Toward Improved Prediction of AAA Rupture Risk: Implementation of Feature-Based Geometry Quantification Measures Compared to Maximum Diameter Alone

2012 ◽  
Author(s):  
J. Shum ◽  
S. C. Muluk ◽  
A. Doyle ◽  
A. Chandra ◽  
M. Eskandari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Statistical Models ◽  
Learning Algorithm ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Rupture Risk ◽  
Large Database ◽  
Abdominal Aortic ◽  
Feature Based

Data mining techniques are capable of extracting important relationships and correlations among large amounts of data while machine learning methodologies can utilize these correlations to generate models capable of classification and prediction. The combination of machine learning and data mining is an important contribution of the present work for two reasons: (1) given a large database of features that describe the geometry of native abdominal aortic aneurysms (AAAs), patterns and relationships in the data are derived that may not be apparent to the human eye, and (2) statistical models are generated that can classify new data and determine which features discriminate among different aneurysm populations. The objectives of this study were to use anatomically realistic AAA models to evaluate a proposed set of global geometric indices describing the size, shape and individual wall thickness of the aneurysm sac, and use a learning algorithm to develop a model that is capable of discriminating the rupture status of these aneurysms.

scholarly journals High-frequency murine ultrasound provides enhanced metrics of BAPN-induced AAA growth

2019 ◽  
Vol 317 (5) ◽  
pp. H981-H990 ◽  
Author(s):  
Daniel J. Romary ◽  
Alycia G. Berman ◽  
Craig J. Goergen
Keyword(s):  
Surface Area ◽  
Murine Model ◽  
High Frequency ◽  
Wall Stress ◽  
Growth Patterns ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Rupture Risk ◽  
Abdominal Aortic ◽  
Peak Wall Stress

An abdominal aortic aneurysm (AAA), defined as a pathological expansion of the largest artery in the abdomen, is a common vascular disease that frequently leads to death if rupture occurs. Once diagnosed, clinicians typically evaluate the rupture risk based on maximum diameter of the aneurysm, a limited metric that is not accurate for all patients. In this study, we worked to evaluate additional distinguishing factors between growing and stable murine aneurysms toward the aim of eventually improving clinical rupture risk assessment. With the use of a relatively new mouse model that combines surgical application of topical elastase to cause initial aortic expansion and a lysyl oxidase inhibitor, β-aminopropionitrile (BAPN), in the drinking water, we were able to create large AAAs that expanded over 28 days. We further sought to develop and demonstrate applications of advanced imaging approaches, including four-dimensional ultrasound (4DUS), to evaluate alternative geometric and biomechanical parameters between 1) growing AAAs, 2) stable AAAs, and 3) nonaneurysmal control mice. Our study confirmed the reproducibility of this murine model and found reduced circumferential strain values, greater tortuosity, and increased elastin degradation in mice with aneurysms. We also found that expanding murine AAAs had increased peak wall stress and surface area per length compared with stable aneurysms. The results from this work provide clear growth patterns associated with BAPN-elastase murine aneurysms and demonstrate the capabilities of high-frequency ultrasound. These data could help lay the groundwork for improving insight into clinical prediction of AAA expansion. NEW & NOTEWORTHY This work characterizes a relatively new murine model of abdominal aortic aneurysms (AAAs) by quantifying vascular strain, stress, and geometry. Furthermore, Green-Lagrange strain was calculated with a novel mapping approach using four-dimensional ultrasound. We also compared growing and stable AAAs, finding peak wall stress and surface area per length to be most indicative of growth. In all AAAs, strain and elastin health declined, whereas tortuosity increased.

Geometry Quantification of Abdominal Aortic Aneurysms

2010 ◽  
Author(s):  
Judy Shum ◽  
Elena Di Martino ◽  
Satish Muluk ◽  
Ender A. Finol
Keyword(s):  
Wall Thickness ◽  
Three Dimensional ◽  
Roc Curves ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Transverse Diameter ◽  
Rupture Risk ◽  
Abdominal Aortic ◽  
Elective Repair ◽  
Segmented Images

