Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period.
Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period.
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DOI：
10.1097/sla.0000000000004711
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发表时间：
2023-01-01
影响因子：
9
通讯作者：
Lee R
中科院分区：
文献类型：
--
作者：
Chandrashekar A;Handa A;Lapolla P;Shivakumar N;Ngetich E;Grau V;Lee R
我们研究了几何特征对未来AAA生长预测的效用。新的方法来预测AAA的生长被认为是一个研究重点。几何特征已被用于预测脑动脉瘤破裂，但没有检查作为AAA生长的预测因子。对肾下AAA患者的计算机断层扫描（CT）进行了分析。使用自动管道分割主动脉容积，以提取AAA直径（APD）、波动指数（UI）和曲率半径（RC）。使用前瞻性招募的队列，我们首先检查了这些几何测量与患者人口统计学特征之间的关系（n = 102）。从正在进行的临床数据库中识别出192例AAA患者，在AAA监测期间进行了连续CT扫描。训练和优化了多项式逻辑和多元线性回归模型，以预测这些患者未来的AAA生长。几何测量值与患者的人口统计学特征之间无相关性。APD（斯皮尔曼r = 0.25，P < 0.05）、UI（斯皮尔曼r = 0.38，P < 0.001）和RC（斯皮尔曼r =-0.53，P < 0.001）与AAA的年增长显著相关。使用APD、UI和RC作为3个输入变量，预测12个月时缓慢生长（<2.5 mm/年）或快速生长（>5 mm/年）的接收器操作特征曲线下面积分别为0.80和0.79。在87%的情况下，预测或增长率误差在2 mm以内。AAA的几何特征可以预测其未来的增长。该方法可应用于在AAA监测路径期间从患者获得的常规临床CT扫描。
We investigated the utility of geometric features for future AAA growth prediction. Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients. There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =–0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases. Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.