AUTHOR=Yao Yuan , Xu Yifan , Liu Shihe , Xue Feng , Wang Bao , Qin Shanshan , Sun Xiubin , He Jingzhen 

TITLE=Predicting the grade of meningiomas by clinical–radiological features: A comparison of precontrast and postcontrast MRI

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1053089

DOI=10.3389/fonc.2022.1053089

ISSN=2234-943X

ABSTRACT=Objectives Postcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningioma. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aims to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI. 
Methods From January 2019 to October 2021, a total of 231 meningioma patients (development cohort: n=137, low-grade/high-grade: 85/52; external validation cohort: n=94, low-grade/high-grade: 60/34) were retrospectively included. Fourteen kinds of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomogram, based on full MRI and precontrast MRI, respectively. Their distinguishing performances were validated and compared by the external validation cohort.  
Results One demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, peritumoral edema) and one postcontrast MRI feature (absence of dural tail sign) were independent predictive factors for high-grade meningioma. The area under the ROC curve (AUC) values of the two distinguishing models (precontrast-postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898, and in the validation cohort were 0.922 and 0.878, respectively. DeLong’ s test showed no statistical difference between the AUCs of two distinguishing models (P=0.101). 
Conclusions An accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.