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An X, He J, Di Y, Wang M, Luo B, Huang Y, Ming D. Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion. Front Neurosci 2022; 16:813056. [PMID: 35250455 PMCID: PMC8893318 DOI: 10.3389/fnins.2022.813056] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/05/2022] [Indexed: 12/25/2022] Open
Abstract
The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.
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Affiliation(s)
- Xingwei An
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Center for Brain Science, Tianjin, China
| | - Jiaqian He
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yang Di
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Miao Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Bin Luo
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Neurosurgery, Huanhu Hospital of Tianjin University, Tianjin, China
| | - Ying Huang
- Department of Neurosurgery, Huanhu Hospital of Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Center for Brain Science, Tianjin, China
- Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- *Correspondence: Dong Ming,
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Ropp A, Frazier AA, Gelfand B, Jeudy J. The Dean Effect: An Aortic Arch Flow Artifact Mimicking Dissection. Radiol Cardiothorac Imaging 2022; 4:e210229. [PMID: 35782762 PMCID: PMC8893211 DOI: 10.1148/ryct.210229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/13/2021] [Accepted: 11/29/2021] [Indexed: 06/15/2023]
Abstract
The unique hemodynamics of the aortic arch create conditions for potential formation of a flow-related artifact that mimics disease on CT angiographic images. The hemodynamic basis for this artifact can be explained by fluid mechanics incorporating a mathematical principle known as the Dean number. Therefore, in this review, the artifact is referred to as the Dean effect. It is important for radiologists and other clinicians to recognize this artifact when encountered. It is also helpful for the interpreting radiologist to have a basic understanding of the relevant hemodynamic principles. This review provides an example of the artifact, reviews the basic underlying hemodynamics, and presents methods of how to prevent this artifact and distinguish it from pathologic mimics in clinical practice. Keywords: CT Angiography, Vascular, Thorax, Aorta, Artifacts, Blood, Dissection, Hemodynamics/Flow Dynamics © RSNA, 2022.
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Zhu D, Chen Y, Zheng K, Chen C, Li Q, Zhou J, Jia X, Xia N, Wang H, Lin B, Ni Y, Pang P, Yang Y. Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study. Front Neurosci 2021; 15:721268. [PMID: 34456680 PMCID: PMC8385786 DOI: 10.3389/fnins.2021.721268] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 07/26/2021] [Indexed: 01/08/2023] Open
Abstract
Objective Radiomics and morphological features were associated with aneurysms rupture. However, the multicentral study of their predictive power for specific-located aneurysms rupture is rare. We aimed to determine robust radiomics features related to middle cerebral artery (MCA) aneurysms rupture and evaluate the additional value of combining morphological and radiomics features in the classification of ruptured MCA aneurysms. Methods A total of 632 patients with 668 MCA aneurysms (423 ruptured aneurysms) from five hospitals were included. Radiomics and morphological features of aneurysms were extracted on computed tomography angiography images. The model was developed using a training dataset (407 patients) and validated with the internal (152 patients) and external validation (73 patients) datasets. The support vector machine method was applied for model construction. Optimal radiomics, morphological, and clinical features were used to develop the radiomics model (R-model), morphological model (M-model), radiomics-morphological model (RM-model), clinical-morphological model (CM-model), and clinical-radiomics-morphological model (CRM-model), respectively. A comprehensive nomogram integrating clinical, morphological, and radiomics predictors was generated. Results We found seven radiomics features and four morphological predictors of MCA aneurysms rupture. The R-model obtained an area under the receiver operating curve (AUC) of 0.822 (95% CI, 0.776, 0.867), 0.817 (95% CI, 0.744, 0.890), and 0.691 (95% CI, 0.567, 0.816) in the training, temporal validation, and external validation datasets, respectively. The RM-model showed an AUC of 0.848 (95% CI, 0.810, 0.885), 0.865 (95% CI, 0.807, 0.924), and 0.721 (95% CI, 0.601, 0.841) in the three datasets. The CRM-model obtained an AUC of 0.856 (95% CI, 0.820, 0.892), 0.882 (95% CI, 0.828, 0.936), and 0.738 (95% CI, 0.618, 0.857) in the three datasets. The CRM-model and RM-model outperformed the CM-model and M-model in the internal datasets (p < 0.05), respectively. But these differences were not statistically significant in the external dataset. Decision curve analysis indicated that the CRM-model obtained the highest net benefit for most of the threshold probabilities. Conclusion Robust radiomics features were determined related to MCA aneurysm rupture. The RM-model exhibited good ability in classifying ruptured MCA aneurysms. Integrating radiomics features into conventional models might provide additional value in ruptured MCA aneurysms classification.
