Quantitative assessment of abdominal aortic aneurysm geometry.
Quantitative assessment of abdominal aortic aneurysm geometry.
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DOI：
10.1007/s10439-010-0175-3
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发表时间：
2011-01
影响因子：
3.8
通讯作者：
Finol, Ender A.
中科院分区：
文献类型：
--
作者：
Shum, Judy;Martufi, Giampaolo;Di Martino, Elena;Washington, Christopher B.;Grisafi, Joseph;Muluk, Satish C.;Finol, Ender A.
最近的研究表明，腹主动脉瘤（AAA）的最大横径和扩张率并不是完全可靠的破裂可能性指标。我们假设动脉瘤形态学和壁厚更能预测破裂风险，并且可以成为该疾病临床管理的决定性因素。在对10例破裂和66例未破裂动脉瘤的回顾性研究中，对AAA形状进行了无创、基于图像的评价。从分割的对比增强计算机断层扫描图像生成三维模型。基于新的分割算法估计壁厚的几何指数和区域变化。使用J48决策树算法创建模型，并使用十重交叉验证评估其性能。使用χ2检验进行特征选择。该模型正确分类了65个数据集，平均预测准确率为86.6%（κ = 0.37）。排名最高的特征是囊长度、囊高度、体积、表面积、最大直径、隆起高度和腔内血栓体积。鉴于单个AAA具有复杂的形状，表面曲率和壁厚局部变化，AAA破裂风险的评估应基于对囊性形状和大小的准确定量。
Recent studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) and expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that aneurysm morphology and wall thickness are more predictive of rupture risk and can be the deciding factors in the clinical management of the disease. A non-invasive, image-based evaluation of AAA shape was implemented on a retrospective study of 10 ruptured and 66 unruptured aneurysms. Three-dimensional models were generated from segmented, contrast-enhanced computed tomography images. Geometric indices and regional variations in wall thickness were estimated based on novel segmentation algorithms. A model was created using a J48 decision tree algorithm and its performance was assessed using ten-fold cross validation. Feature selection was performed using the χ2-test. The model correctly classified 65 datasets and had an average prediction accuracy of 86.6% (κ = 0.37). The highest ranked features were sac length, sac height, volume, surface area, maximum diameter, bulge height, and intra-luminal thrombus volume. Given that individual AAAs have complex shapes with local changes in surface curvature and wall thickness, the assessment of AAA rupture risk should be based on the accurate quantification of aneurysmal sac shape and size.
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影响因子：
2.8
作者：
Giannoglou, G.;Giannakoulas, G.;Louridas, G.
通讯作者：
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