Discrimination models with radiomics features derived from cardiovascular magnetic resonance images for distinguishing hypertensive heart disease from hypertrophic cardiomyopathy

作者全名："Zhang, Hongbo; Tian, Jie; Zhang, Chen; Wang, Haoru; Hui, Keyao; Wang, Tongming; Chai, Senchun; Schoenhagen, Paul; Zhao, Lei; Ma, Xiaohai"

作者地址："[Zhang, Hongbo; Tian, Jie; Zhang, Chen; Hui, Keyao; Ma, Xiaohai] Capital Med Univ, Beijing Anzhen Hosp, Dept Intervent Diag & Treatment, 2nd Anzhen Rd, Beijing 100029, Peoples R China; [Zhang, Hongbo; Tian, Jie; Hui, Keyao; Zhao, Lei] Capital Med Univ, Beijing Anzhen Hosp, Dept Radiol, 2nd Anzhen Rd, Beijing 100029, Peoples R China; [Wang, Haoru] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Key Lab Child Dev & Disorders, Chongqing Key Lab Pediat,Dept Radiol,Childrens Hos, Chongqing, Peoples R China; [Wang, Tongming; Chai, Senchun] Beijing Inst Technol, Sch Automat, Beijing, Peoples R China; [Schoenhagen, Paul] Cleveland Clin, Cardiovasc Imaging, Miller Pavil Desk J1-4, Cleveland, OH USA"

通信作者："Ma, XH (通讯作者)，Capital Med Univ, Beijing Anzhen Hosp, Dept Intervent Diag & Treatment, 2nd Anzhen Rd, Beijing 100029, Peoples R China.; Zhao, L (通讯作者)，Capital Med Univ, Beijing Anzhen Hosp, Dept Radiol, 2nd Anzhen Rd, Beijing 100029, Peoples R China."

来源：CARDIOVASCULAR DIAGNOSIS AND THERAPY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001195804400018

JCR分区：Q3

影响因子：2.1

年份：2024

卷号：14

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Magnetic resonance image (MR image); hypertrophic cardiomyopathy (HCM); hypertensive heart disease (HHD); radiomic

摘要："Background: 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. Methods: 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. Results: 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). Conclusions: Radiomics features derived from cardiac cine and LGE images can effectively discriminate HHD from HCM."

基金机构：National Natural Science Foundation of China [82071875]; Beijing Natural Science Foundation [7222302]; National Key Research and Development Program of China [2021YFF0501400]

基金资助正文："Funding: This work was supported by National Natural Science Foundation of China (No. 82071875) , Beijing Natural Science Foundation (No. 7212025) , Beijing Natural Science Foundation (No. 7222302) , and National Key Research and Development Program of China (Project No. 2021YFF0501400) ."