Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis

• 成果类型: Article
• 署名作者: Crawford, Lorin; Monod, Anthea; Chen, Andrew X.; Mukherjee, Sayan; Rabadan, Raul
• 署名单位: Brown University; Brown University; Brown University; Tel Aviv University; Columbia University; Duke University; Duke University; Duke University; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1671198
• 发表日期: 2020
• 页码: 1139-1150
• 关键词: survival; brain; cancer; oncology; models; SPACE

摘要

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.