key factor. The preoperative MRI characteristics of meningioma can help predict WHO grade and brain invasion, thus facilitating complete lesion resection and improving patient prognosis.