Key result
Patients with CAD exhibit lower PCAT gray-level variability compared to non-CAD patients.
Why the study?
The role of pericoronary adipose tissue texture in coronary artery disease development and its assessment by photon-counting CT radiomics analysis is not well understood.
Does radiomics texture analysis of pericoronary adipose tissue using photon-counting CT differentiate between patients with and without coronary artery disease?
Observational (n=36)
No
Does radiomics texture analysis of pericoronary adipose tissue using photon-counting CT differentiate between patients with and without coronary artery disease?
Radiomics analysis of pericoronary adipose tissue using photon-counting CT can identify texture differences, specifically a more homogeneous pattern, in patients with coronary artery disease compared to those without.
Lower PCAT gray-level variability was associated with CAD on photon-counting CT; hypothesis-generating for radiomics-based detection pending prospective validation.
Aim: Recent research highlights the role of pericoronary adipose tissue (PCAT) in coronary artery disease (CAD) development. PCAT has been recognized as a metabolically active tissue involved in local inflammation and oxidative stress, potentially impacting CAD initiation and progression. Radiomics texture analysis shows promising results to better understand the link between PCAT quality and CAD risk. Photon-counting CT (PCCT) offers improved feature stability and holds the potential for advancing radiomics analysis in CAD research. Methods: In this retrospective, single-center, ethic committee-approved study, PCAT of the left descending artery (LAD) and right coronary artery (RCA) was manually segmented and radiomic features were extracted using pyradiomics. The study population consisted of one group of patients with CAD and plaques exclusively located in the left coronary artery and another group without CAD. Mean and standard deviation were calculated using R Statistics. Random forest feature selection was performed to identify differentiating features between the four sets CAD-LAD, CAD-RCA, non-CAD-LAD and non-CAD-RCA. Results: 36 patients were enrolled in this study (16 female, mean age 56 years). The feature "original_glszm_GrayLevelNonUniformity" measuring the gray-level variability was identified as the most potent differentiator between CAD-LAD and non-CAD-LAD, as well as CAD-RCA and non-CAD-RCA with the greatest differentiating capability for the LAD comparison. The feature showed little differentiating power between CAD-LAD and CAD-RCA and virtually none between non-CAD-LAD and non-CAD-RCA. The mean values were consistently lower in LAD-PCAT and exhibited patient-specific reductions in CAD patients (155.16 for CAD-LAD, 163.21 for non-CAD-LAD, 189.13 for CAD-RCA and 215.40 for non-CAD-RCA). Conclusion: Radiomics analysis revealed differences in PCAT texture of patients with and without CAD with a potentially more homogeneous pattern in CAD-affected patients. These changes related to plaques in the left coronary artery also seemed to occur in the unaffected RCA-PCAT, although to a slightly lesser extent.
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Kahmann et al. (2024) conducted an observational in Coronary artery disease (n=36). Coronary artery disease vs. No coronary artery disease was evaluated on original_glszm_GrayLevelNonUniformity (gray-level variability) of pericoronary adipose tissue. Patients with CAD exhibited lower gray-level variability in pericoronary adipose tissue compared to those without CAD (mean 155.16 vs 163.21 for LAD, and 189.13 vs 215.40 for RCA).
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