CT after Endovascular Repair of Abdominal Aortic Aneurysms: Diagnostic Accuracy of Diameter Measurements for the Detection of Aneurysm Sac Enlargement

The Role of Aortic Volume in the Natural History of Abdominal Aortic Aneurysms and Post-Endovascular Aortic Aneurysm Repair Surveillance

There has been a debate about whether maximum diameter can be solely used to assess the natural history of abdominal aortic aneurysm. The aim of the present review is to collect all the available evidence on the role of abdominal aortic aneurysm (AAA) volume in the natural history of AAAs, including small untreated AAAs and AAAs treated by EVAR. The current literature appears to reinforce the role of volume as a supplementary measure for evaluating the natural history of AAA, in both intact AAAs and after EVAR. The clinical impact of AAA volume measurements remains unclear. Several studies show that volumetric analysis can assess changes in AAAs and predict successful endoluminal exclusion after EVAR more accurately than diameter. However, most studies lack strict standardized measurement criteria and well-defined outcome definitions. It remains unclear whether volumetry could replace diameter assessment in defining the risk of rupture of AAAs and identifying clinically relevant sac growth.

Abdominal Aortic Aneurysm Diameter versus Volume: A Systematic Review

Recently, AAA volume measurement has been proposed as a potentially valuable surveillance method in situations when diameter measurement might fail.

OBJECTIVE

The aim of this systematic review was to analyze the results of previous studies comparing AAA diameter and volume measurements.

METHODS

A systematic search in PubMed, Cochrane, and EMBASE databases was performed to identify studies investigating the use of diameter and volume measurements in AAA diagnosis and prognosis in English, German, and Russian, published until December 2022. The manuscripts were reviewed by three researchers and scored on the quality of the research using MINORS criteria.

RESULTS

After screening 752 manuscripts, 19 studies (n = 1690) were included. The majority (n = 17) of the manuscripts appeared to favor volume. It is, however, important to highlight the heterogeneity of methodologies and lack of standardized protocol for measuring both volume and diameter in the included studies, which hindered the interpretation of the results.

CONCLUSIONS

The clinical relevance of abdominal aortic aneurysm volume measurement is still unclear, although studies show favorable and promising results for volumetric changes in AAA, especially in follow-up after EVAR.

The association of body composition with abdominal aortic aneurysm growth after endovascular aneurysm repair

Background

Body composition (BC) may be associated with abdominal aortic aneurysm (AAA) growth, but the results of previous research are contradictory. This study aimed to explore the relationship between BC and postoperative aneurysm progression.

Methods

Patients with regular postoperative follow-ups were retrospectively identified. The volume change of the aneurysm was measured to evaluate AAA progression. After segmenting different body components (subcutaneous fat, visceral fat, pure muscle, and intramuscular fat), the shape features and gray features of these tissues were extracted. Uni- and multivariable methods were used to analyze the relationship between imaging features of BC and AAA growth.

Results

A total of 94 patients (68 ± 8 years) were eligible for feature analyses. Patients with expansive aneurysms (29/94; volume change > 2%) were classified into Group(+) and others with stable or shrunken aneurysms (65/94) were classified into Group(−). Compared with Group(+), Group(−) showed a higher volume percent of pure muscle (21.85% vs 19.51%; p = .042) and a lower value of intramuscular fat (1.23% vs 1.65%; p = .025). CT attenuation of muscle tissues of Group(−) got a higher mean value (31.16 HU vs 23.92 HU; p = .019) and a lower standard deviation (36.12 vs 38.82; p = .006) than Group(+). For adipose tissue, we found no evidence of a difference between the two groups. The logistic regression model containing muscle imaging features showed better discriminative accuracy than traditional factors (84% vs 73%).

Conclusions

Muscle imaging features are associated with the volume change of postoperative aneurysms and can make an early prediction. Adipose tissue is not specifically related to AAA growth.

Automatic measurement of maximal diameter of abdominal aortic aneurysm on computed tomography angiography using artificial intelligence

INTRODUCTION

The treatment of abdominal aortic aneurysm (AAA) relies on surgical repair and the indication mainly depends on its size evaluated by the maximal diameter (Dmax). The aim of this study was to evaluate a new automatic method based on artificial intelligence (AI) to measure the Dmax on computed tomography angiography (CTA).

