Nomogram Predicting Adverse Pathology Outcome on Radical Prostatectomy in Low-Risk Prostate Cancer Men

OBJECTIVE To develop and validate a prediction model to predict the risk of adverse pathology outcome on final pathology in low-risk prostate cancer (PCa) men. MATERIALS AND METHODS This study was a monocentric retrospective analysis of 426 men who underwent radical prostatectomy (RP) for low-risk PCa. The validation cohort included 103 men from another hospital. Adverse pathology outcome was defined either by upgrading on RP Gleason Score (GS) (from GS 3+3 to GS >= 3+4 with Gleason pattern 4 >= 10%) or a non-organ confined disease (pathologic stage >= pT3a). Multivariable logistic regression analysis was performed to build nomogram for predicting adverse pathology outcome. Nomogram validation was performed by calculating the area under receiver operating characteristic curves (AUC) and comparing nomogram-predicted probabilities with actual rates of adverse pathology outcome in the external cohort. The Kaplan-Meier method was used to estimate and compare the biochemical recurrence-free survival rates between the two groups. RESULTS Of 426 men in the development cohort, 45.7% showed adverse pathology outcome on RP. Age, body mass index, prostate specific antigen density, history of prior negative biopsy, magnetic resonance imaging prostate imaging reporting and data system score 4-5 and percentage of positive biopsies were significant predictors in multivariate analysis. A nomogram was constructed with an area under curve of 87%. There was agreement between predicted and actual rates of adverse pathology outcome in the validation cohort. The 5-year biochemical recurrence-free survival rates in patients with and without adverse pathology outcome was 70% and 98%, respectively. CONCLUSION This novel nomogram would help identify low-risk PCa men at risk of adverse pathology outcome and can be relevant for treatment decision-making. (c) 2022 Elsevier Inc.

Automated summary

This study developed and validated a prediction model to assess the risk of adverse pathology outcome in low-risk prostate cancer patients. The model showed good accuracy in predicting adverse pathology outcomes and could aid in treatment decision-making for this patient population.