Dataset Information

Custom tissue engineered aneurysm models with varying neck size and height for early stage in vitro testing of flow diverters.

ABSTRACT: Endovascular techniques for treating cerebral aneurysms are rapidly advancing and require testing to optimize device configurations. The purpose of this work was to customize tissue-engineered aneurysm "blood vessel mimics" (aBVMs) for early stage in vitro assessment of vascular cell responses to flow diverters and other devices. Aneurysm scaffolds with varying neck size and height were created through solid modeling, mold fabrication, mandrel creation, and electrospinning. Scaffold dimensions and fiber morphology were characterized. aBVMs were created by depositing human smooth muscle and endothelial cells within scaffolds, and cultivating within perfusion bioreactors. These vessels were left untreated or used for flow diverter implantation. Cellular responses to flow diverters were evaluated at 3 days. Custom scaffolds were created with aneurysm neck diameters of 2.3, 3.5, and 5.5?mm and with aneurysm heights of 2, 5, and 8?mm. A set of scaffolds with varying neck size was used for aBVM creation, and dual-sodding of endothelial and smooth muscle cells resulted in consistent and confluent cellular linings. Flow diverters were successfully implanted in a subset of aBVMs, and initial cell coverage over devices was seen in the parent vessel at 3 days. Direct visualization of the device over the neck region was feasible, supporting the future use of these models for evaluating and comparing flow diverter healing. Tissue-engineered aneurysm models can be created with custom neck sizes and heights, and used to evaluate cellular responses to flow diverters and other endovascular devices.

SUBMITTER: Villadolid C

PROVIDER: S-EPMC7072062 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Custom tissue engineered aneurysm models with varying neck size and height for early stage in vitro testing of flow diverters.

Villadolid Camille C Puccini Brandon B Dennis Benjamin B Gunnin Tessa T Hedigan Conor C Cardinal Kristen O'Halloran KO

Journal of materials science. Materials in medicine 20200314 3

Endovascular techniques for treating cerebral aneurysms are rapidly advancing and require testing to optimize device configurations. The purpose of this work was to customize tissue-engineered aneurysm "blood vessel mimics" (aBVMs) for early stage in vitro assessment of vascular cell responses to flow diverters and other devices. Aneurysm scaffolds with varying neck size and height were created through solid modeling, mold fabrication, mandrel creation, and electrospinning. Scaffold dimensions a ...[more]

PMID: 32172490

Similar Datasets

Project description:OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

Project description:BACKGROUND AND PURPOSE:Incompletely occluded flow diverter treated aneurysms remain at risk of rupture and thromboembolic complications. Our aim was to identify the potential for incomplete occlusion of intracranial aneurysms treated by flow diverters. We investigated whether aneurysm ostium size in relation to parent artery size affects angiographic outcomes of flow diverter-treated sidewall aneurysms. MATERIALS AND METHODS:Flow diverter-treated sidewall aneurysms were divided into "occluded" and "residual" (incomplete occlusion) groups based on 6-month angiographic follow-up. We calculated the ostium ratio, a new parameter defined as the aneurysm ostium surface area versus the circumferential surface area of the parent artery. We also calculated the neck ratio, defined as clinical aneurysm neck diameter versus parent artery diameter from pretreatment 2D DSA, as a 2D surrogate. We compared the performance of these ratios with existing aneurysm morphometrics (size, neck diameter, volume, aspect ratio, size ratio, undulation index, nonsphericity index, ellipticity index, bottleneck factor, aneurysm angle, and parent vessel angle) and flow diverter-related parameters (metal coverage rate and pore density). Statistical tests and receiver operating characteristic analyses were performed to identify significantly different parameters between the 2 groups and test their predictive performances. RESULTS:We included 63 flow diverter-treated aneurysms, 46 occluded and 17 residual. The ostium ratio and neck ratio were significantly higher in the residual group than in the occluded group (P < .001 and P = .02, respectively), whereas all other parameters showed no statistical difference. As discriminating parameters for occlusion, ostium ratio and neck ratio achieved areas under the curve of 0.912 (95% CI, 0.838-0.985) and 0.707 (95% CI, 0.558-0.856), respectively. CONCLUSIONS:High ostium ratios and neck ratios could predict incomplete occlusion of flow diverter-treated sidewall aneurysms. Neck ratio can be easily calculated by interventionists to predict flow-diverter treatment outcomes.

Dataset Information

Custom tissue engineered aneurysm models with varying neck size and height for early stage in vitro testing of flow diverters.

Publications

Custom tissue engineered aneurysm models with varying neck size and height for early stage in vitro testing of flow diverters.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets