Abstract
Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.
Highlights
The incidence of intracranial aneurysm is ∼3% in the adult population at a mean age of 50 years [1, 2]
This study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a classification model on CT angiography, which may provide a basis for automated diagnosis of aneurysm rupture
CT angiography (CTA) imaging data of the patients with intracranial aneurysm diagnosed between May 2016 and April 2019 were collected from Wuhan Union Hospital and Union West Hospital
Summary
The incidence of intracranial aneurysm is ∼3% in the adult population at a mean age of 50 years [1, 2]. Aneurysms are responsible for about 80–90% of subarachnoid hemorrhages (SAH), with a resultant mortality rate of 23–51%, and a permanent disability risk of 10–20% [3, 4]. Proper prevention is essential to reducing the risk of aneurysm rupture in the majority of cases, and timely management in case of rupture is critical for reducing the complications and preventing re-bleeding [2, 5, 6]. Compared with digital subtraction angiography, which is the gold standard for diagnosing intracranial aneurysms, CT angiography is noninvasive and more widely available [6, 9]. Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture
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