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Hostile aortic neck characteristics, such as short length and large diameter, have been associated with type Ia endoleaks and reintervention after endovascular aneurysm repair (EVAR). However, such characteristics partially describe the complex aortic neck morphology. The statistical shape model (SSM) of the infrarenal neck is an objective model that provides a quantitative description of the 3D neck morphology of an individual patient.
- principal component
- statistical shape model
- aortic aneurysm
- abdominal
1. Introduction
Endovascular aortic aneurysm repair (EVAR) is the preferred treatment for the exclusion of an abdominal aortic aneurysm (AAA) in the presence of suitable aorto-bi-iliac anatomy [1][2]. EVAR is associated with lower 30-day mortality and morbidity rates and shorter intensive care and hospital stay compared to open surgical procedures [3][4]. Hostile aortic neck characteristics, such as short length (<1 cm), severe supra- (>45°) and infrarenal (>60°) angulation, conicity, and diameter > 30 mm, have been associated with higher rates of type Ia endoleak, reintervention, and aneurysm-related mortality [3][5][6][7][8][9][10][11].
Currently, there is no consensus on the definition of hostile aortic neck morphology and how the individual neck characteristics or a combination of characteristics influence post-EVAR complications [12]. A more objective and personalized perspective on the 3D neck shape could provide more information that vascular surgeons can use during the decision making for conventional EVAR, fenestrated or branched-EVAR, open surgical repair, or a watchful waiting policy.
It can be hypothesized that an objective and patient-specific determination may be provided by investigating the 3D morphological infrarenal aortic neck shapes in a given dataset. This can be accomplished by means of a statistical shape model (SSM). An SSM is a mathematical technique that models the shape variation of an anatomy of interest in a population. For this, a principal component analysis (PCA) is performed, which returns linearly independent components describing the variation of the shape in the population, capturing potential interactions between morphological characteristics in these different components [13]. SSMs are widely applied for medical purposes, such as computer-assisted surgical planning tasks [14][15][16].
The SSM distinguishes unique shape components into a morphological model in such a way that the aortic neck morphology of a single patient can be reconstructed from a combination of these separate components. The shape components obtained from the SSM are not necessarily the same as the present known neck characteristics since a shape component might be a combination of these characteristics, such as angulation and neck diameter.
2. The Morphological Shape Variation of the Infrarenal Aortic Neck of Patients Treated with Standard Infrarenal EVAR
Current measurements of neck characteristics, such as neck length, diameter, and supra- and infrarenal angulation, are a simplification of the true morphological shape. It is still unknown if a combination or which combination of neck characteristics most influences successful EVAR outcomes [12]. The proposed SSM with five morphological shape components includes the combination of variation in length, deflection, and diameter and provides insight in anatomical features that are not captured by the conventional measurements. In addition to simplified centerline measurements, the SSM is able to reconstruct a complete infrarenal neck shape from patient-specific morphological shape components.
The current SSM uses a digital twin of the aorta as input. The SSM describes the variation of anatomical features that are present in the population of AAA patients. Parallel to this concept, digital models built with the help of artificial intelligence from preoperative aortic CT scans enable preoperative sizing in complex endovascular repair [17]. The development of automated segmentation and supervised deep learning may enrich the current SSM models with more detailed aortic segmentation [18]. Both techniques should be embraced by modern endovascular specialists taking care of (complex) AAA patients.
In the vascular field, Liang et al. implemented an SSM to associate morphological shape features of the ascending thoracic aortic aneurysms with the risk of rupture [19]. De Bruijne et al. created an SSM of the abdominal aortic aneurysm as a prerequisite for automated segmentation [20]. An SSM of the infrarenal neck of the abdominal aortic aneurysm, which is most important for sealing of the endograft, has not been developed yet.
A significant challenge of building an SSM is in obtaining point-wise alignment between input shapes. In lieu of a large set of well-defined anatomical landmarks on the aortic aneurysm neck, here, a tubular parametrization [21] was used. For the alignment of shapes in space, the LRA was chosen as the anchor point. Therefore, the shape variations are relative to the lowest renal artery baseline, which is most relevant for the EVAR procedure.
Each addition of a PC, to a maximum of nine PCs, results in an SSM that is better able to reconstruct the patient’s anatomy. The SSM, including nine PCs, is a robust model that includes 98% of the total shape variation in the dataset and that is able to describe new and unseen shapes.
Limitations of the SSM are that it relies on the input data and therefore on the patients in the dataset and the measurement of the lowest renal artery baseline and the distal end of the aortic neck. The dataset only included EVAR patients who were treated successfully with an infrarenal Endurant II/IIs device, with 96% of the patients treated within proximal neck-related IFU and who did not have a type Ia endoleak on the 30-days CT scan. The shape variation in the current SSM is specific for this population. Another limitation is the number of patients. Addition of more patients will make the SSM more generalizable.
In future studies, a patient-specific prediction of target apposition within the infrarenal or suprarenal aortic neck with either standard infrarenal EVAR or complex repair is desired. A next step in this direction would be to add patients with complications, such as type Ia endoleak, to the SSM and to perform a comparative study with a control cohort of uncomplicated EVAR patients. Moreover, to improve the model, it would be worthwhile to add aortic necks in the SSM that were judged not suitable for standard EVAR, such as patients with a juxtarenal AAA or patients who underwent open aortic repair due to too challenging an infrarenal neck morphology. This way, specific shape components that contribute to unanticipated loss of seal or neck anatomies not suitable for standard EVAR can be identified. As a future perspective, implementation of such a semi-automatic SSM in clinical practice, providing insight in the 3D morphology, may support endovascular aortic specialists in treatment planning.
3. Conclusions
An SSM of the morphological shape variation of the infrarenal aortic neck was developed of patients who were treated with EVAR for an abdominal aortic aneurysm. The SSM is an objective model that provides a quantitative description of the neck morphology of an individual patient. Nine PCs provide a generalizable and robust model to determine the morphology of the infrarenal aortic neck.
This entry is adapted from the peer-reviewed paper 10.3390/jcm11061687
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