METHODS

A fully automatic segmentation of the vascular system was performed using a hybrid method combining expert system with supervised deep learning (DL). The aorta centreline was extracted from the segmented aorta and the aortic diameters were automatically calculated. Results were compared to manual segmentation performed by two human operators.

RESULTS

The median absolute error between the two human operators was 1.2 mm (IQR 0.5- 1.9). The automatic method using the DL algorithm demonstrated correlation with the human segmentation, with a median absolute error of 0.8 (0.5- 4.2) mm and a coefficient correlation of 0.91 (p<0.001).

CONCLUSION

Although validation in larger cohorts is required, this method brings perspectives to develop new tools to standardize and automate the measurement of AAA Dmax in order to help clinicians in the decision-making process.

Comparison of Outcomes Following EVAR Based on Aneurysm Diameter and Volume and Their Postoperative Variations

PURPOSE

to evaluate the impact of bi- and 3-dimensional preoperative aortic morphological features and their immediate postoperative variations on the outcome of abdominal aortic aneurysms (AAA) treated by endovascular exclusion with standard devices (EVAR).

MATERIALS AND METHODS

Double centre retrospective analysis of prospectively collected registry data of EVAR patients. For all patients, preoperative and 30-day computed tomographic angiography images (CTA) were reviewed. Preoperative maximum AAA diameter >59 mm and volume >159 cm³, and any 30-day postoperative increasing at CTA, were considered as potentially influencing the outcome. The outcome measures were: primary technical success; 30-day, 1-year, and mean follow-up reintervention, all-cause and AAA-related mortality rates, and also endoleak-related reinterventions.

RESULTS

Three hundred and thrity-three patients were enrolled. Mean preoperative and 30-day AAA diameter and volume were 50.4 mm ± 11.8 vs. 49.1 mm ± 12.1, and 112.9 cm3 ± 79.5 vs. 112.1 cm3 ± 80.5, respectively. Primary technical success was achieved in all cases. At 34.9 months follow-up, cumulative reintervention rate was 12.0%, mortality rates 7.2%, without AAA-related deaths. Endoleak-related reintervention rate was 7.5%. At uni- and multi-variate analysis, preoperative AAA diameter >59 mm, and AAA volume >159 cm³ were significantly associated to reintervention (P = 0.012; P = 0.002), and reintervention and death (P = 0.002; P = 0.001) during follow-up. Additionally, any increase in postoperative AAA diameter or volume was significantly associated with reintervention (P = 0.001, P = 0.001) and reintervention and death (P = 0.006, P = 0.001). Endoleak-related reintervention were also significantly associated with all of the analysed morphological parameters (P = 0.019, P = 0.005, P = 0.005, and P = 0.002, respectively).

CONCLUSIONS

Patients with larger baseline AAA size and volume as well as unfavourable early remodelling of the sac are associated to worse long-term EVAR outcome.

Machine deep learning accurately detects endoleak after endovascular abdominal aortic aneurysm repair

Objective

The objective of this study was to develop a machine deep learning algorithm for endoleak detection and measurement of aneurysm diameter, area, and volume from computed tomography angiography (CTA).

Methods

Digital Imaging and Communications in Medicine files representing three-phase postoperative CTA images (N = 334) of 191 unique patients undergoing endovascular aneurysm repair for infrarenal abdominal aortic aneurysm (AAA) with a variety of commercial devices were used to train a deep learning pipeline across four tasks. The RetinaNet object-detection convolutional neural network (CNN) architecture was trained to predict bounding boxes around the axial CTA slices that were then stitched together in two dimensions into a smaller region containing the aneurysm. Multiclass endoleak detection and segmentation of the AAA, endograft, and endoleak were performed on this smaller region. Segmentations on a single randomly selected contrast from each scan included 33 full and 68 partial segmentations for endograft and AAA and 99 full segmentations for endoleak. A modified version of ResNet-50 CNN was used to detect endoleak on individual axial slices. A three-dimensional U-Net CNN model was trained on the task of dense three-dimensional segmentation and used to measure diameter and volume with a specially designed loss function. We made use of fivefold cross-validation to evaluate model performance for each step, splitting training and testing data at each fold, such that multiple scans from the same patient were preserved with the same fold. Algorithm predictions for endoleak were compared with the radiology report and with a subset of CTA images independently read by two vascular specialists.

