Abstract：
Personalized management involving heart failure (HF) etiology is crucial for better prognoses. We aim to evaluate the utility of a radiomics nomogram based on gated myocardial perfusion imaging (GMPI) in distinguishing ischemic from non-ischemic origins of HF. A total of 172 heart failure patients with reduced left ventricular ejection fraction (HFrEF) who underwent GMPI scan were divided into training (n = 122) and validation sets (n = 50) based on chronological order of scans. Radiomics features were extracted from the resting GMPI. Four machine learning algorithms were used to construct radiomics models, and the model with the best performances were selected to calculate the Radscore. A radiomics nomogram was constructed based on the Radscore and independent clinical factors. Finally, the model performance was validated using operating characteristic curves, calibration curve, decision curve analysis, integrated discrimination improvement values (IDI), and the net reclassification index (NRI). Three optimal radiomics features were used to build a radiomics model. Total perfusion deficit (TPD) was identified as the independent factors of conventional GMPI metrics for building the GMPI model. In the validation set, the radiomics nomogram integrating the Radscore, age, systolic blood pressure, and TPD significantly outperformed the GMPI model in distinguishing ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) (AUC 0.853 vs. 0.707, p = 0.038). IDI analysis indicated that the nomogram improved diagnostic accuracy by 28.3% compared to the GMPI model in the validation set. By combining radiomics signatures with clinical indicators, we developed a GMPI-based radiomics nomogram that helps to identify the ischemic etiology of HFrEF.
摘要：
涉及心力衰竭（HF）病因的个性化管理对于更好的预后至关重要。我们旨在评估基于门控心肌灌注成像（GMPI）的放射学列线图在区分缺血性和非缺血性HF起源中的实用性。根据扫描的时间顺序，总共172例左心室射血分数（HFrEF）降低的心力衰竭患者接受了GMPI扫描，分为训练集（n=122）和验证集（n=50）。从静息GMPI中提取影像组学特征。四种机器学习算法用于构建影像组学模型，并选择性能最好的模型来计算Radscore。根据Radscore和独立的临床因素构建放射组学列线图。最后,使用工作特性曲线对模型性能进行了验证，校正曲线,决策曲线分析,综合判别改进值(IDI)，和净重新分类指数(NRI)。使用三个最佳的影像组学特征来构建影像组学模型。总灌注不足(TPD)被确定为用于构建GMPI模型的常规GMPI度量的独立因素。在验证集中，整合Radscore的影像组学列线图，年龄,收缩压,TPD在区分缺血性心肌病（ICM）和非缺血性心肌病（NICM）方面明显优于GMPI模型（AUC0.853vs.0.707,p=0.038)。IDI分析表明，与验证集中的GMPI模型相比，列线图的诊断准确性提高了28.3％。通过将影像组学特征与临床指标相结合，我们开发了一个基于GMPI的放射组学列线图,有助于确定HFrEF的缺血性病因.
