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

TL;DR

A deep-learning model accurately measures aortic dissection volumes on CT scans, showing that local false-lumen volumes predict adverse remodeling better than diameter or global volumes.

Contribution

A deep-learning model for automated aortic dissection volume measurement is developed and validated, showing local false-lumen volumes are better predictors of adverse remodeling than traditional metrics.

Findings

The deep-learning model achieved high accuracy in segmenting aortic dissection components with strong agreement to manual measurements.

Local false-lumen volumes (FLLoc) outperformed maximal diameter (Dmax) and global false-lumen volumes in predicting adverse aortic remodeling.

FLLoc showed good predictive performance with high sensitivity and specificity in identifying patients at risk for adverse remodeling.

Abstract

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).

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).

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).

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.

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