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Understanding the Complex Structure of the Left Atrium from Cardiac CT – A Machine Learning-Based Radiomics Model to Predict Post-Ablation Recurrence of Atrial Fibrillation
Session:
Comunicações Orais - Sessão 12 - Fibrilhação Auricular: Novas Perspetivas sobre os Mecanismos
Speaker:
João Bicho Augusto
Congress:
CPC 2023
Topic:
C. Arrhythmias and Device Therapy
Theme:
05. Atrial Fibrillation
Subtheme:
05.9 Atrial Fibrillation - Other
Session Type:
Comunicações Orais
FP Number:
---
Authors:
João Bicho Augusto; Pedro Cunha; Sérgio M Laranjo; Guilherme Portugal; Bruno Valente; Ana Lousinha; Bárbara Teixeira; André V Monteiro; Margarida Paulo; Cátia Guerra; Mário M Oliveira
Abstract
<p><strong>Background: </strong>Complex properties of the left atrial (LA) wall and cavity could help understand the pathophysiology of atrial fibrillation (AF) and the risk of recurrence after ablation. Beyond conventional cardiac CT measures, radiomics allow extraction of high-dimensional data and deep quantitative phenotyping of the LA.<br /> <br /> <strong>Objective: </strong>We aimed to assess radiomics models based on LA wall images from cardiac CT to predict the risk of AF recurrence after ablation.<br /> <br /> <strong>Methods: </strong>Cardiac CT images from 37 patients obtained immediately prior to AF ablation were prospectively collected and reviewed. The LA wall was segmented using a machine-learning LA wall segmentation tool with minimal input from the user. The LA cavity was segmented using a semi-automated tool. A total of 140 radiomics features were extracted (without wavelet decomposition) using the PyRadiomics library, which included first-order and textural features from the LA wall, and shape and size features from the LA cavity. Features with a high variance inflation factor were excluded from the analysis. A model of radiomics signatures was built using least absolute shrinkage and selection operator (LASSO) regression to explore the prognostic value for AF recurrence within 12 months. Flow chart is summarized in Figure.<br /> <br /> <strong>Results: </strong>Size zone non-uniformity (SZN), an LA wall texture feature, was the only independent predictor of AF recurrence at 12 months follow-up. SZN measures the variability of size zone volumes in the image, with a lower value indicating more homogeneity in size zone volumes. SZN was significantly higher (suggesting more LA wall heterogeneity) in patients with AF recurrence (median 24567 [IQR 19729-30286] vs 18481 [IQR 13485 - 21623], p=0.03). C-statistics showed good ability in predicting AF recurrence, with AUC 0.712 (95% confidence interval 0.539 – 0.886). The survival analysis revealed a log-rank Mantel-Cox test with a chi-square of 103 (p<0.001).<br /> <br /> <strong>Conclusion: </strong>The complex structure of the LA wall through radiomics conveys information beyond conventional CT imaging. We present a novel non-invasive tool to measure heterogeneous atrial tissue. Heterogenous LA walls are more prone to AF recurrence post-ablation, likely reflecting a higher susceptibility to re-entry mechanisms, high conduction anisotropy, or a combination of these.</p>
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