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A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine
Mostafa Zakeri1,2, Amirhossein Atef1, Mohammad Aziznia1
1CNNFM Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, 1450 Kargar St. N., Tehran, 14399-57131, Iran.
Insights
Predicting cerebral aneurysm rupture is crucial for patient outcomes. Machine learning models, particularly Support Vector Machines, show high accuracy and recall in identifying rupture risk using clinical and novel morphological features.
Area of Science:
- Neurosurgery and Medical Imaging
- Biomedical Engineering
- Data Science in Healthcare
Background:
- Cerebral aneurysms affect a significant global population, with rupture leading to severe consequences.
- Accurate prediction of aneurysm rupture is challenging for physicians using traditional methods.
- Early and precise diagnosis guides critical treatment decisions.
Purpose of the Study:
- To predict the rupture status of cerebral aneurysms using machine learning.
- To leverage clinical and morphological data from 3D geometries for improved prediction.
- To assist physicians in making confident treatment choices and potentially saving lives.
Main Methods:
- Utilized a dataset of 708 cerebral aneurysms with 3 clinical and 35 morphological features.
- Developed and trained 5 classification machine learning algorithms.
- Introduced 8 novel morphological features, including ideal roundness, neck circumference, and outlet numbers.
Main Results:
- Models achieved high performance with accuracy (0.76-0.82) and precision (0.79-0.83).
- Prioritizing recall due to the critical nature of the condition, models achieved recall scores from 0.85 to 0.92.
- Support Vector Machine demonstrated superior performance with 0.82 accuracy, 0.92 recall, and 0.84 AUC.
Conclusions:
- Machine learning models can significantly enhance the prediction of cerebral aneurysm rupture.
- Key morphological features like ellipticity index, size ratio, shape irregularity, ideal roundness, neck circumference, and outlet numbers are pivotal predictors.
- This approach offers valuable insights for informed clinical decision-making in aneurysm management.
Abstract:
Cerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatment becomes evident upon diagnosis, as the severity of the disease guides the course of action. Cerebral aneurysms are particularly vulnerable in the circle of Willis and pose a significant concern due to the potential for rupture, which can lead to irreversible consequences, including fatality. The primary objective of this study is to predict the rupture status of cerebral aneurysms. To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. Diagnosing and predicting aneurysm rupture based solely on brain scans is a significant challenge with limited reliability, even for experienced physicians. However, by employing statistical methods and machine learning techniques, we can assist physicians in making more confident predictions regarding rupture likelihood and selecting appropriate treatment strategies. To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. The dataset comprised 3 clinical features and 35 morphological parameters, including 8 novel morphological features introduced for the first time in this study. Our models demonstrated exceptional performance in predicting cerebral aneurysm rupture, with accuracy ranging from 0.76 to 0.82 and precision score from 0.79 to 0.83 for the test dataset. As the data are sensitive and the condition is critical, recall is prioritized as the more crucial parameter over accuracy and precision, and our models achieved outstanding recall score ranging from 0.85 to 0.92. Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. The ellipticity index, size ratio, and shape irregularity are pivotal features in predicting aneurysm rupture, respectively, contributing significantly to our understanding of this complex condition. Among the multitude of parameters under investigation, these are particularly important. In this study, the ideal roundness parameter was introduced as a novel consideration and ranked fifth among all 38 parameters. Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.
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