Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT.
F, W., R, B., SO, S., A, H., & I, A. (2026). Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT.. Tomography (Ann Arbor, Mich.). https://doi.org/10.3390/tomography12060081
F W, R B, SO S, A H, I A. Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT.. Tomography (Ann Arbor, Mich.). 2026; doi: 10.3390/tomography12060081
F W, R B, SO S, et al. Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT.[J]. Tomography (Ann Arbor, Mich.). 2026. DOI: 10.3390/tomography12060081.
@article{f2026,
author = {Waßmer F and Bauer R and Schoenberg SO and Hertel A and Ayx I},
title = {Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT.},
journal = {Tomography (Ann Arbor, Mich.)},
year = {2026},
doi = {10.3390/tomography12060081},
note = {PMID: 42347136},
}
TY - JOUR AU - Waßmer F AU - Bauer R AU - Schoenberg SO AU - Hertel A AU - Ayx I TI - Analysis of Myocardial Textures in Relation to Nicotine Abuse Using Radiomics in Cardiac PCCT. T2 - Tomography (Ann Arbor, Mich.) PY - 2026 DO - 10.3390/tomography12060081 AN - PMID:42347136 ER -
Background/Objectives: Photon-counting computed tomography (PCCT) combined with radiomics enables advanced myocardial tissue characterization beyond conventional imaging. This study investigated whether myocardial radiomic features derived from PCCT are associated with nicotine status in patients without coronary artery disease. Methods: In this retrospective, single-center study, 104 patients (38 men, 66 women; median age 54 years) without coronary calcification (Agatston score = 0) underwent cardiac PCCT. Myocardial septal thickness was measured at three points during the 65-70% cardiac phase. Myocardial tissue was manually segmented, and 105 radiomic features were extracted. After correlation-based feature reduction, 45 independent features were used for analysis. Patients were categorized based on nicotine status. Machine learning models, including logistic regression, random forest, and gradient boosting, were trained and evaluated using stratified five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC) and additional classification metrics. Results: No significant differences in myocardial septal thickness were observed between smokers and non-smokers (p > 0.05). However, radiomic features enabled moderate discrimination between smokers and non-smokers. Logistic regression with L2 regularization achieved the best performance (ROC-AUC 0.66, balanced accuracy 0.67), outperforming random forest and gradient boosting models. The most relevant radiomic features primarily comprised higher-order texture and shape-based parameters associated with spatial gray-level heterogeneity and subtle variations in myocardial tissue architecture. Conclusions: PCCT-based radiomics may capture subtle myocardial imaging signatures associated with smoking status, even in the absence of structural changes detectable by conventional metrics. These findings highlight the potential of cardiac radiomics as a non-invasive imaging biomarker for early cardiovascular risk assessment and support its integration into advanced cardiac imaging workflows. Future multicenter studies with larger cohorts, external validation, and multimodal correlation are warranted to improve robustness and facilitate clinical translation.