Zhang Hongbo, Tian Jie, Zhang Chen, Wang Haoru, Hui Keyao, Wang Tongming, Chai Senchun, Schoenhagen Paul, Zhao Lei, Ma Xiaohai
Department of Interventional Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Cardiovasc Diagn Ther. 2024 Feb 15;14(1):129-142. doi: 10.21037/cdt-23-350. Epub 2024 Jan 27.
Discriminating hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) is challenging, because both are characterized by left ventricular hypertrophy (LVH). Radiomics might be effective to differentiate HHD from HCM. Therefore, this study aimed to investigate discriminators and build discrimination models between HHD and HCM using multiparametric cardiac magnetic resonance (CMR) findings and radiomics score (radscore) derived from late gadolinium enhancement (LGE) and cine images.
In this single center, retrospective study, 421 HCM patients [median and interquartile range (IQR), 50.0 (38.0-59.0) years; male, 70.5%] from January 2017 to September 2021 and 200 HHD patients [median and IQR, 44.5 (35.0-57.0) years; male, 88.5%] from September 2015 to July 2022 were consecutively included and randomly stratified into a training group and a validation group at a ratio of 6:4. Multiparametric CMR findings were obtained using cvi42 software and radiomics features using Python software. After dimensional reduction, the radscore was calculated by summing the remaining radiomics features weighted by their coefficients. Multiparametric CMR findings and radscore that were statistically significant in univariate logistic regression were used to build combined discrimination models via multivariate logistic regression.
After multivariate logistic regression, the maximal left ventricular end diastolic wall thickness (LVEDWT), left ventricular ejection fraction (LVEF), presence of LGE, cine radscore and LGE radscore were identified as significant characteristics and used to build a combined discrimination model. This model achieved an area under the receiver operator characteristic curve (AUC) of 0.979 (0.968-0.990) in the training group and 0.981 (0.967-0.995) in the validation group, significantly better than the model using multiparametric CMR findings alone (P<0.001).
Radiomics features derived from cardiac cine and LGE images can effectively discriminate HHD from HCM.
鉴别肥厚型心肌病（HCM）和高血压性心脏病（HHD）具有挑战性，因为两者均以左心室肥厚（LVH）为特征。放射组学可能有助于区分HHD和HCM。因此，本研究旨在利用多参数心脏磁共振成像（CMR）结果以及从延迟钆增强（LGE）和电影图像得出的放射组学评分（radscore），研究鉴别因素并建立HHD与HCM之间的鉴别模型。
在这项单中心回顾性研究中，连续纳入了2017年1月至2021年9月的421例HCM患者[中位数和四分位间距（IQR），50.0（38.0 - 59.0）岁；男性，70.5%]以及2015年9月至2022年7月的200例HHD患者[中位数和IQR，44.5（35.0 - 57.0）岁；男性，88.5%]，并按6:4的比例随机分层为训练组和验证组。使用cvi42软件获取多参数CMR结果，使用Python软件获取放射组学特征。经过降维后，通过对剩余放射组学特征与其系数加权求和来计算radscore。在单因素逻辑回归中具有统计学意义的多参数CMR结果和radscore用于通过多因素逻辑回归建立联合鉴别模型。
经过多因素逻辑回归，最大左心室舒张末期壁厚度（LVEDWT）、左心室射血分数（LVEF）、LGE的存在、电影radscore和LGE radscore被确定为显著特征，并用于建立联合鉴别模型。该模型在训练组中的受试者操作特征曲线下面积（AUC）为0.979（0.968 - 0.990），在验证组中为0.981（0.967 - 0.995），显著优于仅使用多参数CMR结果的模型（P<0.001）。
从心脏电影和LGE图像得出的放射组学特征可有效区分HHD和HCM。
