Objectives 1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter (Dmax) 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 Dmax, global false-lumen volume (FLGlo = CFL + Th), and local false-lumen volume (FLLoc, 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. FLLoc outperformed Dmax 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, FLLoc 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 <Emphasis Type="BoldItalic">Question</Emphasis> 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. <Emphasis Type="BoldItalic">Findings</Emphasis> 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. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> 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

Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT

期刊论文
出版模式：

开放获取
发表日期：

2025年11月7日

查看全文

Joris Fournel,
Mariangela De Masi,
Charlotte Lu,
Virgile Omnes,
Baptiste Muselier,
Badih Ghattas,
Olivier Bouchot,
Moundji Kafi,
Alain Lalande,
Marine Gaudry,
Alexis Jacquier,
Axel Bartoli

摘要

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