Application of Magnetic Resonance Biparametric Radiomics Machine Learning in the Diagnosis of Significant Prostate Cancer
Objective To explore the diagnostic value of machine learning model based on magnetic resonance two-parameter texture analysis in clinically significant prostate cancer.Methods A retrospective inclusion of 222 patients with pathologically confirmed prostate cancer by preoperative magnetic resonance examination and needle biopsy between January 2018 and January 2023 was included.Among them,there were 117 cases of clinically significant carcinoma(Gleason≥7)and 105 cases of non-clinically significant carcinoma(Gleason＜7).All patients were treated with ITK-SNAP software to delineate all levels of the lesion as region of interest(ROI),and 504 radiomics features in the ROI were extracted by the radiomics software FAE(V.0.54).The 222 patients were randomly divided into training group and test group according to the ratio of 7∶3.The radiomics features were screened by different methods such as linear discriminant analysis(LDA),random forest(RF),Logistic regression(LR),and support vector machine(SVM).Select an optimal model based on the model's AUC,sensitivity,specificity,PPV,NPV,confidence interval,etc.on the test set.Results The linear discriminant classifier LDA model based on dwi_original_firstorder_Variance,dwi_original_glcm_ClusterProminence,adc_original_firstorder_Mean,and adc_original_firstorder_Median4 features can obtain the highest AUC on the verification dataset.AUC and accuracy reach 0.764 and 0.769,respectively,The influence of AUC and accuracy on the test data set reached 0.950 and 0.909,respectively.Conlusion The magnetic resonance biparametric radiomics machine learning model has higher accuracy in diagnosing clinically significant prostate cancer,and the machine learning model using LDA method has higher diagnostic performance than other models.
Magnetic Resonance BiparameterRadiomicsMachine LearningClinically Significant Prostate Cancer
万方数据
维普
