Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation

Figures

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  Figure 1. Workflow showing (A) patient selection and (B) model development and (C) model validation. NVAF, non-valvular atrial fibrillation; ECG, electrocardiogram; TTE, transthoracic echocardiography; TEE, transesophageal echocardiography; CTA, CT angiography; CA, catheter ablation; LA, left atrium; LVEF, left ventricular ejection fraction.

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  Figure 2. Key steps in a radiomics workflow for AF patients. First, ROIs containing LA are delineated on apical 4CV and 2CV images, respectively, to extract seven types of radiomics features. After normalization, the key radiomics features associated with AF recurrence are identified by a three-step feature selection method (interclass correlation coefficient test, Student's t-test, and LASSO logistic regression). Finally, they are integrated into the rad score using linear regression. AF, atrial fibrillation; ROI, region of interest; LA, left atrium; 4CV, four-chamber view; 2CV, two-chamber view; LASSO, least absolute shrinkage and selection operator; Rad score, radiomics score.

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  Figure 3. Radiomics feature selection performed using LASSO logistic regression for establishing the rad score. (A–C) and (D–E) describe the results of radiomics analysis for 4CV and 2CV images, respectively. In 4CV analysis, seven features are selected by identifying the best λ with λ.1-SE in the LASSO model (A,B), and their weights are listed in (C). In 2CV analysis, no features can be selected with the optimal λ values (D,E). LASSO, least absolute shrinkage and selection operator; 4CV, four-chamber view; 2CV, two-chamber view; λ, penalty regularization parameter; λ.min, minimum criteria; λ.1-SE, 1-standard error of the minimum criteria.

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  Figure 4. Nomograms developed for predicting the likelihood of sinus rhythm maintenance within 1 year following CA. (A) illustrates a radiomics nomogram, where each selected predictor, inclusive of the rad score, is assigned a score on the points scale situated at the top of the nomogram. The sum of these scores is then matched with the bottom scale, estimating the probability of sinus rhythm maintenance within 1 year after CA. As a comparison, (B) represents a clinical nomogram. This is designed utilizing the same set of clinical predictors as those in the radiomics nomogram, excluding the rad score. In line with the methodology of the radiomics nomogram, each predictor in the clinical nomogram is allocated a score on the points scale, and the cumulative scores serve to predict the likelihood of maintaining the sinus rhythm. AF, atrial fibrillation; PAF, paroxysmal atrial fibrillation; PeAF, persistent atrial fibrillation; LAAFV, left atrial appendage emptying flow velocity; PALS, peak atrial longitudinal strain; CA, catheter ablation.

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  Figure 5. Evaluation of the model discrimination in the training and validation cohorts. The discrimination of a radiomics nomogram and a clinical nomogram (building without the rad score) is indicated by orange and green lines, respectively. The discrimination of the radiomics nomogram is higher than that of the clinical nomogram both in the training (A) and in the validation (B) cohort. ROC, receiver operating characteristic; rad score, radiomics score.

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  Figure 6. Evaluation of calibration and clinical utility of a radiomics nomogram in the training and validation cohorts. The calibration curves of the radiomics nomogram in the training (A) and validation (B) cohorts are close to the ideal line, indicating good agreement. The DCA curves for predicting the outcome of CA in the training (C) and validation (D) cohorts reveal that using the radiomics nomogram to predict the outcome within 1 year after CA adds more net benefit than the treat-all or treat-none scheme. CA, catheter ablation; DCA, decision curve analysis.