Liu Zhan, Deng Yisen, Wang Xuming, Liu Xiaopeng, Zheng Xia, Sun Guang, Zhen Yanan, Liu Min, Ye Zhidong, Wen Jianyan, Liu Peng
Department of Cardiovascular Surgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China.
Front Cardiovasc Med. 2023 Jan 9;9:1046931. doi: 10.3389/fcvm.2022.1046931. eCollection 2022.
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).
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 five-fold cross-validation was applied to assess the reliability and robustness of the radiomics signature.
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 1,218 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 five-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.
The radiomics signature achieved good discrimination, calibration, and clinical practicability. As a potential imaging biomarker, the radiomics signature of epicardial adipose tissue (EAT) may provide a reference for the risk assessment and individualized treatment of CTEPH patients at high risk of developing POAF after PEA.
本研究旨在构建慢性血栓栓塞性肺动脉高压（CTEPH）患者肺动脉内膜剥脱术（PEA）后预测术后房颤（POAF）的心外膜脂肪组织的放射组学特征。
我们回顾了2016年12月至2022年5月在我院接受PEA的CTEPH患者的术前计算机断层扫描肺动脉造影图像。通过分层随机抽样，将患者按7:3的比例分为训练/验证队列和测试队列。通过使用组内和组间相关系数、冗余分析以及最小绝对收缩和选择算子算法选择放射组学特征，以构建放射组学特征。采用受试者工作特征曲线（AUC）下面积、校准曲线和决策曲线分析（DCA）来评估放射组学特征的鉴别能力、校准度和临床实用性。应用200次分层五折交叉验证来评估放射组学特征的可靠性和稳健性。
本研究共纳入93例CTEPH患者，其中23例发生POAF，70例未发生POAF。最终从1218个放射组学特征中选择了5个来构建放射组学特征。放射组学特征在训练/验证队列中的AUC为0.804（95%CI：0.664 - 0.943），在测试队列中的AUC为0.728（95%CI：0.503 - 0.953），显示出良好的鉴别能力。在训练队列和验证队列中，200次分层五折交叉验证的平均AUC分别为0.804（95%CI：0.801 - 0.806）和0.807（95%CI：0.798 - 0.816）。校准曲线显示预测值与实际观察值之间具有良好的一致性。基于DCA，发现放射组学特征具有临床意义且实用。
放射组学特征具有良好的鉴别能力、校准度和临床实用性。作为一种潜在的影像学生物标志物，心外膜脂肪组织（EAT）的放射组学特征可为PEA后发生POAF高风险的CTEPH患者的风险评估和个体化治疗提供参考。
