Lee Joonsang, Narang Shivali, Martinez Juan J, Rao Ganesh, Rao Arvind
University of Texas , MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, 1515 Holcombe Boulevard, Houston, Texas 77030, United States.
University of Texas , MD Anderson Cancer Center, Department of Neurosurgery, 1515 Holcombe Boulevard, Houston, Texas 77030, United States.
J Med Imaging (Bellingham). 2015 Oct;2(4):041006. doi: 10.1117/1.JMI.2.4.041006. Epub 2015 Aug 25.
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.
我们使用肿瘤区域内栖息地多样性的各种测量方法,分析了肿瘤栖息地的空间多样性,即具有明显不同强度特征的肿瘤区域。然后,这些特征被用于研究与胶质母细胞瘤(GBM)患者12个月生存状态的关联,并用于识别表皮生长因子受体(EGFR)驱动的肿瘤。本研究分析了65例GBM患者的T1增强和T2液体衰减反转恢复图像。基于感兴趣区域内的像素丰度,共获得了36个空间多样性特征。使用受试者工作特征(ROC)分析评估了两个分类任务中的性能。对于与12个月总生存的关联,ROC曲线下面积为0.74,置信区间为[0.630至0.858]。ROC上最佳操作点([公式:见正文])处的敏感性和特异性分别为0.59和0.75。对于识别EGFR驱动的肿瘤,ROC曲线下面积(AUC)为0.85,置信区间为[0.750至0.945]。ROC上最佳操作点([公式:见正文])处的敏感性和特异性分别为0.76和0.83。我们的研究结果表明,这些空间栖息地多样性特征与这些临床特征相关,并且可能是GBM患者磁共振成像研究中一种有用的预后工具。