Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis
Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis
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DOI：
10.1080/01621459.2019.1671198
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发表时间：
2019-10-17
影响因子：
3.7
通讯作者：
Rabadan, Raul
中科院分区：
文献类型：
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作者：
Crawford, Lorin;Monod, Anthea;Rabadan, Raul
多形性胶质母细胞瘤（GBM）是一种侵袭性的人类脑癌，在癌症生物学领域正在积极研究。它的快速发展和获得分子数据的相对时间成本使得其他容易获得的数据形式，如图像，成为患者可行措施的重要资源。我们的目标是在统计设置中使用从GBM患者拍摄的医学图像所提供的信息。为了做到这一点，我们设计了一个新的统计数据？光滑欧拉特征变换（SECT）？量化肿瘤的磁共振图像。由于其定义良好的内积结构，SECT可以用于更广泛的功能和非参数建模方法比其他先前提出的拓扑汇总统计。当应用于GBM患者队列时，我们发现SECT比现有的肿瘤形状定量和常见的分子检测更能预测临床结局。具体来说，我们证明，SECT功能单独解释更多的GBM患者生存的差异比基因表达，体积功能，形态特征。因此，我们的研究结果的主要结论是两方面的。首先，他们认为图像包含有价值的信息，可以在临床预后和其他医疗决策中发挥重要作用。其次，他们表明，SECT是一个可行的工具，更广泛的研究医学影像信息学。包括可用于复制作品的材料的标准化描述，可作为在线补充。
Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under active study in the field of cancer biology. Its rapid progression and the relative time cost of obtaining molecular data make other readily available forms of data, such as images, an important resource for actionable measures in patients. Our goal is to use information given by medical images taken from GBM patients in statistical settings. To do this, we design a novel statistic?the smooth Euler characteristic transform (SECT)?that quantifies magnetic resonance images of tumors. Due to its well-defined inner product structure, the SECT can be used in a wider range of functional and nonparametric modeling approaches than other previously proposed topological summary statistics. When applied to a cohort of GBM patients, we find that the SECT is a better predictor of clinical outcomes than both existing tumor shape quantifications and common molecular assays. Specifically, we demonstrate that SECT features alone explain more of the variance in GBM patient survival than gene expression, volumetric features, and morphometric features. The main takeaways from our findings are thus 2-fold. First, they suggest that images contain valuable information that can play an important role in clinical prognosis and other medical decisions. Second, they show that the SECT is a viable tool for the broader study of medical imaging informatics. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.