Prediction of complicated and uncomplicated acute appendicitis using CT-based radiomics combined with machine learning models
Objective To explore and validate the value of various radiomics models in the preoperative discrimination between complicated and uncomplicated acute appendicitis.Methods retrospective analysis of clinical data and CT plain images from 212 surgically confirmed acute appendicitis cases was conducted.Ra-diomic features were extracted from CT images,following feature reduction and selection.Various algorithms including Logistic Regression,Support Vector Machine(SVM),and Random Forest were employed to con-struct radiomics models.Model performance was evaluated by comparing metrics such as the Area Under the Receiver Operating Characteristic(ROC)Curve(AUC),accuracy,and 95％ confidence intervals(95％ CI)to determine the optimal radiomics model.Additionally,univariate and multivariate Logistic Regression analyses were performed to select clinical features and establish a clinical model.A combined model was developed by integrating radiomic labels with clinical labels using multivariate logistic regression.Finally,ROC curve analy-sis was conducted to assess the model's performance,and Decision Curve Analysis(DCA)was conducted to e-valuate its clinical utility.Results The final selection included age and C-reactive protein as the two clinical features.From each patient's CT images,a total of 1 834 radiomic features were extracted,16 most valuable features identified.Among the radiomics models,SVM exhibited the highest predictive efficiency and stability,with AUCs of 0.916(95％ CI:0.862～0.970)in the training set and 0.842(95％ CI:0.739～0.945)in the test set.In all models,the combined model showed the best diagnostic performance,with AUCs of 0.943(95％ CI:0.896～0.990)in the training set and 0.855(95％ CI:0.759～0.951)in the test set.DCA sugges-ted that the combined model had superior predictive performance and clinical value.Conclusion The com-bined model integrating radiomic and clinical features demonstrates robust predictive ability in distinguishing between complicated and uncomplicated acute appendicitis,providing a non-invasive and effective approach for clinical decision-making and potentially avoiding unnecessary surgical resection.
万方数据
维普