Recent studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) and expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that aneurysm morphology and wall thickness can be quantified in a systematic approach leading to accurate differentiation of the geometric characteristics of aneurysm population subsets. A non-invasive, image-based evaluation of AAA shape was implemented on a retrospective study of sixty-six subjects who underwent elective repair and twenty-eight subjects who suffered AAA rupture within 1 month of their last pre-operative follow-up. The contrast-enhanced computed tomography (CT) scans of these patients were used to generate three-dimensional models from the segmented images. Twenty-eight geometry-based indices were calculated to characterize the size and shape of the AAA sac, and regional variations in wall thickness were estimated based on a novel segmentation algorithm. A multivariate analysis of variance using a maximum AAA diameter of 5.5 cm as a factor was performed for all indices as dependent variables, for the electively repaired group. Box and Whisker plots and ROC curves were generated to determine the indices’ potential as predictors of rupture risk. Listed from highest to lowest area under the ROC curve (AUC), the following six indices were found statistically significant (p < 0.05): volume (V, p < 0.0001), surface area (S, p < 0.0001), intraluminal thrombus volume (VILT, p < 0.0001), diameter-to-diameter ratio (DDr, p < 0.0001), diameter-to-height ratio (DHr, p = 0.015), and centroid distance of the maximum diameter (dc, p = 0.008). Given that individual AAAs have complex, tortuous and asymmetric shapes with local changes in surface curvature and wall thickness, the assessment of AAA rupture risk should require the accurate characterization of aneurysmal sac shape.

Three-Dimensional Geometrical Characterization of Abdominal Aortic Aneurysms: Image-Based Wall Thickness Distribution

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Giampaolo Martufi ◽  
Elena S. Di Martino ◽  
Cristina H. Amon ◽  
Satish C. Muluk ◽  
Ender A. Finol
Keyword(s):  
Wall Thickness ◽  
Three Dimensional ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Patient Specific ◽  
Future Research ◽  
Geometrical Shape ◽  
Rupture Risk ◽  
Abdominal Aortic ◽  
Elective Repair

The clinical assessment of abdominal aortic aneurysm (AAA) rupture risk is based on the quantification of AAA size by measuring its maximum diameter from computed tomography (CT) images and estimating the expansion rate of the aneurysm sac over time. Recent findings have shown that geometrical shape and size, as well as local wall thickness may be related to this risk; thus, reliable noninvasive image-based methods to evaluate AAA geometry have a potential to become valuable clinical tools. Utilizing existing CT data, the three-dimensional geometry of nine unruptured human AAAs was reconstructed and characterized quantitatively. We propose and evaluate a series of 1D size, 2D shape, 3D size, 3D shape, and second-order curvature-based indices to quantify AAA geometry, as well as the geometry of a size-matched idealized fusiform aneurysm and a patient-specific normal abdominal aorta used as controls. The wall thickness estimation algorithm, validated in our previous work, is tested against discrete point measurements taken from a cadaver tissue model, yielding an average relative difference in AAA wall thickness of 7.8%. It is unlikely that any one of the proposed geometrical indices alone would be a reliable index of rupture risk or a threshold for elective repair. Rather, the complete geometry and a positive correlation of a set of indices should be considered to assess the potential for rupture. With this quantitative parameter assessment, future research can be directed toward statistical analyses correlating the numerical values of these parameters with the risk of aneurysm rupture or intervention (surgical or endovascular). While this work does not provide direct insight into the possible clinical use of the geometric parameters, we believe it provides the foundation necessary for future efforts in that direction.

Aortic Lumen Area Is Increased in Ruptured Abdominal Aortic Aneurysms and Correlates to Biomechanical Rupture Risk

2018 ◽  
Vol 25 (6) ◽  
pp. 750-756 ◽  
Author(s):  
Antti Siika ◽  
Moritz Lindquist Liljeqvist ◽  
Rebecka Hultgren ◽  
T. Christian Gasser ◽  
Joy Roy
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Patient Specific ◽  
Lumen Area ◽  
Rupture Risk ◽  
Cross Sectional ◽  
Vessel Area ◽  
Abdominal Aortic ◽  
Risk Parameters

Purpose: To investigate how 2-dimensional geometric parameters differ between ruptured and asymptomatic abdominal aortic aneurysms (AAAs) and provide a biomechanical explanation for the findings. Methods: The computed tomography angiography (CTA) scans of 30 patients (mean age 77±10 years; 23 men) with ruptured AAAs and 60 patients (mean age 76±8 years; 46 men) with asymptomatic AAAs were used to measure maximum sac diameter along the center lumen line, the cross-sectional lumen area, the total vessel area, the intraluminal thrombus (ILT) area, and corresponding volumes. The CTA data were segmented to create 3-dimensional patient-specific models for finite element analysis to compute peak wall stress (PWS) and the peak wall rupture index (PWRI). To reduce confounding from the maximum diameter, 2 diameter-matched groups were selected from the initial patient cohorts: 28 ruptured AAAs and another with 15 intact AAAs (diameters 74±12 vs 73±11, p=0.67). A multivariate model including the maximum diameter, the lumen area, and the ILT area of the 60 intact aneurysms was employed to predict biomechanical rupture risk parameters. Results: In the diameter-matched subgroup comparison, ruptured AAAs had a significantly larger cross-sectional lumen area (1954±1254 vs 1120±623 mm2, p=0.023) and lower ILT area ratio (55±24 vs 68±24, p=0.037). The ILT area (2836±1462 vs 2385±1364 mm2, p=0.282) and the total vessel area (3956±1170 vs 4338±1388 mm2, p=0.384) did not differ statistically between ruptured and intact aneurysms. The PWRI was increased in ruptured AAAs (0.80 vs 0.48, p<0.001), but the PWS was similar (249 vs 284 kPa, p=0.194). In multivariate regression analysis, lumen area was significantly positively associated with both PWS (p<0.001) and PWRI (p<0.01). The ILT area was also significantly positively associated with PWS (p<0.001) but only weakly with PWRI (p<0.01). The lumen area conferred a higher risk increase in both PWS and PWRI when compared with the ILT area. Conclusion: The lumen area is increased in ruptured AAAs compared to diameter-matched asymptomatic AAAs. Furthermore, this finding may in part be explained by a relationship with biomechanical rupture risk parameters, in which lumen area, irrespective of maximum diameter, increases PWS and PWRI. These observations thus suggest a possible method to improve prediction of rupture risk in AAAs by measuring the lumen area without the use of computational modeling.

