Key result
Non-contrast CT PCAT radiomics matches CCTA for predicting coronary plaques in low Agatston scores.
Why the study?
Patients with low Agatston scores often have clinical signs of coronary artery disease despite minimal calcium deposits, necessitating evaluation of CT radiomics methods for plaque prediction.
Does non-contrast CT PCAT radiomics predict coronary artery plaques as effectively as CCTA PCAT radiomics in patients with suspected CAD and low Agatston scores?
Observational (n=459)
No
Does non-contrast CT PCAT radiomics predict coronary artery plaques as effectively as CCTA PCAT radiomics in patients with suspected CAD and low Agatston scores?
Effect estimate: r = 0.920 (95% CI -0.443-0.045)
p-value: p=<0.001
Non-contrast CT PCAT radiomics demonstrates comparable efficacy to CCTA PCAT radiomics for predicting coronary artery plaques in patients with low Agatston scores, potentially reducing the need for contrast.
May support non-contrast CT radiomics to limit contrast exposure; hypothesis-generating and requires prospective validation before practice change.
RATIONALE AND OBJECTIVES: Patients with a low Agatston score often present with clinical signs and symptoms suggestive of coronary artery disease, despite having minimal calcium deposits. This study aimed to compare the efficacy of low-dose non-contrast cardiac CT with coronary computed tomography angiography (CCTA) in pericoronary adipose tissue (PCAT) radiomics for predicting coronary artery plaques, using CCTA as the reference standard. MATERIALS AND METHODS: This retrospective study analyzed 459 patients with suspected coronary artery disease and a coronary artery calcium score < 100 Agatston units, who were treated between June 2021 and December 2023 at a tertiary hospital. Three predictive models for coronary artery plaques were developed: (1) a clinical factor model, (2) a hybrid model integrating clinical factors and CT PCAT radiomics, and (3) a hybrid model integrating clinical factors and CCTA PCAT radiomics. Multivariable logistic regression and receiver operating characteristic curve evaluations were performed to develop and validate predictive models. RESULTS: Both hybrid models showed significant correlations in the training set (r = 0.890, P < 0.001) and the validation set (r = 0.920, P < 0.001). The mean agreement in the training set is 0, with 3.42% (11/322) of the data points outside the 95% CI (-0.18-0.18, P < 0.001). The mean agreement in the validation set is -0.244, with 6.57% (9/137) of the data points outside the 95% CI (-0.443-0.045, P < 0.001). CONCLUSIONS: Non-contract CT PCAT radiomics showed comparable efficacy to CCTA PCAT radiomics in predicting coronary artery plaques among patients with low Agatston scores.
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Jianhua et al. (2024) conducted an observational in Suspected coronary artery disease with low Agatston score (n=459). Non-contrast CT pericoronary adipose tissue (PCAT) radiomics vs. Coronary computed tomography angiography (CCTA) PCAT radiomics was evaluated on Prediction of coronary artery plaques (r = 0.920, 95% CI -0.443-0.045, p=<0.001). Non-contrast CT PCAT radiomics showed comparable efficacy to CCTA PCAT radiomics in predicting coronary artery plaques among patients with low Agatston scores (validation set r=0.920, P<0.001).
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