Objective To evaluate the value of coronary CT angiography(CCTA)radiomics machine learning(ML)model combined with pericoronary fat attenuation index(FAI)for predicting coronary plaques progression.Methods Totally 194 patients with CCTA showing coronary plaques and received at least one CCTA review afterwards were retrospectively collected.The annual change value of total plaque burden(△TPB/y)was calculated based on the first and last CCTA to assess plaque progression.All patients were categorized into non-progressive(△TPB/y＜median △TPB/y)and progressive(△ TPB/y≥median △ TPB/y)groups.The patients were divided into training set(n=155)and validation set(n=39)at the ratio of 8∶2.Univariate and multivariate logistic regression analyses were used to screen clinical and primary CCTA related factors for plaque progression,and CCTA model was constructed.Radiomics features were extracted and screened based on primary CCTA to build ML models using random forest(RF),Gaussian process(GP),partial least squares discriminant analysis(PLS-DA),quadratic discriminant analysis(QDA)and support vector machine(SVM)algorithms.The effectiveness of all models was verified in validation set and the optimal ML model was selected.And its combination with CCTA model constructed combined model.The efficacy of each model for predicting coronary plaques progression was evaluated.Results Of 194 cases,97 were in progressive group and 97 were in non-progressive group.The training set included 77 cases of plaques progression and 78 of plaques non-progression,and the validation set included 20 of plaques progression and 19 of plaques non-progression.FAI was the independent predictor of plaque progression(OR=1.08,P＜0.001)and CCTA model was constructed.Ten optimal radiomics features based on training set were selected to build RF,GP,PLS-DA,QDA and SVM models.The area under the curve(AUC)of RF model in training set and validation set were both high,was considered as the optimal ML model.The AUC of CCTA,RF and combined models in training set was 0.684,0.847 and 0.861,respectively,while was 0.629,0.768 and 0.821 in validation set,respectively.Conclusion CCTA radiomics ML model combined with FAI could effectively predict coronary plaques progression.

Objective To evaluate the value of coronary CT angiography(CCTA)radiomics machine learning(ML)model combined with pericoronary fat attenuation index(FAI)for predicting coronary plaques progression.Methods Totally 194 patients with CCTA showing coronary plaques and received at least one CCTA review afterwards were retrospectively collected.The annual change value of total plaque burden(△TPB/y)was calculated based on the first and last CCTA to assess plaque progression.All patients were categorized into non-progressive(△TPB/y＜median △TPB/y)and progressive(△ TPB/y≥median △ TPB/y)groups.The patients were divided into training set(n=155)and validation set(n=39)at the ratio of 8∶2.Univariate and multivariate logistic regression analyses were used to screen clinical and primary CCTA related factors for plaque progression,and CCTA model was constructed.Radiomics features were extracted and screened based on primary CCTA to build ML models using random forest(RF),Gaussian process(GP),partial least squares discriminant analysis(PLS-DA),quadratic discriminant analysis(QDA)and support vector machine(SVM)algorithms.The effectiveness of all models was verified in validation set and the optimal ML model was selected.And its combination with CCTA model constructed combined model.The efficacy of each model for predicting coronary plaques progression was evaluated.Results Of 194 cases,97 were in progressive group and 97 were in non-progressive group.The training set included 77 cases of plaques progression and 78 of plaques non-progression,and the validation set included 20 of plaques progression and 19 of plaques non-progression.FAI was the independent predictor of plaque progression(OR=1.08,P＜0.001)and CCTA model was constructed.Ten optimal radiomics features based on training set were selected to build RF,GP,PLS-DA,QDA and SVM models.The area under the curve(AUC)of RF model in training set and validation set were both high,was considered as the optimal ML model.The AUC of CCTA,RF and combined models in training set was 0.684,0.847 and 0.861,respectively,while was 0.629,0.768 and 0.821 in validation set,respectively.Conclusion CCTA radiomics ML model combined with FAI could effectively predict coronary plaques progression.