Objective To explore the value of machine learning models based on MRI radiomics features in predicting the degree of meniscus damage.Methods Knee MRI images of 368 patients were retrospectively analyzed.Combined with sagittal and coronal proton density-weighted fat suppression images,the SelectKBest,Minimum redundancy maximum relevance and least absolute shrinkage and selection operator were used to select and reduce the dimension of the radiomics features.Then,based on the optimal features,a variety of machine learning methods are used to build a four-category pre-diction model,and its performance was evaluated by the receiver operating characteristic(ROC)curve.Results Finally,18 optimal features are obtained.The Macro AUC values of support vector machine,logistic regression,Gaussian process,random forest,quadratic discriminant analysis and Bagging decision tree model were the best,which were 0.876,0.871,0.870,0.869,0.868 and 0.868,respectively.The AUC values of random forest,logistic regression,Bagging decision tree and random forest were the highest(0.948,0.833,0.805,0.902)in the diagnosis of normal meniscus injury,grade 1,grade 2,grade 3 meniscus damage,respectively.Conclusion The machine learning model based on MRI radiomics fea-tures for meniscus damage is feasible and has good diagnostic efficiency.
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