Radiomics signature of aging myocardium in cardiac photon-counting computed tomography
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 signa...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
16 July 2025
|
| In: |
Diagnostics
Year: 2025, Volume: 15, Issue: 14, Pages: 1-14 |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics15141796 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/diagnostics15141796 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2075-4418/15/14/1796 
|
| Author Notes: | Alexander Hertel, Mustafa Kuru, Johann S. Rink, Florian Haag, Abhinay Vellala, Theano Papavassiliu, Matthias F. Froelich, Stefan O. Schoenberg and Isabelle Ayx |
| Summary: | 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. |
|---|---|
| Item Description: | Gesehen am 04.09.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics15141796 |