A Gradient Boosting machine learning model using radiomics and clinical features achieved an AUC of 0.9897 in distinguishing the left from right ventricular outflow tract for PVC localization.
Observational (n=304)
No
Does combining radiomics and machine learning improve the localization of premature ventricular contractions compared to traditional ECG methods in patients undergoing catheter ablation?
Machine learning models combining CCTA radiomics and clinical features demonstrate high accuracy (AUC > 0.85) for localizing premature ventricular contractions, offering a robust alternative to traditional ECG methods.
Effect estimate: AUC 0.9897
Objective Premature Ventricular Contractions (PVC) is a common arrhythmia. Accurate localization is crucial for effective treatment and prognosis. Current Electrocardiography (ECG) methods face inherent limitations in localizing PVC precisely. This study aimed to develop a robust PVC localization model using radiomics and machine learning. Methods Data was collected from 304 PVC patients who underwent catheter radiofrequency ablation at the First Affiliated Hospital of Dalian Medical University between November 2015 and May 2023. Coronary Computed Tomography Angiography and clinical baseline data were used to extract 980 radiomic features. Least Absolute Shrinkage and Selection Operator regression identified the most valuable features. The dataset was divided into training and testing sets in a 7: 3 ratio. Fifteen machine learning algorithms were used for model construction and evaluation, with SHapley Additive exPlanations analysis to assess feature importance. The results were compared with traditional ECG localization diagnostics and previously-studied articles. Results Gradient Boosting (GB), LightGBM, and Random Forest models performed well, with the area under the receiver operating characteristic curve (AUC) exceeding 0. 8515, showing competitive performance compared to reported metrics of ECG-based methods. The GB model achieved an AUC of 0. 9897 in distinguishing the left ventricular outflow tract from the right ventricular outflow tract. SHAP analysis revealed that radiomics features such as originalglszmHighGrayLevelZoneEmphasis and clinical features such as B-type natriuretic peptide and left ventricular ejection fraction all emerged as important contributors to the predictive capacity. Conclusion Combining radiomics and machine learning techniques offers a robust, data-driven framework that complements traditional diagnostic approaches for PVC localization. This method enhances diagnostic precision and aids in developing personalized treatment plans for PVC patients.
Liu et al. (Sun,) conducted a observational in Premature Ventricular Contractions (n=304). Radiomics and machine learning models vs. Traditional ECG localization diagnostics was evaluated on PVC localization (distinguishing left ventricular outflow tract from right ventricular outflow tract) (AUC 0.9897). A Gradient Boosting machine learning model using radiomics and clinical features achieved an AUC of 0.9897 in distinguishing the left from right ventricular outflow tract for PVC localization.
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