Liu Xianwang, Han Tao, Wang Yuzhu, Ke Xiaoai, Xue Caiqiang, Deng Juan, Li Shenglin, Sun Qiu, Liu Hong, Zhou Junlin
Radiology of Department, Lanzhou University Second Hospital, Lanzhou, 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.
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.
World Neurosurg. 2023 Sep;177:e446-e452. doi: 10.1016/j.wneu.2023.06.073. Epub 2023 Jun 24.
To investigate the possibility of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating microcystic meningioma (MM) from intracranial solitary fibrous tumor (SFT).
Eighteen patients with MM and 23 patients with SFT were enrolled in this retrospective study. Conventional magnetic resonance imaging (MRI) features and 9 ADC histogram parameters (including mean, first (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentiles ADC, as well as variance, skewness, and kurtosis) between MM and SFT were compared. The diagnostic performance of the optimal parameter was determined by the receiver operating characteristic analysis.
SFT showed a significantly lower mean, ADC1, ADC10, ADC50, ADC90, and ADC99 than MM (all P < 0.05), while no significant difference was found in conventional MRI features or other ADC histogram parameters (all P > 0.05). ADC1 was identified as the optimal parameter in differentiating between MM and SFT, which achieved an area under the curve of 0.861, with sensitivity, specificity, and accuracy of 78.26%, 88.89%, and 82.93%, respectively.
MM and SFT show overlapping conventional MRI features. ADC histogram analysis helps to differentiate between MM and SFT, with ADC1 being the optimal parameter with the best discrimination performance.
探讨表观扩散系数(ADC)图的直方图分析在鉴别微囊性脑膜瘤(MM)与颅内孤立性纤维瘤(SFT)中的应用可能性。
本回顾性研究纳入了18例MM患者和23例SFT患者。比较了MM和SFT之间的常规磁共振成像(MRI)特征以及9个ADC直方图参数(包括均值、第1百分位数(ADC1)、第10百分位数(ADC10)、第50百分位数(ADC50)、第90百分位数(ADC90)、第99百分位数(ADC99)的ADC,以及方差、偏度和峰度)。通过受试者工作特征分析确定最佳参数的诊断性能。
SFT的均值、ADC1、ADC10、ADC50、ADC90和ADC99均显著低于MM(均P < 0.05),而常规MRI特征或其他ADC直方图参数无显著差异(均P > 0.05)。ADC1被确定为鉴别MM和SFT的最佳参数,其曲线下面积为0.861,灵敏度、特异度和准确度分别为78.26%、88.89%和82.93%。
MM和SFT的常规MRI特征存在重叠。ADC直方图分析有助于鉴别MM和SFT,其中ADC1是具有最佳鉴别性能的最佳参数。