Abstract
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Current clinical guidelines recommend elective surgical repair when the maximum AAA diameter exceeds 55 mm in men or 50 mm in women. Patients that do not meet these criteria are periodically monitored, with surveillance intervals based on the maximum AAA diameter. However, this diameter does not take into account the complex relation between the 3D AAA shape and its growth, making standardized intervals potentially unfit. Personalized AAA growth predictions could improve monitoring strategies. We propose to use an SE(3)-symmetric transformer model to predict AAA growth directly on the vascular model surface enriched with local, multi-physical features. In contrast to other works which have parameterized the AAA shape, this representation preserves the vascular surface's anatomical structure and geometric fidelity. We train our model using a longitudinal dataset of 113 computed tomography angiography (CTA) scans of 24 AAA patients at irregularly sampled intervals. After training, our model predicts AAA growth to the next scan moment with a median diameter error of 1.18 mm. We further demonstrate our model's utility to identify whether a patient will become eligible for elective repair within two years (acc = 0.93). Finally, we evaluate our model's generalization on an external validation set consisting of 25 CTAs from 7 AAA patients from a different hospital. Our results show that local directional AAA growth prediction from the vascular surface is feasible and may contribute to personalized surveillance strategies.
Abstract (translated)
腹主动脉瘤（AAAs）是腹部主动脉的局部扩张病变。这些瘤体有可能破裂，且破裂后的生存率仅为20%。目前的临床指南建议，在最大AAAs直径超过55毫米（男性）或50毫米（女性）时进行择期手术修复。不满足这些条件的患者将定期接受监测，其监控间隔基于最大AAAs直径来确定。然而，仅根据这种直径测量值无法全面考虑3D AAAs形状与其生长之间的复杂关系，这使得标准化的时间间隔可能不适合所有情况。个性化预测AAAs生长速度可以改进监控策略。 我们提出使用一个SE(3)对称变换模型直接在血管模型表面（该表面丰富了局部多物理特性）上进行AAAs增长的预测。与将AAAs形状参数化的其他研究不同，这种表示方法保留了血管表面对解剖结构和几何精确度。我们的模型是通过24名患者113次不规则时间间隔采样的计算机断层扫描（CTA）纵向数据集训练的。 在完成训练后，我们的模型可以预测到下次扫描时刻AAAs的增长情况，并且直径误差中位数仅为1.18毫米。此外，我们还展示了该模型能够识别患者在未来两年内是否会达到需要进行择期手术修复的标准（准确率0.93）。 最后，在来自另一家医院的7名患者的25次CTA组成的外部验证集上评估了我们的模型泛化能力。结果显示，从血管表面预测局部方向上的AAAs增长是可行的，并且可以有助于制定个性化的监控策略。
URL
https://arxiv.org/abs/2506.08729
