Deep learning radiomics model of epicardial adipose tissue for predicting postoperative atrial fibrillation after lung lobectomy in lung cancer patients

Objective To develop and validate a deep learning (DL) radiomics model based on epicardial adipose tissue (EAT) for identifying high-risk lung cancer patients with postoperative atrial fibrillation after lung lobectomy. Methods A total of 1,008 patients from two centers were included. Handcrafted and DL radiomics features were extracted from the preoperative contrast-enhanced chest CT images of EAT. Clinical features and handcrafted and DL radiomics signatures were integrated to construct predictive models using the logistic regression algorithm as the baseline model. Twenty DL radiomics models were constructed through various combinations of machine learning algorithms and resampling techniques. The post hoc Nemenyi test was employed to compare the predictive performance in terms of the area under the receiver operating characteristic curve (AUC), G-mean, and F-measure. Results Advanced age and male sex were identified as independent risk factors for POAF. The DL radiomics model, integrating clinical features, handcrafted radiomics signature, and DL radiomics signature, outperformed the clinical model, achieving AUC values of 0.890 (95% CI: 0.816–0.963), 0.876 (95% CI: 0.755–0.997), and 0.803 (95% CI: 0.651–0.955) in the training, testing, and validation cohorts, respectively. The results of the post hoc Nemenyi tests indicated that neither machine learning algorithms nor resampling techniques significantly improved model performance, as measured by the AUC, G-mean, or F-measure. Conclusion The DL radiomics model based on preoperative EAT images effectively identifies high-risk lung cancer patients with POAF following lung lobectomy and offers a novel tool for risk stratification.