腹主动脉瘤的预后:一种融合临床、形态、生物力学和纹理信息的机器学习方法

F. García-García, E. Metaxa, S. Christodoulidis, M. Anthimopoulos, N. Kontopodis, Martina Correa-Londono, T. Wyss, Y. Papaharilaou, C. Ioannou, H. Tengg-Kobligk, S. Mougiakakou
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引用次数: 2

摘要

通过个性化评估腹主动脉瘤(AAAs)的预期生长速度,可以实现对其进展的有效监测策略。考虑到影响AAA生长的各种因素,结合临床和形态测量数据以及机械应力特征,综合解决这个问题可能会有潜在的好处。此外,我们研究了在AAA囊内的计算机断层血管造影图像上使用纹理信息。一组n=38例患者接受了基线检查,并进行了随访,以最大直径(Dmax)除以经过的时间来测量AAA的生长速率。随后,与人口研究报告的预期增长率相比,将每个病例标记为缓慢、中等或快速增长,作为性别和基线Dmax的函数。我们总共计算了102个特征(5个临床特征、17个形态特征、4个生物力学特征和76个纹理特征),并使用了许多机器学习(ML)算法;目的是最小化错误分类的代价。采用留一交叉验证方案对系统的性能进行了评估。使用整个102维特征空间的决策树集成(LPBoost)方法获得的结果表明,不同信息源的组合以及ML算法可能对AAA预后评估产生积极影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information
An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as slow, medium or quick growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (LPBoost) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.
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