Abstract: Background　The pathological stage after robot-assisted radical prostatectomy (RARP) affects the prognosis of patients. However, factors affect the upstaging of low-intermediate risk prostate cancer (PCa) after RARP remain unclear. Objective　To analyze the independent risk factors of pathological stage upstaging after RARP in patients with low-intermediate risk PCa, and construct and validate a prediction model.Methods　A retrospective analysis was conducted on the clinical data about 215 patients with low-intermediate risk PCa who underwent RARP at The First Medical Center of PLA General Hospital from January 2017 to December 2023. The pathological stage upstaging was defined as the clinical stage T1 - T2, while the postoperative pathological stage was pT3 - T4. Logistic regression analysis was utilized to identify factors linked to pathological stage upstaging and to develop a predictive model. The prediction model was evaluated by area under curve (AUC) of receiver operating characteristic curve (ROC), calibration curve and decision curve analysis. A 1000-time Bootstrap resampling strategy was employed for internal validation. The clinical data about the same type of patients in The Third Medical Center of PLA General Hospital from January 2022 to June 2024 were used for external validation.Results　Among the 215 patients included in the study, 37 cases (17.2%) presented with pathological stage upstaging after the operation. According to univariate logistic regression analysis, significant differences were observed in the prostate volume, the PSA density (PSAD), the percentage of positive biopsies, the ISUP grade on biopsy, and the clinical stage between the two groups (P＜0.05). Multivariate logistic regression analysis identified prostate volume (OR: 0.954,95% CI: 0.922-0.988, P=0.008), positive biopsy percentage ≥30% (OR: 3.697, 95% CI: 1.345-10.163, P=0.011), and biopsy ISUP grade 3 (OR: 2.988, 95% CI: 1.110-8.043, P=0.030) as risk factors for pathological stage upstaging. The internal validation AUC was 0.790 (95% CI: 0.707 - 0.867), and the external validation AUC was 0.737 (95%CI: 0.575 - 0.890). The calibration curves for both internal and external validation demonstrated good consistency. DCA results suggested that the nomogram model provided a higher net clinical benefit within the threshold range.Conclusion　The novel nomogram, developed using prostate volume, the percentage of positive biopsy cores, and the ISUP grade on biopsy, can accurately predict pathological stage upstaging. This model demonstrates favorable predictive performance through both internal and external validation.