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

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.

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Publication Details

Journal
European Radiology
Published
2025-11-07
DOI
https://doi.org/10.1007/s00330-025-12116-9
Primary Topic
Aortic Disease and Treatment Approaches
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Joris Fournel, Marine Gaudry, Charlotte Lu, Alain Lalande et al.
European Radiology
Aortic Disease and Treatment Approaches
article

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

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

Abstract

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.

European Radiology
Centre National de la Recherche Scientifique (FR), Université de Bourgogne (FR), Aix-Marseille Université (FR), Danish Geotechnical Society (DK), Maison des Sciences sociales et des Humanités de Dijon (FR), Instituttet for Produktudvikling (Denmark) (DK), Hôpital de la Timone (FR), Technical University of Denmark (DK)
Novo Nordisk, Danmarks Tekniske Universitet, Novo Nordisk Fonden
Openalex Percentile: Top 52%
Aortic Disease and Treatment Approaches
DOIOpen Access PDFOpenAlex
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