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.

Objective To observe the value of diffusion kurtosis imaging(DKI)radiomics for evaluating Parkinson disease(PD).Methods Totally 76 PD patients(PD group)and 80 healthy controls(HC group)were retrospectively analyzed.The subjects were divided into training set(n=125,including 61 PD and 64 HC)and test set(n=31,including 15 PD and 16 HC)at the ratio of 8:2.ROI of bilateral substantia nigra,caudate nucleus,putamen,globus pallidus and thalamus were automatically delineated on mean kurtosis(MK)images of cerebral DKI.The mean MK values(MKmean)of the above ROIs were obtained and compared between groups.Support vector machine(SVM)model was constructed based on 50 selected optimal texture features.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of SVM model for evaluating PD.Results MKmean of bilateral substantia nigra,caudate nucleus and thalamus in PD group were all significantly lower than those in HC group(all P＜0.05).No significant difference of MKmean of bilateral putamen nor globus pallidus was found between groups(all P＞0.05).The sensitivity,specificity,accuracy and AUC of SVM model for evaluating PD in training set was 86.89％,93.75％,90.40％and 0.982,respectively,which in test set was 86.67％,93.75％,90.32％and 0.958,respectively.Conclusion DKI radiomics could be used to effectively evaluate PD through description of microstructural changes of cerebral nuclei.