Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT.
Authors
Affiliations (9)
Affiliations (9)
- CRMBM-UMR CNRS 7339, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385, Marseille, Cedex 05, France. [email protected].
- Visual Computing, DTU Compute, Kongens Lyngby, Denmark. [email protected].
- Department of Vascular Surgery, Hôpital de la Timone, AP-HM, Marseille, France.
- Department of Radiology, Hôpital de la Timone, AP-HM, Marseille, France.
- Aix-Marseille School of Economics (AMSE), Aix-Marseille University, Marseille, France.
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France.
- ICMUB Laboratory, Faculty of Medicine, CNRS UMR 6302, University of Burgundy, Dijon, France.
- Medical Imaging Department, University Hospital of Dijon, Dijon, France.
- CRMBM-UMR CNRS 7339, Aix-Marseille University, 27, Boulevard Jean Moulin, 13385, Marseille, Cedex 05, France.
Abstract
1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter (D<sub>max</sub>) and volumes of aortic dissection components: true-lumen (TL), circulating false-lumen (CFL), and thrombus (Th) on CT angiography (CTA). 2. To assess the predictive value of these measures for adverse aortic remodeling in residual aortic dissection (RAD). This retrospective study included 322 patients from two centers. The segmentation model was trained on 120 patients (Center 1) and tested on an internal dataset (30 patients, Center 1) and an external dataset (10 patients, Center 2) in terms of Dice Similarity Coefficient (DSC). The model extracted D<sub>max</sub>, global false-lumen volume (FL<sub>Glo </sub>= CFL + Th), and local false-lumen volume (FL<sub>Loc</sub>, measured 3 cm around the largest diameter). Clinical validation was performed on 83 patients from Center1 (internal validation, 2-year follow-up) and 79 patients from Center2 (external validation, 4.5-year follow-up). The segmentation model achieved high accuracy (Center 1, DSC: 0.93 TL, 0.93 CFL, 0.87 Th; Center 2, DSC: 0.92 TL, 0.93 CFL, 0.84 Th) with strong agreement between automated and manual measurements. Aortic remodeling occurred in 39/83 patients (46.9%) from Center1 and 33/79 patients (41.7%) from Center2. Aortic remodeling occurred in 39/83 patients (47%) from Center1 and 33/80 (42%) from Center2. FL<sub>Loc</sub> outperformed D<sub>max</sub> and FLGlo (Center 1: AUC = 0.83, 0.73, and 0.76; Center 2: AUC = 0.77, 0.64, and 0.70). At optimal thresholds, FL<sub>Loc</sub> showed good predictive performance (Center 1: Sensitivity = 0.87, Specificity = 0.68). Deep-learning segmentation provides accurate aortic measurements. Local false-lumen volumes predict adverse aortic remodeling in RAD better than diameter and global false-lumen volumes. Question In residual aortic dissection (RAD) after type-A dissection, early identification of high-risk patients on initial CT angiography is crucial for endovascular treatment decisions. Findings False-lumen local volumes (3 cm around aortic dissection maximal diameters), obtained with an automatic deep-learning method, predict adverse remodeling better than diameter or global false-lumen volumes. Clinical relevance A deep-learning segmentation method of aortic dissection components on CTA, enabling automatic measurements of diameters and volumes is feasible. It provides local false-lumen volumes, a better predictive marker of adverse aortic remodeling than the currently used diameters and global volumes.