Radiomics signature of epicardial adipose tissue for predicting postoperative atrial fibrillation after pulmonary endarterectomy - Liu, Zhan; Deng, Yisen; Wang, Xuming; Liu, Xiaopeng; Zheng, Xia; Sun, Guang; Zhen, Yanan; Liu, Min; Ye, Zhidong; Wen, Jianyan; Liu, Peng (Frontiers in Cardiovascular Medicine, 2023)
Purpose: This study aimed to construct a radiomics signature of epicardial adipose tissue for predicting postoperative atrial fibrillation (POAF) after pulmonary endarterectomy (PEA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH). Methods: We reviewed the preoperative computed tomography pulmonary angiography images of CTEPH patients who underwent PEA at our institution between December 2016 and May 2022. Patients were divided into training/validation and testing cohorts by stratified random sampling in a ratio of 7:3. Radiomics features were selected by using intra- and inter-class correlation coefficient, redundancy analysis, and Least Absolute Shrinkage and Selection Operator algorithm to construct the radiomics signature. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical practicability of the radiomics signature. Two hundred-times stratified 5-fold cross-validation was applied to assess the reliability and robustness of the radiomics signature. Results: A total of 93 patients with CTEPH were included in this study, including 23 patients with POAF and 70 patients without POAF. Five of the 1218 radiomics features were finally selected to construct the radiomics signature. The radiomics signature showed good discrimination with an AUC of 0.804 (95%CI: 0.664-0.943) in the training/validation cohort and 0.728 (95% CI: 0.503-0.953) in the testing cohorts. The average AUC of 200 times stratified 5-fold cross-validation was 0.804 (95%CI: 0.801-0.806) and 0.807 (95%CI: 0.798-0.816) in the training and validation cohorts, respectively. The calibration curve showed good agreement between the predicted and actual observations. Based on the DCA, the radiomics signature was found to be clinically significant and useful. Conclusions: The radiomics signature achieved good discrimination, calibration, and clinical practicability. As a potential imaging biomarker, the radiomics signature of EAT may provide a reference for the risk assessment and individualized treatment of CTEPH patients at high risk of developing POAF after PEA.