Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth. Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth.

翻译：目的：升主动脉瘤生长预测在临床上仍具挑战性。本研究评估并比较了局部与全局形状特征预测升主动脉瘤生长的能力。材料与方法：研究纳入了70名动脉瘤患者，每例均具有两次3D影像采集数据。分割完成后，计算三项局部形状特征：(1) 升主动脉中心线最大直径与长度之比，(2) 升主动脉外缘线与内缘线长度之比，(3) 升主动脉段迂曲度。通过利用纵向数据推导动脉瘤生长速率。采用径向基函数网格变形技术构建等拓扑表面网格。通过无监督主成分分析（PCA）和有监督偏最小二乘法（PLS）进行统计形状分析，识别出两类全局形状特征：三个PCA导出的形状模态和三个PLS导出的形状模态。建立三种回归模型进行生长预测：两种基于高斯支持向量机（分别采用局部特征和PCA全局特征），第三种为基于PLS全局特征的线性回归模型。评估预测结果并识别最易生长的主动脉形态。结果：留一法交叉验证的预测均方根误差分别为：局部特征0.112毫米/月，PCA特征0.083毫米/月，PLS特征0.066毫米/月。初始直径较大且靠近根部的动脉瘤生长更快。结论：全局形状特征可能为预测动脉瘤生长提供重要依据。