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Yan Y, An X, Ren H, Luo B, Jin S, Liu L, Di Y, Li T, Huang Y. Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms. Neurosurg Rev 2024; 47:169. [PMID: 38635054 DOI: 10.1007/s10143-024-02408-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/30/2024] [Accepted: 04/09/2024] [Indexed: 04/19/2024]
Abstract
Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.
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Affiliation(s)
- Yujia Yan
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Xingwei An
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Hecheng Ren
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Bin Luo
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China
| | - Song Jin
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Li Liu
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Yang Di
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Tingting Li
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China
- State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China
| | - Ying Huang
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
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Yang H, Cho KC, Hong I, Kim Y, Kim YB, Kim JJ, Oh JH. Influence of circle of Willis modeling on hemodynamic parameters in anterior communicating artery aneurysms and recommendations for model selection. Sci Rep 2024; 14:8476. [PMID: 38605063 PMCID: PMC11009257 DOI: 10.1038/s41598-024-59042-2] [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: 01/11/2024] [Accepted: 04/04/2024] [Indexed: 04/13/2024] Open
Abstract
Computational fluid dynamics (CFD) has been utilized to calculate hemodynamic parameters in anterior communicating artery aneurysm (AComA), which is located at a junction between left and right A1 and A2 segments. However, complete or half circle of Willis (CoW) models are used indiscriminately. This study aims to suggest recommendations for determining suitable CoW model. Five patient-specific CoW models with AComA were used, and each model was divided into complete, left-half, and right-half models. After validating the CFD using a flow experiment, the hemodynamic parameters and flow patterns in five AComAs were compared. In four out of five cases, inflow from one A1 side had a dominant influence on the AComA, while both left and right A1 sides affected the AComA in the remaining case. Also, the average difference in time-averaged wall shear stress between the complete and half models for four cases was 4.6%, but it was 62% in the other case. The differences in the vascular resistances of left and right A1 and A2 segments greatly influenced the flow patterns in the AComA. These results may help to enhance clinicians' understanding of blood flow in the brain, leading to improvements in diagnosis and treatment of cerebral aneurysms.
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Affiliation(s)
- Hyeondong Yang
- Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-Ro, Sangnok-Gu, Ansan, 15588, Gyeonggi-Do, Korea
| | - Kwang-Chun Cho
- Department of Neurosurgery, College of Medicine, Yonsei University, Yongin Severance Hospital, Yongin, Gyeonggi-Do, Korea
| | - Ineui Hong
- Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-Ro, Sangnok-Gu, Ansan, 15588, Gyeonggi-Do, Korea
| | - Yeonwoo Kim
- Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-Ro, Sangnok-Gu, Ansan, 15588, Gyeonggi-Do, Korea
| | - Yong Bae Kim
- Department of Neurosurgery, College of Medicine, Yonsei University, Severance Hospital, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Korea
| | - Jung-Jae Kim
- Department of Neurosurgery, College of Medicine, Yonsei University, Severance Hospital, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Korea.
- Department of Anatomy, Graduate School of Medicine, Korea University, 13 Jongam-Ro, Seongbuk-Gu, Seoul, 02841, Korea.
| | - Je Hoon Oh
- Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-Ro, Sangnok-Gu, Ansan, 15588, Gyeonggi-Do, Korea.
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Habibi MA, Fakhfouri A, Mirjani MS, Razavi A, Mortezaei A, Soleimani Y, Lotfi S, Arabi S, Heidaresfahani L, Sadeghi S, Minaee P, Eazi S, Rashidi F, Shafizadeh M, Majidi S. Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants. Neurosurg Rev 2024; 47:34. [PMID: 38183490 DOI: 10.1007/s10143-023-02271-2] [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: 11/15/2023] [Revised: 12/08/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.
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Affiliation(s)
- Mohammad Amin Habibi
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
| | - Amirata Fakhfouri
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Mohammad Sina Mirjani
- Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Alireza Razavi
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ali Mortezaei
- Student Research Committee, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Yasna Soleimani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sohrab Lotfi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Shayan Arabi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Ladan Heidaresfahani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sara Sadeghi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Poriya Minaee
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - SeyedMohammad Eazi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Farhang Rashidi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Shafizadeh
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Shahram Majidi
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
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Yang H, Kim JJ, Kim YB, Cho KC, Oh JH. Investigation of paraclinoid aneurysm formation by comparing the combined influence of hemodynamic parameters between aneurysmal and non-aneurysmal arteries. J Cereb Blood Flow Metab 2023:271678X231218589. [PMID: 38051823 DOI: 10.1177/0271678x231218589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Numerous studies have evaluated the effects of hemodynamic parameters on aneurysm formation. However, the reasons why aneurysms do not initiate in intracranial arteries are still unclear. This study aimed to investigate the influence of hemodynamic parameters, wall shear stress (WSS) and strain, on aneurysm formation by comparing between aneurysmal and non-aneurysmal arteries. Fifty-eight patients with paraclinoid aneurysms on one side were enrolled. Based on magnetic resonance angiography, each patient's left and right internal carotid arteries (ICAs) were reconstructed. For a patient having an aneurysm on one side, the ICA with the paraclinoid aneurysm was defined as the aneurysmal artery after eliminating the aneurysm, whereas the opposite ICA without aneurysm was defined as the non-aneurysmal artery. Computational fluid dynamics and fluid-structure interaction analyses were then performed for both aneurysmal and non-aneurysmal arteries. Finally, the relationship between high hemodynamic parameters and aneurysm location was investigated. For aneurysmal arteries, high WSS and strain locations were well-matched with the aneurysm formation site. Also, considerable correlations between high WSS and strain locations were observed. However, there was no significant relationship between high hemodynamic parameters and aneurysm formation for non-aneurysmal arteries. The findings are helpful for understanding aneurysm formation mechanism and encouraging further relevant research.
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Affiliation(s)
- Hyeondong Yang
- Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan, Korea
| | - Jung-Jae Kim
- Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yong Bae Kim
- Department of Neurosurgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kwang-Chun Cho
- Department of Neurosurgery, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Je Hoon Oh
- Department of Mechanical Engineering and BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan, Korea
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Influence of blood viscosity models and boundary conditions on the computation of hemodynamic parameters in cerebral aneurysms using computational fluid dynamics. Acta Neurochir (Wien) 2023; 165:471-482. [PMID: 36624234 DOI: 10.1007/s00701-022-05467-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Computational fluid dynamics (CFD) is widely used to calculate hemodynamic parameters that are known to influence cerebral aneurysms. However, the boundary conditions for CFD are chosen without any specific criteria. Our objective is to establish the recommendations for setting the analysis conditions for CFD analysis of the cerebral aneurysm. METHOD The plug and the Womersley flow were the inlet boundary conditions, and zero and pulsatile pressures were the outlet boundary conditions. In addition, the difference in the assumption of viscosity was analyzed with respect to the flow rate. The CFD process used in our research was validated using particle image velocimetry experiment data from Tupin et al.'s work to ensure the accuracy of the simulations. RESULTS It was confirmed that if the entrance length was sufficiently secured, the inlet and outlet boundary conditions did not affect the CFD results. In addition, it was observed that the difference in the hemodynamic parameter between Newtonian and non-Newtonian fluid decreased as the flow rate increased. Furthermore, it was confirmed that similar tendencies were evaluated when these recommendations were utilized in the patient-specific cerebral aneurysm models. CONCLUSIONS These results may help conduct standardized CFD analyses regardless of the research group.
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