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Johnson MD, Palmisciano P, Yamani AS, Hoz SS, Prestigiacomo CJ. A Systematic Review and Meta-Analysis of 3-Dimensional Morphometric Parameters for Cerebral Aneurysms. World Neurosurg 2024; 183:214-226.e5. [PMID: 38160907 DOI: 10.1016/j.wneu.2023.12.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/22/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Imaging modalities with increased spatial resolution have allowed for more precise quantification of cerebral aneurysm shape in 3-dimensional (3D) space. We conducted a systematic review and meta-analysis to assess the correlation of individual 3D morphometric measures with cerebral aneurysm rupture status. METHODS Two independent reviewers performed a PRISMA (preferred reporting items of systematic reviews and meta-analysis)-guided literature search to identify articles reporting the association between 3D morphometric measures of intracranial aneurysms and rupture status. RESULTS A total of 15,122 articles were identified. After screening, 39 studies were included. We identified 17 3D morphometric measures, with 11 eligible for the meta-analysis. The meta-analysis showed a significant association with rupture status for the following measures: nonsphericity index (standardized mean difference [SMD], 0.66; 95% confidence interval [CI], 0.53-0.79; P < 0.0001; I2 = 55.2%), undulation index (SMD, 0.55; 95% CI, 0.26-0.85; P = 0.0017; I2 = 68.1%), ellipticity index (SMD, 0.53; 95% CI, 0.29-0.77; P = 0.0005; I2 = 70.8%), volume (SMD, 0.18; 95% CI, 0.02-0.35; P = 0.0320; I2 = 82.3%), volume/ostium ratio (SMD, 0.43; 95% CI, 0.16-0.71; P = 0.0075; I2 = 90.4%), elongation (SMD, -0.94; 95% CI, -1.12 to -0.76; P = 0.0005; I2 = 0%), flatness (SMD, -0.87; 95% CI, -1.04 to -0.71; P = 0.0005; I2 = 0%), and sphericity (SMD, -0.62; 95% CI, -1.06 to -0.17; P = 0.0215; I2 = 67.9%). A significant risk of publication bias was estimated for the ellipticity index (P = 0.0360) and volume (P = 0.0030). CONCLUSIONS Based on the results of a meta-analysis containing 39 studies, the nonsphericity index, undulation index, elongation, flatness, and sphericity demonstrated the most consistent correlation with rupture status.
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Affiliation(s)
- Mark D Johnson
- College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA; Department of Neurosurgery, University of Cincinnati, Cincinnati, Ohio, USA.
| | - Paolo Palmisciano
- College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA; Department of Neurosurgery, University of Cincinnati, Cincinnati, Ohio, USA
| | - Ali S Yamani
- College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Samer S Hoz
- College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA; Department of Neurosurgery, University of Cincinnati, Cincinnati, Ohio, USA
| | - Charles J Prestigiacomo
- College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA; Department of Neurosurgery, University of Cincinnati, Cincinnati, Ohio, USA
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Cao H, Zeng H, Lv L, Wang Q, Ouyang H, Gui L, Hua P, Yang S. Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model. Front Physiol 2024; 15:1293380. [PMID: 38426204 PMCID: PMC10901972 DOI: 10.3389/fphys.2024.1293380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.
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Affiliation(s)
- Heshan Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hui Zeng
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lei Lv
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qi Wang
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hua Ouyang
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Long Gui
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ping Hua
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Songran Yang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Biobank and Bioinformatics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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MacDonald DE, Cancelliere NM, Pereira VM, Steinman DA. Sensitivity of hostile hemodynamics to aneurysm geometry via unsupervised shape interpolation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107762. [PMID: 37598472 DOI: 10.1016/j.cmpb.2023.107762] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/19/2023] [Accepted: 08/10/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND AND OBJECTIVE Vessel geometry and hemodynamics are intrinsically linked, whereby geometry determines hemodynamics, and hemodynamics influence vascular remodeling. Both have been used for testing clinical outcomes, but geometry/morphology generally has less uncertainty than hemodynamics derived from medical image-based computational fluid dynamics (CFD). To provide clinical utility, CFD-based hemodynamic parameters must be robust to modeling errors and/or uncertainties, but must also provide useful information not more-easily extracted from shape alone. The objective of this study was to methodically assess the response of hemodynamic parameters to gradual changes in shape created using an unsupervised 3D shape interpolation method. METHODS We trained the neural network NeuroMorph on 3 patient-derived intracranial aneurysm surfaces (labelled A, B, C), and then generated 3 distinct morph sequences (A→B, B→C, C→A) each containing 10 interpolated surfaces. From high-fidelity CFD simulation of these, we calculated a variety of common reduced hemodynamic parameters, including many previously associated with aneurysm rupture, and analyzed their responses to changes in shape, and their correlations. RESULTS The interpolated surfaces demonstrate complex, gradual changes in branch angles, vessel diameters, and aneurysm morphology. CFD simulation showed gradual changes in aneurysm jetting characteristics and wall-shear stress (WSS) patterns, but demonstrated a range of responses from the reduced hemodynamic parameters. Spatially and temporally averaged parameters including time-averaged WSS, time-averaged velocity, and low-shear area (LSA) showed low variation across all morph sequences, while parameters of flow complexity such as oscillatory shear, spectral broadening, and spectral bandedness indices showed high variation between slightly-altered neighboring surfaces. Correlation analysis revealed a great deal of mutual information with easier-to-measure shape-based parameters. CONCLUSIONS In the absence of large clinical datasets, unsupervised shape interpolation provides an ideal laboratory for exploring the delicate balance between robustness and sensitivity of nominal hemodynamic predictors of aneurysm rupture. Parameters like time-averaged WSS and LSA that are highly "robust" may, as a result, be effectively redundant to morphological predictors, whereas more sensitive parameters may be too uncertain for practical clinical use. Understanding these sensitivities may help identify parameters that are capable of providing added value to rupture risk assessment.
