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Liao J, Misaki K, Sakamoto J. Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization. Cardiovasc Eng Technol 2024:10.1007/s13239-024-00721-6. [PMID: 38782877 DOI: 10.1007/s13239-024-00721-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 02/04/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms. METHODS We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets. RESULTS The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000). CONCLUSION Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient.
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Affiliation(s)
- Jing Liao
- Division of Transdisciplinary Sciences, Graduate School of Frontier Science Initiative, Kanazawa University, Ishikawa, Japan.
| | - Kouichi Misaki
- Department of Neurosurgery, Kanazawa University, Ishikawa, Japan
| | - Jiro Sakamoto
- Division of Mechanical Science and Engineering, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Ishikawa, Japan
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Wen Z, Wang Y, Zhong Y, Hu Y, Yang C, Peng Y, Zhan X, Zhou P, Zeng Z. Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images. Front Neurol 2024; 15:1391382. [PMID: 38694771 PMCID: PMC11061371 DOI: 10.3389/fneur.2024.1391382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.
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Affiliation(s)
- Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
| | - Yuxin Zhong
- School of Nursing, Guizhou Medical University, Guiyang, China
| | - Yiheng Hu
- Department of Medical Imaging, Southwest Medical University, Luzhou, China
| | - Cheng Yang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Yan Peng
- Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiang Zhan
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, School of Nursing, Southwest Medical University, Luzhou, China
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zhen Zeng
- Psychiatry Department, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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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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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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Tian Z, Li W, Feng X, Sun K, Duan C. Prediction and analysis of periprocedural complications associated with endovascular treatment for unruptured intracranial aneurysms using machine learning. Front Neurol 2022; 13:1027557. [PMID: 36313499 PMCID: PMC9596813 DOI: 10.3389/fneur.2022.1027557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background The management of unruptured intracranial aneurysm (UIA) remains controversial. Recently, machine learning has been widely applied in the field of medicine. This study developed predictive models using machine learning to investigate periprocedural complications associated with endovascular procedures for UIA. Methods We enrolled patients with solitary UIA who underwent endovascular procedures. Periprocedural complications were defined as neurological adverse events resulting from endovascular procedures. We incorporated three machine learning algorithms into our prediction models: artificial neural networks (ANN), random forest (RF), and logistic regression (LR). The Shapley Additive Explanations (SHAP) approach and feature importance analysis were used to identify and prioritize significant features associated with periprocedural complications. Results In total, 443 patients were included. Forty-eight (10.83%) procedure-related complications occurred. In the testing set, the ANN model produced the largest value (0.761) for area under the curve (AUC). The RF model also achieved an acceptable AUC value of 0.735, while the AUC value of the LR model was 0.668. SHAP and feature importance analysis identified distal aneurysm, aneurysm size and treatment modality as most significant features for the prediction of periprocedural complications following endovascular treatment for UIA. Conclusion Periprocedural complications after endovascular treatment for UIA are not negligible. Prediction of periprocedural complications via machine learning is feasible and effective. Machine learning can serve as a promising tool in the decision-making process for UIA treatment.
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Affiliation(s)
- Zhongbin Tian
- National Key Clinical Specialty, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Wenqiang Li
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Feng
- National Key Clinical Specialty, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Kaijian Sun
- National Key Clinical Specialty, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chuanzhi Duan
- National Key Clinical Specialty, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Neurosurgery Institute, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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