Correlation between transversal and orthogonal maximal diameters of abdominal aortic aneurysms and alternative rupture risk predictors

Novák, Kamil; Polzer, Stanislav; Křivka, Tomáš; Vlachovský, Robert; Staffa, Robert; Kubíček, Luboš; Lambert, Lukáš; Burša, Jiří

doi:10.1016/j.compbiomed.2017.03.005

Cited by 9 publications

(2 citation statements)

References 42 publications

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“…Novak and co-workers concluded that the median of the axial diameter (58.0 mm) was significantly higher (p < 0.001) than the median of the orthogonal diameter (54.5 mm). 17 A study conducted by Kontopodis and colleagues 9 reported a consistently higher axial measurement of D max compared to the orthogonal measurement. A mean difference of 2.0 2.8 mm and a range of differences of 0 – 12.3 mm were obtained.…”

Section: Discussionmentioning

confidence: 96%

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

et al. 2018

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Abdominal aortic aneurysm (AAA) is an asymptomatic aortic disease with a survival rate of 20% after rupture. It is a vascular degenerative condition different from occlusive arterial diseases. The size of the aneurysm is the most important determining factor in its clinical management. However, other measures of the AAA geometry that are currently not used clinically may also influence its rupture risk. With this in mind, the objectives of this work are to develop an algorithm to calculate the AAA wall thickness and abdominal aortic diameter at planes orthogonal to the vessel centerline, and to quantify the effect of geometric indices derived from this algorithm on the overall classification accuracy of AAA based on whether they were electively or emergently repaired. Such quantification was performed based on a retrospective review of existing medical records of 150 AAA patients (75 electively repaired and 75 emergently repaired). Using an algorithm implemented within the MATLAB computing environment, 10 diameter- and wall thickness-related indices had a significant difference in their means when calculated relative to the AAA centerline compared to calculating them relative to the medial axis. Of these 10 indices, nine were wall thickness-related while the remaining one was the maximum diameter (D). D calculated with respect to the medial axis is over-estimated for both electively and emergently repaired AAA compared to its counterpart with respect to the centerline. C5.0 decision trees, a machine learning classification algorithm implemented in the R environment, were used to construct a statistical classifier. The decision trees were built by splitting the data into 70% for training and 30% for testing, and the properties of the classifier were estimated based on 1000 random combinations of the 70/30 data split. The ensuing model had average and maximum classification accuracies of 81.0 and 95.6%, respectively, and revealed that the three most significant indices in classifying AAA are, in order of importance: AAA centerline length, L2-norm of the Gaussian curvature, and AAA wall surface area. Therefore, we infer that the aforementioned three geometric indices could be used in a clinical setting to assess the risk of AAA rupture by means of a decision tree classifier. This work provides support for calculating cross-sectional diameters and wall thicknesses relative to the AAA centerline and using size and surface curvature based indices in classification studies of AAA.

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Section: Discussionmentioning

confidence: 96%

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

et al. 2018

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“…Decisionmaking related to clinical management for AAA patients is complex, because the evaluation of the rupture risk can only be assessed by monitoring the AAA without any intervention, which has its own risk. There have been several papers [3]- [6], wherein the traditional guideline for clinical AAA management based on a single criterion has been challenged; alternatives have been proposed which take into account various factors such as growth rate [3] [6], AAA volume [4], thrombus accumulation [8], asymmetry and tortuosity [8] [9] for improved assessment of aneurysm development and rupture risk. Particularly, there is recent consensus that the growth rate is critical for AAA clinical management even for small diameter AAAs [11].…”

Section: Introductionmentioning

confidence: 99%

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

Akkoyun

Kwon

Acar

et al. 2020

Computers in Biology and Medicine

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Objective: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.Methods: 106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.

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CT Angiography of the Abdominal Aorta

Luo,

Xu,

Zhang

2024

Whole Body Computed Tomography Angiography

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Correlation between transversal and orthogonal maximal diameters of abdominal aortic aneurysms and alternative rupture risk predictors

Cited by 9 publications

References 42 publications

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

CT Angiography of the Abdominal Aorta

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