标题： 基于放射影像的脑肿瘤统计形状分析 显示英文标题

标题： Radiologic Image-based Statistical Shape Analysis of Brain Tumors

摘要： 我们提出了一种基于曲线的黎曼几何方法，用于对从放射影像中获得的肿瘤进行一般形态统计分析。 该框架的关键组成部分是一种合适的度量，它（1）能够比较肿瘤形状，（2）提供工具以计算描述性统计和在肿瘤形状空间上实施主成分分析，以及（3）允许肿瘤形状进行一类丰富的连续变形。 通过在一组被诊断为多形性胶质母细胞瘤患者的放射影像数据集上进行的具体统计任务，展示了该框架的实用性。这种恶性脑肿瘤预后不良。 特别是，我们的分析发现了两个具有非常不同生存率、亚型和基因组特征的患者群组。 此外，研究表明，在包含临床和基因组变量的生存模型中加入肿瘤形状信息，可以显著提高预测能力。 显示英文摘要

摘要： We propose a curve-based Riemannian-geometric approach for general shape-based statistical analyses of tumors obtained from radiologic images. A key component of the framework is a suitable metric that (1) enables comparisons of tumor shapes, (2) provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumor shapes, and (3) allows for a rich class of continuous deformations of a tumor shape. The utility of the framework is illustrated through specific statistical tasks on a dataset of radiologic images of patients diagnosed with glioblastoma multiforme, a malignant brain tumor with poor prognosis. In particular, our analysis discovers two patient clusters with very different survival, subtype and genomic characteristics. Furthermore, it is demonstrated that adding tumor shape information into survival models containing clinical and genomic variables results in a significant increase in predictive power.