Akbari Hamed, Macyszyn Luke, Da Xiao, Wolf Ronald L, Bilello Michel, Verma Ragini, O'Rourke Donald M, Davatzikos Christos
From the Departments of Radiology (H.A., X.D., R.L.W., M.B., R.V., C.D.) and Neurosurgery (L.M., D.M.O.), University of Pennsylvania, 3600 Market St, Suite 380, Philadelphia, PA 19104.
Radiology. 2014 Nov;273(2):502-10. doi: 10.1148/radiol.14132458. Epub 2014 Jun 19.
To augment the analysis of dynamic susceptibility contrast material-enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma.
Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score.
The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score.
Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication.
加强对动态磁敏感对比剂增强磁共振（MR）图像的分析，以发现独特的组织特征，通过更好地了解胶质母细胞瘤患者的瘤周区域，可能有助于治疗方案的制定。
本研究获得机构审查委员会批准，对病历进行回顾性审查无需知情同意。获取了79例患者的动态磁敏感对比增强MR成像数据，并对灌注信号强度应用主成分分析。前六个主成分足以表征所有感兴趣区域血液灌注时间动态中超过99%的方差。随后，主成分与支持向量机分类器结合使用，以创建瘤周区域内的异质性图谱，该图谱的方差用作异质性评分。
计算得到的主成分能够实现可能被肿瘤高度浸润的组织与不太可能被肿瘤浸润的组织之间近乎完美的分离。使用主成分创建的异质性图谱显示，支持向量机判断为高度浸润的体素与随后的复发之间存在明确关系。结果表明，异质性评分与患者生存率之间存在显著相关性（r = 0.46，P <.0001）。根据异质性评分中位数，高异质性评分患者与低异质性评分患者之间的风险比为2.23（95%置信区间：1.4，3.6；P <.01）。
使用主成分分析对动态磁敏感对比增强MR成像数据进行分析，有助于识别后续可用于评估胶质母细胞瘤瘤周区域的成像变量。这些变量可能指示肿瘤浸润，并且可能成为指导治疗以及个体化预后的有用工具。
