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Published on: July 28, 2018
Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on
Xin Cao1,2, Yanwei Zeng1,2, Junying Wang3
1Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China.
Insights
A new radiomic model using vessel wall MRI effectively distinguishes cerebral dissecting aneurysms from hemorrhagic saccular aneurysms. This AI-driven approach offers superior diagnostic accuracy compared to traditional methods and expert radiologists, aiding surgical planning.
Area of Science:
- Neuroradiology
- Medical Imaging
- Artificial Intelligence
Background:
- Distinguishing cerebral dissecting aneurysms (DA) from hemorrhagic saccular aneurysms (SA) is crucial for surgical planning, often relying on intraoperative findings.
- Improved non-invasive diagnostic methods are needed to differentiate these conditions preoperatively.
Purpose of the Study:
- To develop and validate a radiomic model using high-resolution vessel wall magnetic resonance imaging (VW-MRI) and machine learning to differentiate DA from hemorrhagic SA.
- To compare the diagnostic performance of the radiomic model against clinico-radiological models and experienced radiologists.
Main Methods:
- Retrospective analysis of 851 radiomic features from 146 patients.
- Development of a radiomic model using the ElasticNet algorithm on a training set (77 cases).
- Creation of clinico-radiological and integrated models; validation on an external test set (69 cases).
Main Results:
- The radiomic model achieved an AUC of 0.831, outperforming the clinico-radiological model (AUC = 0.717) and the integrated model (AUC = 0.813).
- The radiomic model demonstrated superior diagnostic performance (AUC = 0.831) compared to experienced radiologists (AUC = 0.801).
- Eight key radiomic features were identified for the diagnostic model.
Conclusions:
- A radiomic model based on VW-MRI reliably distinguishes between cerebral dissecting aneurysms and hemorrhagic saccular aneurysms.
- This AI-powered imaging approach offers a valuable non-invasive tool for preoperative diagnosis and surgical strategy formulation.
- The developed radiomic model shows potential for widespread clinical application in neurovascular diagnostics.
Abstract:
The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.
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