A radiomics model based on aortic computed tomography angiography: the impact on predicting the prognosis of patients with aortic intramural hematoma (IMH).

Background: The prognosis of aortic intramural hematoma (IMH) is unpredictable, but computed tomography angiography (CTA) plays an important role of high diagnostic performance in the initial diagnosis and during follow-up of patients. In this study, we investigated the value of a radiomics model based on aortic CTA for predicting the prognosis of patients with medically treated IMH.

Method: A total of 120 patients with IMH were enrolled in this study. The follow-up duration ranged from 32 to 1,346 days (median 232 days). Progression of these patients was classified as follows: destabilization, which refers to deterioration in the aortic condition, including significant increases in the thickness of the IMH, the progression of IMH to a penetrating aortic ulcer (PAU), aortic dissection (AD), or rupture; or stabilization, which refers to an unchanged appearance or a decrease in the size or disappearance of the IMH. The patients were divided into a training cohort (n=84) and a validation cohort (n=36). Six different machine learning classifiers were applied: random forest (RF), K-nearest neighbor (KNN), Gaussian Naive Bayes, decision tree, logistic regression, and support vector machine (SVM). The clinical-radiomics combined nomogram model was established by multivariate logistic regression. The area under the receiver operating characteristic (ROC) curve (AUC) was implemented to evaluate the discrimination performance of the models. The calibration curves and Hosmer-Lemeshow test were used for evaluating model calibration. DeLong's test was performed to compare the AUC performance of models.

Results: Among all of the patients, 60 patients showed destabilization and 60 patients remained stable. A total of 12 radiomic features were retained after application of the least absolute shrinkage and selection operator (LASSO). These features were used for the machine learning model construction. The SVM-radial basis function (SVM-RBF) model obtained the best performance with an AUC of 0.765 (95% CI, 0.593-0.906). In the validation cohort, the combined clinical-radiomics model [AUC =0.787; 95% confidence interval (CI), 0.619-0.923] showed a significantly higher performance than did the clinical model (AUC =0.596; 95% CI, 0.413-0.796; P=0.021) and had a similar performance to the radiomics model (AUC =0.765; 95% CI, 0.589-0.906; P=0.672).

Conclusions: A quantitative nomogram based on radiomic features of CTA images can be used to predict disease progression in patients with IMH and may help improve clinical decision-making.