Advancements in identifying biomechanical determinants for abdominal aortic aneurysm rupture

Vascular ◽  
2014 ◽  
Vol 23 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Nikolaos Kontopodis ◽  
Eleni Metaxa ◽  
Yannis Papaharilaou ◽  
Emmanouil Tavlas ◽  
Dimitrios Tsetis ◽  
...  
Keyword(s):  
Growth Rate ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Abdominal Aortic Aneurysms ◽  
Aneurysm Rupture ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Rupture Risk ◽  
Abdominal Aortic Aneurysm Rupture ◽  
Abdominal Aortic

Abdominal aortic aneurysms are a common health problem and currently the need for surgical intervention is determined based on maximum diameter and growth rate criteria. Since these universal variables often fail to predict accurately every abdominal aortic aneurysms evolution, there is a considerable effort in the literature for other markers to be identified towards individualized rupture risk estimations and growth rate predictions. To this effort, biomechanical tools have been extensively used since abdominal aortic aneurysm rupture is in fact a material failure of the diseased arterial wall to compensate the stress acting on it. The peak wall stress, the role of the unique geometry of every individual abdominal aortic aneurysm as well as the mechanical properties and the local strength of the degenerated aneurysmal wall, all confer to rupture risk. In this review article, the assessment of these variables through mechanical testing, advanced imaging and computational modeling is reviewed and the clinical perspective is discussed.

scholarly journals Ultrasound-Based Fluid-Structure Interaction Modeling of Abdominal Aortic Aneurysms Incorporating Pre-stress

2021 ◽  
Vol 12 ◽  
Author(s):  
Judith H. C. Fonken ◽  
Esther J. Maas ◽  
Arjet H. M. Nievergeld ◽  
Marc R. H. M. van Sambeek ◽  
Frans N. van de Vosse ◽  
...  
Keyword(s):  
Fluid Structure Interaction ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Maximum Diameter ◽  
Patient Specific ◽  
Risk Indicator ◽  
Rupture Risk ◽  
Fluid Structure ◽  
Structure Interaction ◽  
Abdominal Aortic

Currently, the prediction of rupture risk in abdominal aortic aneurysms (AAAs) solely relies on maximum diameter. However, wall mechanics and hemodynamics have shown to provide better risk indicators. Patient-specific fluid-structure interaction (FSI) simulations based on a non-invasive image modality are required to establish a patient-specific risk indicator. In this study, a robust framework to execute FSI simulations based on time-resolved three-dimensional ultrasound (3D+t US) data was obtained and employed on a data set of 30 AAA patients. Furthermore, the effect of including a pre-stress estimation (PSE) to obtain the stresses present in the measured geometry was evaluated. The established workflow uses the patient-specific 3D+t US-based segmentation and brachial blood pressure as input to generate meshes and boundary conditions for the FSI simulations. The 3D+t US-based FSI framework was successfully employed on an extensive set of AAA patient data. Omitting the pre-stress results in increased displacements, decreased wall stresses, and deviating time-averaged wall shear stress and oscillatory shear index patterns. These results underline the importance of incorporating pre-stress in FSI simulations. After validation, the presented framework provides an important tool for personalized modeling and longitudinal studies on AAA growth and rupture risk.

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

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

2017 ◽  
Vol 83 ◽  
pp. 151-156 ◽  
Author(s):  
Kamil Novak ◽  
Stanislav Polzer ◽  
Tomas Krivka ◽  
Robert Vlachovsky ◽  
Robert Staffa ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Rupture Risk ◽  
Abdominal Aortic ◽  
Risk Predictors
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