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Affiliation(s)
- Daniel E MacDonald
- Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, Ontario M5S 3G8, Canada
| | - Nicole M Cancelliere
- Department of Neurosurgery, St. Michael's Hospital, 36 Queen St E, Toronto, Ontario M5B 1W8, Canada
| | - Vitor M Pereira
- Department of Neurosurgery, St. Michael's Hospital, 36 Queen St E, Toronto, Ontario M5B 1W8, Canada
| | - David A Steinman
- Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, Ontario M5S 3G8, Canada.
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Hellmeier F, Brüning J, Berg P, Saalfeld S, Spuler A, Sandalcioglu IE, Beuing O, Larsen N, Schaller J, Goubergrits L. Geometric uncertainty in intracranial aneurysm rupture status discrimination: a two-site retrospective study. BMJ Open 2022; 12:e063051. [PMID: 36351732 PMCID: PMC9644336 DOI: 10.1136/bmjopen-2022-063051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVES Assessing the risk associated with unruptured intracranial aneurysms (IAs) is essential in clinical decision making. Several geometric risk parameters have been proposed for this purpose. However, performance of these parameters has been inconsistent. This study evaluates the performance and robustness of geometric risk parameters on two datasets and compare it to the uncertainty inherent in assessing these parameters and quantifies interparameter correlations. METHODS Two datasets containing 244 ruptured and unruptured IA geometries from 178 patients were retrospectively analysed. IAs were stratified by anatomical region, based on the PHASES score locations. 37 geometric risk parameters representing four groups (size, neck, non-dimensional, and curvature parameters) were assessed. Analysis included standardised absolute group differences (SADs) between ruptured and unruptured IAs, ratios of SAD to median relative uncertainty (MRU) associated with the parameters, and interparameter correlation. RESULTS The ratio of SAD to MRU was lower for higher dimensional size parameters (ie, areas and volumes) than for one-dimensional size parameters. Non-dimensional size parameters performed comparatively well with regard to SAD and MRU. SAD was higher in the posterior anatomical region. Correlation of parameters was strongest within parameter (sub)groups and between size and curvature parameters, while anatomical region did not strongly affect correlation patterns. CONCLUSION Non-dimensional parameters and few parameters from other groups were comparatively robust, suggesting that they might generalise better to other datasets. The data on discriminative performance and interparameter correlations presented in this study may aid in developing and choosing robust geometric parameters for use in rupture risk models.
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Affiliation(s)
- Florian Hellmeier
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Brüning
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Philipp Berg
- Laboratory of Fluid Dynamics and Technical Flows, University of Magdeburg, Magdeburg, Germany
- Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany
| | - Sylvia Saalfeld
- Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany
- Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany
| | | | | | - Oliver Beuing
- Department of Radiology, AMEOS Hospital Bernburg, Bernburg, Germany
| | - Naomi Larsen
- Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein (UKSH), Kiel, Germany
| | - Jens Schaller
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Leonid Goubergrits
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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Delucchi M, Spinner GR, Scutari M, Bijlenga P, Morel S, Friedrich CM, Furrer R, Hirsch S. Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. Comput Biol Med 2022; 147:105740. [DOI: 10.1016/j.compbiomed.2022.105740] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/24/2022] [Accepted: 06/11/2022] [Indexed: 11/24/2022]
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