Coatsaliou Quentin, Lareyre Fabien, Raffort Juliette, Webster Claire, Bicknell Colin, Pouncey Anna, Ducasse Eric, Caradu Caroline
Department of Vascular Surgery, Bordeaux University Hospital, Bordeaux, France.
Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, Antibes, France.
J Endovasc Ther. 2024 May 9:15266028241252097. doi: 10.1177/15266028241252097.
Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events.
Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions.
Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm [IQR: 113.8-171.7] vs 125.4 cm [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711).
The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter.
The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair.
内漏是血管腔内主动脉修复术（EVAR）后主要并发症之一，可导致再次干预率增加和继发性破裂。需要进行终身连续监测，传统方法包括横断面成像及手动轴向测量。基于人工智能（AI）的成像分析技术已得到发展，可能提供更精确、快速的评估。本研究旨在评估基于AI的软件评估EVAR术后形态学变化、检测内漏以及将其与EVAR相关不良事件相关联的能力。
对2017年1月至2020年3月在一家三级医院接受EVAR且至少有2次随访计算机断层扫描血管造影（CTA）的患者，使用PRAEVAorta 2（Nurea）软件进行分析。将该软件与人类专家提供的真实情况进行比较，计算敏感度（Se）、特异度（Sp）、阴性预测值（NPV）和阳性预测值（PPV）。血管腔内主动脉修复相关不良事件定义为动脉瘤相关死亡、破裂、内漏、肢体闭塞和EVAR相关再次干预。
纳入56例患者，中位影像随访时间为27个月（四分位间距[IQR]：20 - 40）。尽管血栓体积减少了13.47%（p = 0.0216），但最大动脉瘤直径（55.62 mm [IQR：52.33 - 59.25] 对比54.34 mm [IQR：46.13 - 59.47]；p = 0.2162）或体积（130.4 cm³ [IQR：113.8 - 171.7] 对比125.4 cm³ [IQR：96.3 - 169.1]；p = 0.1131）随时间无显著差异。PRAEVAorta软件检测内漏的敏感度为89.47%（95%置信区间[CI]：80.58至94.57），特异度为91.25%（95% CI：83.02至95.70），阳性预测值为90.67%（95% CI：81.97至95.41），阴性预测值为90.12%（95% CI：81.70至94.91）。血管腔内主动脉修复相关不良事件与总体积变化相关，曲线下面积（AUC）为0.7806，而最大直径的AUC为0.7277。内漏情况也观察到相同趋势（AUC为0.7086对比0.6711）。
基于AI的软件PRAEVAorta能够对EVAR术后主动脉重塑进行详细的解剖学特征描述，并显示出其在随访期间自动检测内漏方面的潜在价值。与最大直径相比，主动脉瘤体积与EVAR相关不良事件和内漏的关联更为显著。
将PRAEVAorta AI软件整合到临床实践中有望使EVAR术后监测发生变革性转变。通过提供精确、快速的内漏检测和全面的解剖学评估，临床医生有望提高诊断准确性并简化患者管理。这一创新减少了对手动测量的依赖，可能减少解读误差并缩短评估时间。最终，PRAEVAorta的功能有可能优化患者护理，在血管腔内主动脉修复中实现更及时的干预并改善预后。
