Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation
发表日期： 2015.10.25 来源：J Med Imaging (Bellingham). 2015 Oct;2(4):041006.
Lee J11, Narang S11, Martinez JJ22, Rao G22, Rao A11.
1. University of Texas , MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, 1515 Holcombe Boulevard, Houston, Texas 77030, United States.
2. University of Texas , MD Anderson Cancer Center, Department of Neurosurgery, 1515 Holcombe Boulevard, Houston, Texas 77030, United States.
我们使用肿瘤发生区域内的环境多样性的多种测量方法，分析了具有明显不同的肿瘤强度特征的肿瘤，其发生环境、区域的空间多样性。然后将这些特征用于研究胶质母细胞瘤（GBM）患者的12个月生存状态和表皮生长因子受体（EGFR）驱动的肿瘤的识别之间的关联。本研究分析了65例胶质母细胞瘤患者的T1增强后和T2液体衰减反转恢复图像。基于感兴趣区域内的像素丰度，总共获得了36个空间多样性特征。使用受试者操作特性（ROC）分析评估了两者在分类任务中的性能。对于与12个月总体生存之间的关联性，ROC曲线下面积为0.74，置信区间为[0.630-0.858]。 ROC最佳受试点（阈值= 0.5）的灵敏度和特异度分别为0.59和0.75。而对于EGFR驱动的肿瘤的识别，ROC曲线下面积（AUC）为0.85，置信区间为[0.750-0.945]。ROC最佳受试点（阈值= 0.166）的灵敏度和特异度分别为0.76和0.83。我们的研究结果表明，空间环境多样性特征与一些临床特征相关，可作为胶质母细胞瘤患者磁共振成像研究中一种有用的预后工具。
We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.
glioblastoma; imaging-genomics; radiomics; spatial diversity; tumor habitats