CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

Osamah Alwalid; Xi Long; Mingfei Xie; Jiehua Yang; Chunyuan Cen; Huan Liu; Ping Han

doi:10.3389/fneur.2021.619864

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

Front Neurol. 2021 Feb 22:12:619864. doi: 10.3389/fneur.2021.619864. eCollection 2021.

Authors

Osamah Alwalid^{1

2}, Xi Long^{1

2}, Mingfei Xie^{1

2}, Jiehua Yang³, Chunyuan Cen^{1

2}, Huan Liu⁴, Ping Han^{1

2}

Affiliations

¹ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
³ School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
⁴ GE Healthcare, Shanghai, China.

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.

Keywords: aneurysm rupture; intracranial aneurysm; machine learning; radiomics; subarachnoid hemorrhage.