Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
Clin Radiol. 2022 Nov;77(11):864-869. doi: 10.1016/j.crad.2022.07.004. Epub 2022 Aug 25.
To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM).
Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours.
The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p<0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC = 0.852), when using 120.46 × 10 mm/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively.
Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter.
探讨全病变表观扩散系数（ADC）直方图分析在鉴别微囊型脑膜瘤（MCM）和非典型脑膜瘤（AM）中的价值。
回顾性分析 20 例 MCM 患者和 26 例 AM 患者的临床和术前 MRI 资料。对每位患者的病变进行全病变 ADC 直方图分析，获得直方图参数，包括均值、方差、偏度、峰度、ADCp1、ADCp10、ADCp50、ADCp90 和 ADCp99。比较两种肿瘤 ADC 直方图参数的差异，采用受试者工作特征（ROC）曲线评估有统计学意义的参数鉴别两种肿瘤的诊断性能。
MCM 的平均、ADCp1、ADCp10、ADCp50 和 ADCp90 值均大于 AM，MCM 与 AM 之间的这些参数存在显著差异（均 p<0.05）。ROC 分析显示，在鉴别两种肿瘤时，均值的曲线下面积（AUC）最高（AUC=0.852），当以 120.46×10 mm/s 作为最佳阈值时，鉴别两组的敏感度、特异度、准确度、阳性预测值和阴性预测值分别为 84.6%、75%、80.4%、81.5%和 78.9%。
基于全病变 ADC 图的直方图分析有助于术前鉴别 MCM 和 AM，其中均值是最有潜力的参数。
