CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

Frontiers in Neurology

Huan Liu²  Xi Long³  Osamah Alwalid³  Chunyuan Cen³  Ping Han³  Mingfei Xie³  Jiehua Yang⁴ 

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

关键词: intracranial aneurysm;    aneurysm rupture;    subarachnoid hemorrhage;    machine learning;    radiomics;   

DOI  :  10.3389/fneur.2021.619864

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown