Abstract：
OBJECTIVE: To develop a radiomics model based on cardiac computed tomography (CT) for predicting left ventricular adverse remodeling (LVAR) in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR).
METHODS: Patients with severe AS who underwent TAVR from January 2019 to December 2022 were recruited. The cohort was divided into adverse remodeling group and non-adverse remodeling group based on LVAR occurrence, and further randomly divided into a training set and a validation set at an 8:2 ratio. Left ventricular radiomics features were extracted from cardiac CT. The least absolute shrinkage and selection operator regression was utilized to select the most relevant radiomics features and clinical features. The radiomics features were used to construct the Radscore, which was then combined with the selected clinical features to build a nomogram. The predictive performance of the models was evaluated using the area under the curve (AUC), while the clinical value of the models was assessed using calibration curves and decision curve analysis.
RESULTS: A total of 273 patients were finally enrolled, including 71 with adverse remodeling and 202 with non-adverse remodeling. 12 radiomics features and five clinical features were extracted to construct the radiomics model, clinical model, and nomogram, respectively. The radiomics model outperformed the clinical model (training AUC: 0.799 vs. 0.760; validation AUC: 0.766 vs. 0.755). The nomogram showed highest accuracy (training AUC: 0.859, validation AUC: 0.837) and was deemed most clinically valuable by decision curve analysis.
CONCLUSIONS: The cardiac CT-based radiomics features could predict LVAR after TAVR in patients with severe AS.
METHODS: Patients with severe AS who underwent TAVR from January 2019 to December 2022 were recruited. The cohort was divided into adverse remodeling group and non-adverse remodeling group based on LVAR occurrence, and further randomly divided into a training set and a validation set at an 8:2 ratio. Left ventricular radiomics features were extracted from cardiac CT. The least absolute shrinkage and selection operator regression was utilized to select the most relevant radiomics features and clinical features. The radiomics features were used to construct the Radscore, which was then combined with the selected clinical features to build a nomogram. The predictive performance of the models was evaluated using the area under the curve (AUC), while the clinical value of the models was assessed using calibration curves and decision curve analysis.
RESULTS: A total of 273 patients were finally enrolled, including 71 with adverse remodeling and 202 with non-adverse remodeling. 12 radiomics features and five clinical features were extracted to construct the radiomics model, clinical model, and nomogram, respectively. The radiomics model outperformed the clinical model (training AUC: 0.799 vs. 0.760; validation AUC: 0.766 vs. 0.755). The nomogram showed highest accuracy (training AUC: 0.859, validation AUC: 0.837) and was deemed most clinically valuable by decision curve analysis.
CONCLUSIONS: The cardiac CT-based radiomics features could predict LVAR after TAVR in patients with severe AS.
摘要：
目的：建立基于心脏计算机断层扫描（CT）的影像组学模型，用于预测经导管主动脉瓣置换术（TAVR）的重度主动脉瓣狭窄（AS）患者的左心室不良重塑（LVAR）。
方法：招募2019年1月至2022年12月接受TAVR的重度AS患者。根据LVAR发生情况分为不良重塑组和非不良重塑组,并进一步以8：2的比例随机分为训练集和验证集。从心脏CT中提取左心室影像组学特征。利用最小绝对收缩和选择算子回归来选择最相关的影像组学特征和临床特征。影像组学特征被用来构建Radscore，然后将其与选定的临床特征相结合以构建列线图。使用曲线下面积(AUC)评估模型的预测性能，同时使用校准曲线和决策曲线分析评估模型的临床价值.
结果：最终共纳入273名患者，包括71例不良重塑和202例非不良重塑。提取12个影像组学特征和5个临床特征构建影像组学模型,临床模型，和列线图，分别。影像组学模型优于临床模型(训练AUC：0.799vs.0.760；验证AUC：0.766vs.0.755).列线图显示最高的准确性(训练AUC:0.859,验证AUC:0.837),并且通过决策曲线分析被认为最具临床价值。
结论：基于心脏CT的影像组学特征可以预测严重AS患者TAVR后的LVAR。
方法：招募2019年1月至2022年12月接受TAVR的重度AS患者。根据LVAR发生情况分为不良重塑组和非不良重塑组,并进一步以8：2的比例随机分为训练集和验证集。从心脏CT中提取左心室影像组学特征。利用最小绝对收缩和选择算子回归来选择最相关的影像组学特征和临床特征。影像组学特征被用来构建Radscore，然后将其与选定的临床特征相结合以构建列线图。使用曲线下面积(AUC)评估模型的预测性能，同时使用校准曲线和决策曲线分析评估模型的临床价值.
结果：最终共纳入273名患者，包括71例不良重塑和202例非不良重塑。提取12个影像组学特征和5个临床特征构建影像组学模型,临床模型，和列线图，分别。影像组学模型优于临床模型(训练AUC：0.799vs.0.760；验证AUC：0.766vs.0.755).列线图显示最高的准确性(训练AUC:0.859,验证AUC:0.837),并且通过决策曲线分析被认为最具临床价值。
结论：基于心脏CT的影像组学特征可以预测严重AS患者TAVR后的LVAR。
