Objective To construct a machine learning model based on ultrasound radiomics,and to explore the application value in the differentiating cystic echinococcosis and alveolar echinococcosis.Methods A total of 4976 patients diagnosed with hepatic echinococcosis in our hospital and Shiqu county,Ganzi tibetan autonomous prefecture,Sichuan province according to pathological results(gold standard)or diagnosed by the"Sichuan Province Echinococcosis Expert Group"based on clinical outcomes and expert consensus(silver standard)were selected,including a total of 23 452 ultrasound images,with 8557 images of cystic echinococcosis and 14 895 images of alveolar echinococcosis.The ultrasound images were randomly divided into training set(18 762 images)and independent test set(4690 images)in a 8∶2 ratio according to the lesion type.Pyradiomics(3.1.0)was used to extract radiomic features from ultrasonographic images,and the same extractor was applied for the ultrasonic images of the training set and the independent test set.MinMax,Z-score and Mean methods were used for feature scaling of radiomic features.Principal component analysis and Pearson correlation coefficient were used for feature dimensionality reduction,and analysis of variance,recursive feature elimination,relevant features,as well as the Kruskal-Wallis methods were used to screen the best image radiomic features.The machine learning models were constructed based on 8 classifiers,including support vector machine(SVM),auto-encoder(AE),linear discriminant analysis(LDA),random forest(RF),Logistic regression(LR),adaptive boosting(AB),decision tree(DT)and naive Bayes(NB).In the training set,a ten-fold cross-validation strategy was employed to train the model.Receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic performance of the best machine learning model in different classifiers in the training set and the independent test set in differentiating the classification of hepatic echinococcosis.Results A total of 1130 radiomic features were extracted from each ultrasonic image,and 1～40 optimal radiomic features were dynamically selected by feature selection to establish the optimal machine learning models.ROC curve analysis showed that the area under the curve of RF model for the classification of hepatic echinococcosis in the training and independent test sets were 0.82 and 0.86,respectively,which were higher than those of the SVM,AE,LDA,LR,AB,DT and NB models,and the differences were statistically significant(all P<0.05).Conclusion The RF model based on ultrasound radiomics demonstrates the optimal diagnostic efficacy in differentiating the classification of hepatic echinococcosis,which is helpful for the precise ultrasound diagnosis of the disease.
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