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Updated: Sep 13, 2025
In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
Published on: February 16, 2016
Radiomics Signature of Aging Myocardium in Cardiac Photon-Counting Computed Tomography
Alexander Hertel1, Mustafa Kuru1, Johann S Rink1
1Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
Insights
Photon-counting CT texture analysis reveals myocardial aging changes. Radiomics features of the left ventricle show promise as a cardiac risk predictor, outperforming traditional measures.
Area of Science:
- Cardiology
- Medical Imaging
- Radiology
Background:
- Cardiovascular diseases are a leading cause of death, particularly in older adults.
- Aging cardiovascular structures require deeper understanding for risk prediction.
- Photon-counting computed tomography (PCCT) enhances cardiac imaging for detailed analysis.
Purpose of the Study:
- To investigate the potential of radiomics texture analysis in differentiating age-related changes in the left ventricular myocardium.
- To compare the efficacy of myocardial texture features against conventional parameters like epicardial adipose tissue (EAT) for age stratification.
Main Methods:
- Retrospective analysis of 90 patients undergoing ECG-gated contrast-enhanced cardiac CT using dual-source PCCT.
- Segmentation of the left ventricular myocardium and extraction of radiomics features using pyradiomics.
- Comparison of texture features between two age groups (50-60 and 70-80 years) using statistical analysis and a random forest classifier.
Main Results:
- Radiomics texture analysis successfully differentiated between the age groups, achieving a test accuracy of 0.74.
- Wavelet-HLH_glszm_GrayLevelNonUniformity emerged as a key feature for age differentiation.
- No significant differences were observed in EAT density, thickness, or other clinical parameters between the age groups.
Conclusions:
- Myocardial radiomics texture features are superior to conventional parameters like EAT for identifying age-related myocardial changes.
- This imaging biomarker holds potential for predicting cardiovascular risk by visualizing myocardial aging.
- PCCT-based radiomics offers novel insights into myocardial structural alterations during aging.
Abstract:
Background: Cardiovascular diseases are the leading cause of global mortality, with 80% of coronary heart disease in patients over 65. Understanding aging cardiovascular structures is crucial. Photon-counting computed tomography (PCCT) offers improved spatial and temporal resolution and better signal-to-noise ratio, enabling texture analysis in clinical routines. Detecting structural changes in aging left-ventricular myocardium may help predict cardiovascular risk. Methods: In this retrospective, single-center, IRB-approved study, 90 patients underwent ECG-gated contrast-enhanced cardiac CT using dual-source PCCT (NAEOTOM Alpha, Siemens). Patients were divided into two age groups (50-60 years and 70-80 years). The left ventricular myocardium was segmented semi-automatically, and radiomics features were extracted using pyradiomics to compare myocardial texture features. Epicardial adipose tissue (EAT) density, thickness, and other clinical parameters were recorded. Statistical analysis was conducted with R and a Python-based random forest classifier. Results: The study assessed 90 patients (50-60 years, n = 54, and 70-80 years, n = 36) with a mean age of 63.6 years. No significant differences were found in mean Agatston score, gender distribution, or conditions like hypertension, diabetes, hypercholesterolemia, or nicotine abuse. EAT measurements showed no significant differences. The Random Forest Classifier achieved a training accuracy of 0.95 and a test accuracy of 0.74 for age group differentiation. Wavelet-HLH_glszm_GrayLevelNonUniformity was a key differentiator. Conclusions: Radiomics texture features of the left ventricular myocardium outperformed conventional parameters like EAT density and thickness in differentiating age groups, offering a potential imaging biomarker for myocardial aging. Radiomics analysis of left ventricular myocardium offers a unique opportunity to visualize changes in myocardial texture during aging and could serve as a cardiac risk predictor.
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