Objective This study aims to analyze the imaging features of CE1-type hepatic cystic echinococcosis and liver cysts on CT plain scan images,with the goal of establishing a machine learning classifier for predictive purposes.Methods The study retrospectively gathered CT plain scan image data from 48 patients diagnosed with CE1-type hepatic cystic echinococcosis who underwent surgical treatment.Additionally,data were collected from 119 patients with liver cysts treated.These datasets were utilized for developing the classifier.Furthermore,CT plain scan image features from 24 CE1-type hepatic cystic echinococcosis patients and 28 liver cyst patients in the Guoluo Yushu area were collected for external validation.Image standardization and resampling were conducted using Python to mitigate bias arising from diverse devices and parameters.LASSO regression was employed for image feature selection,and the features were partitioned into training and testing sets in a 7∶3 ratio.A classifier incorporating ten machine learning algorithms logistic regression(LR),decision tree(DT),random forest(RF),k-nearest neighbors(KNN),Catboost,XGBoost,LightGBM,Adaboost,multilayer percep-tron(MLP),and naive Bayes(GNB)was constructed.Hyperparameters were fine-tuned through a 3-fold cross-grid search to optimize classifier performance.Evaluation of the classifier's performance on the testing and validation sets involved met-rics such as AUC,accuracy,F1 score,sensitivity,and specificity.Results LASSO regression identified 15 image features,encompassing 1 morphological feature,10 texture features,and 3 first-order features.Notably,10 features underwent wavelet filtering.In the testing set,the Catboost classifier demonstrated superior performance with an AUC of 0.979 and accuracy of 0.961.During external validation,the Catboost classifier showcased optimal results with an area under the ROC curve of 0.932 and accuracy of 0.904.Conclusion Conducting imaging omics research on cystic echinococcosis using CT plain scan is deemed feasible.The incorporation of wavelet filtering in image feature extraction is deemed essential.The Catboost clas-sifier stands out for its excellent performance and robust generalization ability in classifying imaging features between liver CE1-type hepatic cystic echinococcosis and liver cysts.
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