Machine Learning-derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins from Cardiac CT Scans are Associated with Risk of Recurrence of Atrial Fibrillation Post-ablation.

TitleMachine Learning-derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins from Cardiac CT Scans are Associated with Risk of Recurrence of Atrial Fibrillation Post-ablation.
Publication TypeJournal Article
Year of Publication2021
AuthorsFirouznia, M, Feeny AK, LaBarbera MA, McHale M, Cantlay C, Kalfas N, Schoenhagen P, Saliba W, Tchou P, Barnard J, Chung MK, Madabhushi A
JournalCirculation. Arrhythmia and electrophysiology
Date Published2021 Feb 12
ISSN1941-3084
Abstract

- We hypothesized that computerized morphologic analysis of the LA and pulmonary veins (PVs) via fractal measurements of shape and texture features of the LA myocardial wall could predict AF recurrence after ablation. - Pre-ablation contrast CT scans were collected for 203 patients who underwent AF ablation. The LA body, PVs, and myocardial wall were segmented using a semi-automated region growing method. Twenty-eight fractal-based shape and texture-based features were extracted from resulting segments. The top features most associated with post-ablation recurrence were identified using feature selection and subsequently evaluated with a Random Forest classifier. Feature selection and classifier construction were performed on a discovery cohort (D) of 137 patients; classifiers were subsequently validated on an independent set (D) of 66 patients. Dedicated classifiers to capture the fractal and morphologic properties of LA body (C), PVs (C), and LA myocardial (C) tissue were constructed, as well as a model (C) capturing properties of all segmented compartments. Fractal-based models were also compared against a model employing machine estimation of LA volume. To assess the effect of clinical parameters, such as AF type and catheter technique, a clinical model (C) was also compared against C. - Statistically significant differences were observed for fractal features of C, C and C in distinguishing AF recurrence (p<0.001) on D. Using the five top features, C had the best prediction performance (AUC=0.81 [95% Confidence Interval (CI): 0.78-0.85]), followed by C (AUC=0.78 [95% CI: 0.74-0.80]) and C (AUC=0.70 [95% CI: 0.63-0.78]) on D. The clinical parameter model C yielded an AUC=0.70 [95% CI: 0.65-0.77], while the atrial volume model yielded an AUC=0.59. Combining C and C on D improved the AUC to 0.87 [95% CI: 0.82-0.93]. - Fractal measurements of the LA, PVs, and atrial myocardium on CT scans were associated with likelihood of post-ablation AF recurrence.

DOI10.1161/CIRCEP.120.009265
PDF Link

http://www.ncbi.nlm.nih.gov/pubmed/33576688?dopt=Abstract

Alternate JournalCirc Arrhythm Electrophysiol

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