Journal Article

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease

Wang, Shi-Wei, Yu, Ya-Nan, Wang, Jia-Ning, Zhang, Xiao-Dan, Zheng, Wei, Ma, Ze-Peng, Yuan, Shuang-Rui, Zhang, Tian-Le, Xue, Lin-Yan, Zhao, Yong-Xia, Li, Gao-Yang

2024

Overview

Background The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. Methods 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. Results The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. Conclusions Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Adult / Aged / Algorithms / Bayes Theorem / Cardiac function / Cardiac magnetic resonance / Cardiomyopathy / Cardiovascular disease / Cardiovascular diseases / Early Diagnosis / Feature selection / Female / Heart / Heart diseases / Heart Diseases - diagnostic imaging / Hospitals / Humans / Hypertension / Hypertension - complications / Hypertension - diagnostic imaging / Hypertensive heart disease / Image Interpretation, Computer-Assisted - methods / Imaging / Latent heart damage / Machine learning / Magnetic resonance / Magnetic Resonance Imaging, Cine - methods / Male / Medical diagnosis / Medicine / Medicine & Public Health / Middle Aged / Non-enhanced cine sequence / Normal distribution / Patients / Radiology / Radiomics / ROC Curve / Statistical analysis / Structure-function relationships / Support Vector Machine / Support vector machines / Wavelet transforms