Yifan Guo1,2, Xiaojun Chen3, Xianda Lin4, Litian Chen5, Jiner Shu3, Peipei Pang6, Jianmin Cheng5, Maosheng Xu7,8, Zhichao Sun9,10. 1. Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China. 2. The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China. 3. Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, 365 Renmin East Road, Jinhua, 321000, China. 4. Department of Neurology, The Wenzhou Third Clinical Institute Affiliated To Wenzhou Medical University, 299 Gu'an Road, Wenzhou, 325000, China. 5. Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, 109 Xueyuan West Road, Wenzhou, 325000, China. 6. Department of Pharmaceuticals Diagnosis, GE Healthcare, 122 Shuguang Road, Hangzhou, 310000, China. 7. Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China. xums166@zcmu.edu.cn. 8. The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China. xums166@zcmu.edu.cn. 9. Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China. sunzhichao@zcmu.edu.cn. 10. The First Clinical Medical College of Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 310000, China. sunzhichao@zcmu.edu.cn.
Abstract
OBJECTIVE: To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs). METHODS: We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature. RESULTS: The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86-0.95) in the training set, 0.92 (95% CI, 0.86-0.98) in the validation set, and 0.90 (95% CI, 0.82-0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively. CONCLUSIONS: A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs. KEY POINTS: • The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections. • This radiomic signature shows better predictive performance compared to the current clinical model. • This prediction method may be a potential tool for screening thoracic aortic dissections.
OBJECTIVE: To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs). METHODS: We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature. RESULTS: The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86-0.95) in the training set, 0.92 (95% CI, 0.86-0.98) in the validation set, and 0.90 (95% CI, 0.82-0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively. CONCLUSIONS: A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs. KEY POINTS: • The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections. • This radiomic signature shows better predictive performance compared to the current clinical model. • This prediction method may be a potential tool for screening thoracic aortic dissections.