Results

The localization portion of the network accurately predicted a region of interest containing the AAA in 99% of cases. The best model of binary endoleak detection obtained an area under the receiver operating characteristic curve of 0.94 ± 0.03 with an optimized accuracy of 0.89 ± 0.03 on a balanced data set. An introduced postprocessing algorithm for determining maximum diameter was used on both the predicted AAA segmentation and ground truth segmentation, predicting on average an absolute diameter error of 2.3 ± 2.0 mm by 1.4 ± 1.7 mm for each measurement, respectively. The algorithm measured AAA and endograft volume accurately (Dice coefficient, 0.95 ± 0.2) with an absolute volume error of 10.1 ± 9.1 mL. The algorithm measured endoleak volume less accurately, with a Dice score of 0.53 ± 0.21 and an average absolute volume error of 1.2 ± 1.9 mL.

Conclusions

This machine learning algorithm shows promise in augmenting a human's ability to interpret postoperative CTA images and may help improve surveillance after endovascular aneurysm repair. External validation on larger data sets and prospective study are required before the algorithm can be clinically applicable.

This manuscript describing the application of machine learning for endoleak has clinical relevance to endovascular abdominal aortic aneurysm follow-up. The techniques described herein may be more broadly applied to the diagnosis of aortic disease. In the future, vascular surgeons will benefit from integrating such artificial intelligence algorithms into their practice.

Aortic sac enlargement after endovascular aneurysm repair: volume-related changes and the impact of intraluminal thrombus

Purpose

Abdominal aortic aneurysm (AAA) growth after endovascular aneurysm repair (EVAR) is still unpredictable. The issue of optimal frequency of computed tomography angiography for surveillance and its measurement method accuracy remain unclear. We aimed to assess the value of abdominal aneurysm sac volume measurement for detecting expansions and the association of preprocedural intraluminal thrombus (ILT) volume with aneurysm sac growth following EVAR.

Material and methods

A total of 107 patients underwent elective EVAR. Inclusion criteria provided a cohort of 39 patients. Changes of postoperative maximum aneurysm sac diameter and AAA volume were calculated. Volumetric AAA changes and demographic data of the cases with clinically irrelevant AAA diameter enlargement were evaluated. Preoperative ILT volumes were collected. ILT and AAA sac volume ratio was calculated. Statistical data analysis was performed using standard methods.

Results

The mean changes of maximum AAA diameter and volume in percentage after EVAR were –5.08 ± 8.20 mm and –13.39 ± 23.32%, respectively. A moderate positive linear correlation between those changes was found (R² = 0.731; p < 0.0001). The mean relative AAA volume increase in cases without clinically relevant diameter enlargement was 11.50 ± 8.27%. The means of ILT and AAA sac ratios were 0.59 ± 0.17 and 0.52 ± 1.8 in growing AAA sac and in stable or shrinking AAA sac groups, respectively (p = 0.308).

Conclusions

Volumetric AAA measurement may be useful as an additional method to diameter measurement after EVAR to identify clinically relevant sac growth. Preoperative volume of ILT may not significantly affect the growth rate of AAA after EVAR.

Volumetric assessment of extracranial carotid artery aneurysms

The extracranial carotid artery aneurysm (ECAA) is a rare pathology for which clinical treatment guidelines are lacking. In general, symptoms or growth of the aneurysm sac are thought to indicate intervention. ECAAs may present in a large variety of shapes and sizes, and conventional diameter measurements fail to indicate geometrical differences. Therefore, we propose a protocol to measure ECAA size by 3D volumetric assessment. The volumes of 40 ECAAs in computed tomography angiography (CTA) images were measured through manual segmentation, by two independent operators. Volumes of the entire internal carotid artery (ICA) and the ECAA were measured separately. Excellent inter- and intraoperator reliability was found for both ICA and ECAA volumes, with all intraclass correlation coefficients above 0.94. Bland-Altman analysis revealed normal differences for both inter- and intraoperator agreement. For all volumes, similarity of the segmentations was excellent. Outliers were explained by presence of intraluminal ECAA thrombus, which hampered identification of the aneurysm outer wall. These results implicate robustness of our protocol, which is designed as a step-up towards (semi)automatic volumetric measurements to monitor patients with ECAA. Future (semi)automatic volumetric assessments are recommended and such techniques can be developed and validated using the proposed protocol and manual reference segmentations.