Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: a multicenter study

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.

Automated summary

A radiomic signature based on non-contrast CT images can effectively predict thoracic aortic dissections, serving as a potential screening tool for this condition. The predictive ability of the radiomic signature was demonstrated through high AUC values and good agreement with the probability of thoracic AD.