Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection

Xuefang Lu; Wei Gong; Wenbing Yang; Zhoufeng Peng; Chao Zheng; Yunfei Zha

doi:10.1016/j.ejrad.2024.111468

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection

Eur J Radiol. 2024 Apr 15:175:111468. doi: 10.1016/j.ejrad.2024.111468. Online ahead of print.

Authors

Xuefang Lu¹, Wei Gong¹, Wenbing Yang¹, Zhoufeng Peng¹, Chao Zheng², Yunfei Zha³

Affiliations

¹ Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
² Shukun Technology Co., Ltd, Beichen Century Center, West Beichen Road, 100102 Beijing, China.
³ Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China. Electronic address: zhayunfei999@126.com.

PMID: 38648727
DOI: 10.1016/j.ejrad.2024.111468

Abstract

Purpose: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR).

Methods: We retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images. Feature selection, using Analysis of Variance (ANOVA) and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, refined a radiomic score (Rad-Score). This score, alongside clinical parameters, was modelled via Extreme Gradient Boosting (XGBoost) analysis. Model calibration was assessed by calibration curves.

Results: The integration of the Rad-Score with clinical factors including albumin and C-reactive protein levels moderately enhanced predictive efficiency, exhibiting an area under the curve (AUC) of 1.000 (95%CI, 1.000-1.000) in the training cohort and 0.990 (95%CI, 0.966-1.000) in the internal validation cohort. In an independent validation cohort from another hospital, the combined model yielded an AUC of 0.985 (95%CI, 0.965-1.000), with an accuracy, precision, sensitivity, and specificity of 0.92, 0.92, 0.94, and 0.91, respectively.

Conclusions: The synergistic application of deep learning-based radiomics from CTA and clinical indicators holds promise for anticipating AEs post-initial thoracic endovascular aortic repair in patients with acute uTBAD. The clinical utility of the constructed combined model, offering prognostic foresight during follow-up, has been substantiated.

Keywords: Aortic Dissection; Computed Tomography Angiography; Deep Learning; Radiomics.