Measurements After Image Post-processing Are More Precise in the Morphometric Assessment of Thoracic Aortic Aneurysms: An Intermodal and Intra-observer Evaluation

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

OBJECTIVE

ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth.

MATERIAL AND METHODS

70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified.

RESULTS

the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth.

CONCLUSION

global shape features might provide an important contribution for predicting the aneurysm growth.

New indicators for systematic assessment of aortic morphology: a narrative review

In order to prevent the occurrence of aortic adverse events in ascending thoracic aortic aneurysm patients, preventive surgery is the sole option in case of large aneurysm. Identifying high-risk patients timely and accurately requires effective predictive indicators of aortic adverse events and accurate risk stratification thresholds. Absolute diameter measured after a single imaging examination, which has been used as the predictive indicator for decades, has been proved to be ineffective for risk stratification in moderately dilated aorta. Previously, new indicators combining absolute diameters with personalized parameters have been reported to show better predictive power of aortic adverse events than absolute diameters by correcting the effect of these parameters on the diameters. Meanwhile, combining three-dimensional parameters to formulate risk stratification thresholds not only may characterize the aortic risk morphology more precisely, but also predict aortic adverse events more accurately. These new indicators may provide more systematic assessment methods of patients’ risk, formulate more personalized intervention strategies for ascending thoracic aortic aneurysm patients, and also provide a basis for researchers to develop more accurate and effective risk thresholds. We also highlight that the algorithm obtained by combining multiple indicators may be a better choice compared with single indicator, but this still requires the support of more evidence. Due to the particularity of syndromic aortic disease, whether these new indicators can be used for its risk stratification is still uncertain. Therefore, the scope of this manuscript does not include this kind of disease.

A New Methodology to Determine Apposition, Dilatation, and Position of Endografts in the Descending Thoracic Aorta After Thoracic Endovascular Aortic Repair

Purpose: To validate computed tomography angiography (CTA)–applied software to assess apposition, dilatation, and position of endografts in the proximal and distal landing zones after thoracic endovascular aortic repair (TEVAR) of thoracic aortic aneurysm. Materials and Methods: Twenty-two patients (median age 75.5 years; 11 men) with a degenerative descending thoracic aortic aneurysm treated with TEVAR with at least one postoperative CTA were selected from a single center’s database. New CTA-applied software was used to determine the available apposition surface in the proximal and distal landing zones, apposition of the endograft fabric with the aortic wall, shortest apposition length, endograft inflow and outflow diameters, shortest distance between the left subclavian artery and the proximal endograft fabric, and shortest distance between the celiac trunk and the distal endograft fabric on each CTA. Interobserver variability for these parameters was assessed with the repeatability coefficient and the intraclass correlation coefficient. Results: Excellent interobserver agreement was found for all measurements. Interobserver variability of surface and shortest apposition length calculations was larger for the distal site compared with the proximal site, with a mean difference of 10% vs 2% of the mean available apposition surface, 12% vs 5% of the endograft apposition surface, and 16% vs 8% of the shortest apposition length, respectively. Inflow and outflow diameters of the endograft showed low variability, with a mean difference of 0.1 mm with 95% of the interobserver difference within 1.8 mm. Mean interobserver differences of the proximal and distal shortest fabric distances were 1.0 and 0.9 mm (both 2% of the mean lengths). Conclusion: Assessment of apposition, dilatation, and position of the proximal and distal parts of an endograft in the descending thoracic aorta is feasible after TEVAR with the new software. Interobserver agreement for all measured parameters was excellent for the proximal and distal landing zones. The new method allows detection of subtle changes during follow-up. However, a larger study is needed to quantify how parameters change over time in complicated and uncomplicated TEVAR cases and to define the real added value of the new methodology.