Prediction of Adrenal Venous Sampling Outcome in Patients with PA Using Radiomics and Automated Machine Learning
Objective To build a preoperative prediction model for the subtype classification of primary aldosteronism(PA)based on enhanced high-resolution CT and automated machine learning techniques.Methods A retrospective study was conducted on 312 patients with PA diagnosed by subtypes of adrenal venous sampling(AVS).Among them,207 were diagnosed with unilateral dominance(AVS right∶AVS left=93∶114),and 105 were diagnosed with bilateral dominance.Initial CT images were retrospectively included and radiomics features were extracted from bilateral adrenal based on thin layer venous phase images.The quotient radiomics features were defined as the left-right ratio of bilateral adrenals radiomics features,and then input feature vectors into automatic machine learning for model training.Results According to the automatic model screening,the random forest classifier achieved good overall performance in predicting AVS results,with an accuracy of 0.7500,a recall rate of 0.7466,and an area under operating receiver characteristic curve of 0.8792.Conclusion This system has shown certain potential in predicting AVS outcomes in PA patients.Therefore,the machine learning model can assist in predicting the subtype diagnosis of PA in routine clinical practice.