A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome
A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome
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DOI：
10.1259/bjr.20160575
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发表时间：
2016-01-01
影响因子：
2.6
通讯作者：
Tanougast, Camel
中科院分区：
文献类型：
--
作者：
Chaddad, Ahmad;Desrosiers, Christian;Tanougast, Camel
目的：预测多形性胶质母细胞瘤(GBM)患者的生存结局是临床医生选择最佳疗程的关键。本研究的目的是评价从MR图像中提取的几何形状特征作为一种潜在的非侵入性方法在确定GBM肿瘤特征和预测GBM患者总体生存时间方面的作用。方法：40例GBM患者的数据来自癌症基因组图谱和癌症影像档案。患者的T-1加权增强后和液体衰减的反转-恢复体积被共同记录并划分为对应于三种基底膜表型的区域：坏死型、活动性肿瘤和水肿型/侵袭型。然后从每个表型区域分片提取一组二维形状特征，并在切片上组合来描述这些表型的三维形状。此后，使用Kruskal-Wallis检验来鉴定不同表型间具有显著不同分布的形状特征。此外，还进行了Kaplan-Meier分析，以找出与GBM存活率密切相关的特征。结果：我们使用Kruskal-Wallis检验的分析表明，除了一个形状特征外，所有的形状特征在不同的表型之间都有统计学意义的差异，经Holm-Bonferroni校正后的p值和0.05，证明了基于每个表型的基底膜肿瘤形状的分析是正确的。此外，基于Kaplan-Meier估计值的生存分析确定了来自坏死区的三个特征(即偏心率、范围和坚固性)，它们与总体生存显著相关(校正p值，0.05；风险比在1.68和1.87之间)。在多因素分析中，坏死区的特征对患者存活组的预测准确率最高，受试者-操作特征曲线(AUC)下的平均面积为63.85%。结论：形态特征，尤其是从坏死区提取的形态特征，可以有效地用于确定基底膜肿瘤的特征，并预测基底膜患者的总体生存。知识进展：简单的体积特征在很大程度上被用来表征基底膜肿瘤的不同表型(即活动性肿瘤、水肿性和坏死性)。这项研究扩展了先前的工作，考虑了从不同表型中提取的广泛的形状特征，用于预测GBM患者的生存。
Objective: Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM.Methods: The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T-1 weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal-Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan-Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM.Results: Our analysis using the Kruskal-Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm-Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan-Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value, 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival.Conclusion: Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM.Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM.