Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.

Objective To investigate the diagnostic efficiency of patients with human epidermal growth factor receptor-2(HER-2)over expressed breast cancer via combining the dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)perfusion parameters,apparent diffusion coefficient(ADC)value and clinical feature model.Methods A total of 197 breast cancer patients who underwent DCE-MRI and diffusion weighted imaging(DWI)scans were analyzed retrospectively,including 47 breast cancer patients with HER-2 over expressed and 150 breast cancer patients with non-HER-2 over expressed.The t-test or chi-square test was used to compare the DCE-MRI perfusion parameters[Ktrans,Kep,Ve,W-in,W-out,and time to peak(TTP)],ADC value,and clinical feature between the two groups.The diagnostic efficiency of the models were analyzed via receiver operating characteristic(ROC)curves.Results There were significant difference in the maximum tumor diameter,minimum tumor diameter,T stage,N stage,Kep,W-in,and ADC value between HER-2 over expressed breast cancer and non-HER-2 over expressed breast cancer groups(P＜0.05).The proposed combined model,which included the combined maximum tumor diameter,minimum tumor diameter,T stage,N stage,Kep,W-in,and ADC value,showed a better diagnostic efficiency with area under the curve(AUC)(AUC=0.763)than the clinical model(AUC=0.634)based on the combined maximum tumor diameter,minimum tumor diameter,T stage,and N stage,and the imaging model(AUC=0.715)based on the combined Kep,W-in and ADC value.Conclusion The maximum tumor diameter,minimum tumor diameter,T stage,N stage,Kep,W-in,and ADC value may be associated with HER-2 over expressed breast cancer.Combining all above parameters can improve the diagnostic ability of breast cancer patients with HER-2 over expressed.