Preoperative prediction of pathological grading in bladder cancer by deep residual network model based on T2WI
Objective:To develop and validate the efficacy of a deep learning(DL)model by 50-layer deep residual network(ResNet-50)based on T2WI for preoperative prediction of preoperative pathological grading of bladder cancer(BCa).Materials and Methods:A total of 211 tumors in 169 BCa patients[109 for training and 47 for internal test,from the Tenth Affiliated Hospital of Southern Medical University(centre 1);55 for external test,from Sun Yat-sen University Cancer centre(centre 2)]were enrolled,including 111 tumors of high grade uroepithelial carcinoma(HGUC)and 100 tumors of low grade uroepithelial carcinoma(LGUC).Grade determination was confirmed by pathological examination.ResNet-50 was used to construct the models based on the internal training set from centre 1.The optimal model was selected from the resulting models after being tested on the internal test set from centre 1 and validated independently on the external test set from centre 2.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC),accuracy,sensitivity,and specificity,with feature visualization images presented.Results:In the internal test set,we achieved an AUC of 0.856[95％confidence interval(CI):0.723-0.941],accuracy of 80.9％(95％CI:69.6％-92.1％),sensitivity of 77.8％(95％CI:65.9％-89.7％),and specificity of 82.8％(95％CI:72.0％-93.6％).In the external test set,we achieved an AUC of 0.814(95％CI:0.686-0.906),accuracy of 78.2％(95％CI:67.3％-89.1％),sensitivity of 77.3％(95％CI:66.2％-88.3％),and specificity of 81.8％(95％CI:71.6％-92.0％).Feature visualization showed that the activated regions overlapped with the BCa lesions largely,indicating the DL model identified the target area of BCa correctly.And the t-distributed stochastic neighbor embedding(T-SNE)helped to distinguish HGUC from LGUC in a certain extent.Conclusions:This study is the first to establish a preoperative BCa pathological grading prediction model based on T2WI using DL methods and be validated across two centres.With high prediction accuracy,the model is non-invasive,objective,and has strong repeatability and generalization performance,which contribute to more accurate clinical preoperative diagnosis.
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