Jiang Jun, Yu Juan, Liu Xiajing, Deng Kan, Zhuang Kaichao, Lin Fan, Luo Liangping
Department of Radiology, Health Science Center, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China.
Philips Healthcare, China International Center, Guangzhou, China.
Front Oncol. 2023 Jan 18;12:1100350. doi: 10.3389/fonc.2022.1100350. eCollection 2022.
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.
根据2021年世界卫生组织(WHO)中枢神经系统(CNS)指南分析脑膜瘤的术前MRI扫描,并分析MRI特征在诊断WHO分级和脑侵犯方面的效能。
回顾性分析2006年至2022年在我院接受MRI检查的675例脑膜瘤患者的数据,其中108例有脑侵犯。参照WHO中枢神经系统肿瘤分类指南(2021年第五版),分析17项特征,将年龄、性别和脑膜瘤MRI特征作为评估WHO分级和脑侵犯的危险因素。通过多变量逻辑回归分析确定危险因素,生成其预测WHO分级和脑侵犯的受试者操作特征(ROC)曲线,并计算曲线下面积(AUC)、敏感性和特异性。
单因素分析显示,性别、肿瘤大小、分叶征、瘤周水肿、血管流空、骨质侵犯、肿瘤-脑界面、指状突起和蘑菇征对诊断脑膜瘤WHO分级有意义,而这些特征和表观扩散系数(ADC)值对预测脑侵犯有意义(P<0.05)。多变量逻辑回归分析显示,分叶征、肿瘤-脑界面、指状突起、蘑菇征和骨质侵犯是诊断脑膜瘤WHO分级的独立危险因素,而上述特征、肿瘤大小和ADC值是诊断脑侵犯的独立危险因素(P<0.05)。肿瘤-脑界面在评估WHO分级和脑侵犯方面效能最高,AUC分别为0.779和0.860。综合这些变量,在确定WHO分级和脑侵犯方面的AUC分别为0.834和0.935。
术前MRI在诊断脑膜瘤WHO分级和脑侵犯方面表现出色,而肿瘤-脑界面是关键因素。脑膜瘤的术前MRI特征有助于预测WHO分级和脑侵犯,从而有利于病变完整切除并改善患者预后。