Background: Currently, identification of individual patients who are truly at risk of developing lethal prostate cancer after radical prostatectomy (RP) is based on clinical nomograms. A prospectively validated genomic classifier (Decipher) has been shown to more accurately predict metastatic disease post RP than established clinical predictors and can identify patients with adverse pathology who may be cured by RP alone and may therefore not require additional treatment. Methods: An IRB-approved study assessed the impact of a genomic classifier (GC) test in 240 pathologically high-risk post RP case reviews. Twenty (20) urologic oncologists from 18 institutions reviewed 12 cases presented in a randomized, de-identified fashion via a secure online platform to provide treatment recommendations pre- and post- patient GC test results. Possible recommendations included referral to radiation oncologist and/or initiation of adjuvant hormones, close observation, or other. The primary endpoint was any change in treatment recommendation after unblinding of GC test results. Confidence in treatment recommendations was assessed using a 5-point Likert scale. Results: Following unblinding of GC test results, treatment recommendations changed in 43% (95% CI: 37-49) of all cases. Specifically, among cases with a pre-GC recommendation involving treatment, 31% (95% CI: 23-41) of respondents changed their recommendation to observation post-GC.Respondents considered the GC result to have influenced their recommendation in 63% (95% CI: 56-68) of cases. The addition of information provided by the GC result increased decision making confidence in 39% (95% CI: 30-49) of cases where a change of treatment recommendation was made. Following unblinding, physicians reported that the GC result was clinically relevant in 84% (95% CI: 79-84) of cases. Conclusions: GC appears to influence treatment recommendations and decision making confidence for high-risk prostatectomy patients. This study suggests that clinical implementation of GC may potentially impact treatment recommendations.