Deep-learning derived local false-lumen volume predicted adverse aortic remodeling better than maximal aortic diameter (AUC 0.83 vs 0.73) and global false-lumen volume.
Observational (n=322)
Yes
Does deep learning-derived local false-lumen volume predict adverse aortic remodeling better than maximal aortic diameter or global false-lumen volume in patients with residual aortic dissection?
Deep learning-derived local false-lumen volume is a superior predictor of adverse aortic remodeling compared to standard maximal diameter in patients with residual aortic dissection.
Effect estimate: AUC 0.83
Abstract Objectives 1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter ( D max ) 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). Materials and methods 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 max , global false-lumen volume (FL Glo = CFL + Th), and local false-lumen volume (FL Loc , 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). Results 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 Loc outperformed D max 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 Loc showed good predictive performance (Center 1: Sensitivity = 0.87, Specificity = 0.68). Conclusion 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. Key Points 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 . Graphical Abstract
Fournel et al. (Fri,) conducted a observational in Residual aortic dissection (n=322). Deep-learning derived local false-lumen volume (FLLoc) vs. Maximal aortic diameter (Dmax) and global false-lumen volume (FLGlo) was evaluated on Adverse aortic remodeling (AUC 0.83). Deep-learning derived local false-lumen volume predicted adverse aortic remodeling better than maximal aortic diameter (AUC 0.83 vs 0.73) and global false-lumen volume.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: