Objective:To explore the predictive value of deep learning based on three dimensional deep residual network(3D Res-Unet)model for the dose accuracy of postoperative volume modulated arc therapy(VMAT)plan of endometrial carcinoma.Methods:A retrospective collection of 154 VMAT radiotherapy plans for endometrial carcinoma from The First People's Hospital of Neijiang was conducted.The data set was divided into one training set with 108 cases,one validation set with 15 cases and one test set with 31 cases as the ratio of 7:1:2 through randomly sampling.The approved dose of clinical application was used as"gold standard"to compare the difference between predictive radiotherapy dose of 3D Res-UNet and clinically radiotherapy dose.Results:There were statistical differences in the conformity index(CI)of target area and average dose(Dmean)between deep learning and clinical gold standard(t=-3.115,-0.124,P<0.05),and the difference of bladder V40 of organ at risk(OAR)between them was significant(t=0.510,P<0.05),and the difference of rectum V50 between them was significant(t=-2.121,P<0.05).The predictive dose of the left femoral head V30 was significantly lower than that of clinical dose(t=0.415,P<0.05).The predictive dose of the right femoral head V30 was significantly lower than that of clinical dose(t=-3.102,P<0.05).The predictive dose of pelvic Dmean was significantly higher than that of clinical dose(t=1.224,P<0.05).The predictive dose of small intestine V40 was significantly higher than that of clinical dose(t=0.461,P<0.05).There were no statistically significant difference in other indicators(P>0.05).The difference plot of dose showed that there was few difference between predictive results and clinical results,and the dose volume histogram of prediction basically coincided with that of clinical application.Conclusion:The 3DRes-UNet model can effectively predict the three-dimensionally spatial dose of VMAT plan after surgery for endometrial carcinoma,which can guide clinical radiotherapy work.
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