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Affiliation(s)
- Dongqin Zhu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yongchun Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kuikui Zheng
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Chao Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiong Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Radiology, Wenzhou Central Hospital, Wenzhou, China
| | - Jiafeng Zhou
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiufen Jia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Nengzhi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Hao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Boli Lin
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yifei Ni
- The First School of Medicine, Wenzhou Medical University, Wenzhou, China
| | - Peipei Pang
- GE Healthcare China Co., Ltd., Shanghai, China
| | - Yunjun Yang
- Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Imaging Predictive Factors of Abdominal Aortic Aneurysm Growth. J Clin Med 2021; 10:jcm10091917. [PMID: 33925046 PMCID: PMC8124923 DOI: 10.3390/jcm10091917] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 01/14/2023] Open
Abstract
Background: Variable imaging methods may add important information about abdominal aortic aneurysm (AAA) progression. The aim of this study is to assess available literature data regarding the predictive imaging factors of AAA growth. Methods: This systematic review was conducted using the PRISMA guidelines. A review of the literature was conducted, using PubMed, EMBASE and CENTRAL databases. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Primary outcomes were defined as AAA growth rate and factors associated to sac expansion. Results: The analysis included 23 studies. All patients (2244; mean age; 69.8 years, males; 85%) underwent imaging with different modalities; the initial evaluation was followed by one or more studies to assess aortic expansion. AAA initial diameter was reported in 13 studies (range 19.9–50.9 mm). Mean follow-up was 34.5 months. AAA diameter at the end was ranging between 20.3 and 55 mm. The initial diameter and intraluminal thrombus were characterized as prognostic factors associated to aneurysm expansion. A negative association between atherosclerosis and AAA expansion was documented. Conclusions: Aneurysm diameter is the most studied factor to be associated with expansion and the main indication for intervention. Appropriate diagnostic modalities may account for different anatomical characteristics and identify aneurysms with rapid growth and higher rupture risk. Future perspectives, including computed mathematical models that will assess wall stress and elasticity and further flow characteristics, may offer valuable alternatives in AAA growth prediction.
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Ou C, Chong W, Duan CZ, Zhang X, Morgan M, Qian Y. A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms. Eur Radiol 2020; 31:2716-2725. [PMID: 33052466 DOI: 10.1007/s00330-020-07325-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 08/07/2020] [Accepted: 09/18/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture. METHODS One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use. RESULTS Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876. CONCLUSIONS Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance. KEY POINTS • Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms.
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Affiliation(s)
- Chubin Ou
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
- 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, Guangdong, China
| | - Winston Chong
- Monash Medical Centre, Monash University, Clayton, Victoria, Australia
| | - Chuan-Zhi 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, Guangdong, China
| | - Xin Zhang
- 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, Guangdong, China
| | - Michael Morgan
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Yi Qian
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia.
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Common First-Pass CT Angiography Findings Associated With Rapid Growth Rate in Abdominal Aorta Aneurysms Between 3 and 5 cm in Largest Diameter. AJR Am J Roentgenol 2018; 210:431-437. [DOI: 10.2214/ajr.17.18094] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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