Garzón Benjamín, Emblem Kyrre E, Mouridsen Kim, Nedregaard Baard, Due-Tønnessen Paulina, Nome Terje, Hald John K, Bjørnerud Atle, Håberg Asta K, Kvinnsland Yngve
Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway.
Acta Radiol. 2011 Nov 1;52(9):1052-60. doi: 10.1258/ar.2011.100510. Epub 2011 Oct 3.
A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking.
To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients.
T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression.
Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001).
Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients.
目前缺乏对用于胶质瘤诊断的磁共振成像（MRI）选项的系统比较。
研究多种磁共振衍生图像特征在胶质瘤患者肿瘤分级诊断准确性和生存预测方面的情况。
对74例经组织学证实分级的胶质瘤患者进行了T1加权增强前后、T2加权和动态磁敏感对比扫描。对于每位患者，在包含肿瘤体积的感兴趣区域内，从原始图像导出的参数图中获取一组统计特征。采用向前逐步选择程序，通过交叉验证的逻辑模型寻找用于分级预测的最佳特征组合，并通过Cox比例风险回归寻找用于生存时间预测的最佳特征组合。
强化的有无与首过曲线一阶矩（FM）的峰度相结合是最能预测肿瘤分级的特征组合（II级与III-IV级；中位AUC = 0.96），主要贡献来自第一个特征。排除IV级肿瘤时，预测价值较低（中位AUC = 0.82）。单独强化的有无是生存时间的最佳预测指标，且回归具有显著性（P < 0.0001）。
强化的有无反映了跨内皮渗漏，是胶质瘤患者分级和生存时间预测价值最高的特征。
