Department of Radiology, Xinqiao Hospital, Army Medical University, Chong Qing 400037, People's Republic of China.
Department of Neurosurgery, Xinqiao Hospital, Army Medical University, Chong Qing, People's Republic of China.
Acad Radiol. 2021 May;28(5):e137-e146. doi: 10.1016/j.acra.2020.03.035. Epub 2020 May 13.
To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading.
Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance.
The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (α) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between α and the tumor grades (p < 0.01). The parameter of α yielded a diagnostic accuracy of 85.3%, the combination of MK and α yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and α were more accurate (94.2%) in predicting tumor grade.
The most accurate parameters were rCBV, MK, and α in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.
探讨多参数磁共振成像（MRI）在胶质瘤分级中的应用价值。
回顾性分析 70 例经病理证实的胶质瘤患者的常规 MRI、动态磁敏感对比增强、多扩散加权成像信号模型（包括单指数、双指数、拉伸指数和扩散峰度成像）资料。采用单因素方差分析和独立样本 t 检验比较低级别和高级别胶质瘤以及所有级别胶质瘤之间的 MRI 参数值。采用受试者工作特征曲线分析、Spearman 相关分析和二元逻辑回归分析评估其诊断效能。
标准化相对脑血容量（rCBV）（2.240ml/100g）、平均峰度（MK）（0.471）和水分子扩散异质性指数（α）（1.064）在胶质瘤分级中的诊断效能最佳（最佳阈值、受试者工作特征曲线下面积、敏感度和特异度分别为 2.240ml/100g、0.844、87.8%和 75.9%、0.471、0.873、92.7%和 79.3%、1.064、0.847、79.3%和 78.0%）。rCBV 和 MK 与肿瘤分级呈正相关，α与肿瘤分级呈负相关（p<0.01）。α 参数的诊断准确率为 85.3%，MK 和α参数联合的诊断准确率为 89.7%，而 rCBV、MK 和α参数联合的诊断准确率更高（94.2%）。
在动态磁敏感对比增强、扩散峰度成像和多 b 值扩散加权成像中，rCBV、MK 和α是胶质瘤分级最准确的参数。多参数 MRI 可提高胶质瘤分级的准确性。
