In Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
PURPOSE : This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiography (CTA).
METHODS : A total of 147 patients with acute or subacute TBAD who underwent proximal TEVAR at a single center were retrospectively reviewed. The boundary of aorta was manually segmented, and the point clouds of each aorta were obtained. Prediction of negative aortic remodeling or reintervention was accomplished by a convolutional neural network (CNN) and a point cloud neural network (PC-NN), respectively. The discriminatory value of the established models was mainly evaluated by the area under the receiver operating characteristic curve (AUC) in the test set.
RESULTS : The mean follow-up time was 34.0 months (range: 12-108 months). During follow-up, a total of 25 (17.0%) patients were identified as having negative aortic remodeling, and 16 (10.9%) patients received reintervention. The AUC (0.876) by PC-NN for predicting negative aortic remodeling was superior to that obtained by CNN (0.612, p=0.034) and similar to the AUC by PC-NN combined with clinical features (0.884, p=0.92). As to reintervention, the AUC by PC-NN was significantly higher than that by CNN (0.805 vs 0.579; p=0.042), and AUCs by PC-NN combined with clinical features and PC-NN alone were comparable (0.836 vs 0.805; p=0.81).
CONCLUSION : The CTA-based deep learning algorithms may assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.
CLINICAL IMPACT : Negative aortic remodeling is the leading cause of late reintervention after proximal thoracic endovascular aortic repair (TEVAR) for Stanford type B aortic dissection (TBAD), and possesses great challenge to endovascular repair. Early recognizing high-risk patients is of supreme importance for optimizing the follow-up interval and therapy strategy. Currently, clinicians predict the prognosis of these patients based on several imaging signs, which is subjective. The computed tomography angiography-based deep learning algorithms may incorporate abundant morphological information of aorta, provide with a definite and objective output value, and finally assist clinicians in automated prediction of distal aortic remodeling after TEVAR for acute or subacute TBAD.
Zhou Min, Luo Xiaoyuan, Wang Xia, Xie Tianchen, Wang Yonggang, Shi Zhenyu, Wang Manning, Fu Weiguo
2023-Mar-16
aortic dissection, aortic remodeling, computed tomography angiography, deep learning
