Purpose:To develop a patient-specific rectal toxicity predictor guided plan quality control tool for prostate SBRT plans.Methods:For prostate SBRT cases,rectal wall is identified as organs at risk and the circumference of rectal wall receiving more than 39 Gy(CRW39)and 24 Gy(CRW24)are the rectal toxicity predictors.In this new geometry-dosimetry model,a patient geometry descriptor,differential circumference of rectal wall(dCRW)is used as model input geometry parameters and plan dosimetric endpoints CRW39 and CRW24 are output dosimetric parameters.Linear models are built to correlate dCRW to both CRW39 and CRW24 and established with both a linear regression method and a modified bagging ensemble machine learning method.27 SBRT prostate cases are retrospectively studied from a dose-escalated clinical trial research.20 prescribed 50 Gy SBRT cases are recruited to train the model and the other rescaled 7 cases are used to evaluated model feasibility and accuracy.Results:Each solved linear coefficient sequence related to CRW39 or CRW24 is a one-dimensional decreasing function of the distance from the PTV boundary,indicating that the different locations of each rectal circumference have different contributions to each particular dosimetric endpoint.The fitting errors for those trained 20 prostate SBRT cases are small with mean values of 2.39%,2.45%relative to the endpoint values for SBRT rectal toxicity predictor CRW39 and CRW24 respectively.1 out of 7 evaluation plans is identified as poor quality plan.After re-planning,the CRW39 and CRW24 can be reduced by 3.34%and 3%,without sacrificing PTV coverage.Conclusion:The proposed patient geometry-plan toxicity predictor model for SBRT plans can be successfully applied to plan quality control for prostate SBRT cases.