Objective The purpose of this study was to develop a combined radiomics model to predict coronary plaque texture using perivascular fat CT radiomics features combined with clinical risk factors. Methods The data of 200 patients with coronary plaques were retrospectively analyzed and randomly divided into a training group and a validation group at a ratio of 7:3. In the training group, The best feature set was selected by using the maximum correlation minimum redundancy method and the least absolute shrinkage and selection operator. Radiomics models were built based on different machine learning algorithms. The clinical risk factors were then screened using univariate logistic regression analysis. and finally a combined radiomics model was developed using multivariate logistic regression analysis to combine the best performing radiomics model with clinical risk factors and validated in the validation group. The efficacy of the model was assessed by a receiver operating characteristic curve, the consistency of the nomogram was assessed using calibration curves, and the clinical usefulness of the nomogram was assessed using decision curve analysis. Results Twelve radiomics features were used by different machine learning algorithms to construct the radiomics model. Finally, the random forest algorithm built the best radiomics model in terms of efficacy, and this was combined with age to construct a combined radiomics model. The area under curve for the training and validation group were 0.98 (95% confidence interval, 0.95–1.00) and 0.97 (95% confidence interval, 0.92–1.00) with sensitivities of 0.92 and 0.86 and specificities of 0.99 and 1, respectively. The calibration curve demonstrated that the nomogram had good consistency, and the decision curve analysis demonstrated that the nomogram had high clinical utility. Conclusions The combined radiomics model established based on CT radiomics features and clinical risk factors has high value in predicting coronary artery calcified plaque and can provide a reference for clinical decision-making.

Background Dual Energy spectral computed tomography (DECT) provides a variety of image data sets that can be used to improve the assessment of fat content. Purpose To investigate the clinical value of DECT in the quantitative assessment of pancreatic fat content in patients with type 2 diabetes mellitus (T2DM). Material and Methods The DECT data of 123 patients were retrospectively analyzed, including a case group of 82 patients with T2DM and a control group of 41 patients with normal physical examination findings. The CT value, fat (water) concentration and slope of the spectral curve were measured in both groups. The T2DM group was divided into the T2DM obese subgroup (body mass index [BMI] of ≥ 25 kg/m2) and T2DM non-obese subgroup (BMI of < 25 kg/m2) according to the Asia-Pacific classification criteria for BMI. The differences between the T2DM non-obese subgroup and the control group and between the T2DM obese subgroup and the T2DM non-obese subgroup were compared, and the correlation of the BMI with the fat (water) concentration, CT value, and slope was analyzed in all patients with T2DM. Results The CT value was significantly lower and the fat (water) concentration and slope were significantly higher in the T2DM obese subgroup than in the control group (P < 0.05 for all). The BMI was positively correlated with the fat (water) concentration and slope (r = 0.350 and 0.372, respectively; P < 0.01) and negatively correlated with the CT value (r = − 0.508, P < 0.01). Conclusion DECT is noninvasive and effective for quantitative assessment of the pancreatic fat content.

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