OBJECTIVE: The aim of this study was to develop a nomogram to predict the probability of insignificant prostate cancer. MATERIALS AND METHODS: A retrospective analysis was conducted of patients who underwent radical prostatectomy at a Korean hospital between January 2005 and December 2014. Patients with pathologically insignificant prostate cancer were defined as having organ-confined disease with tumor volume less than 0.5 cm³ without Gleason scores of 4 or 5. Multivariable logistic regression analysis with a stepwise selection was used to model the relationship between preoperative characteristics and insignificant prostate cancer, and a nomogram to predict the probability of insignificant prostate cancer was created. Receiver operating characteristics (ROC) analysis was performed to assess the predictive value of the model. RESULTS: The final study population consisted of 1343 patients. Among these patients, insignificant prostate cancer was confirmed in 188 men (14.0%) at the time of prostatectomy. Six independent predictors of insignificant prostate cancer were identified: number of positive cores (p < 0.001), maximal single core tumor involvement (p < 0.001), biopsy Gleason score (p < 0.001), prostate volume (p = 0.024), patient age (p < 0.001) and prostate-specific antigen density (p < 0.001) in the multivariable model. A nomogram to predict insignificant prostate cancer was developed using these six preoperative characteristics. The area under the ROC curve for nomogram predictions was 0.87. CONCLUSION: The nomogram developed in this paper identifies the probability of insignificant prostate cancer and gives providers more information to guide their clinical decisions.
OBJECTIVE: The aim of this study was to develop a nomogram to predict the probability of insignificant prostate cancer. MATERIALS AND METHODS: A retrospective analysis was conducted of patients who underwent radical prostatectomy at a Korean hospital between January 2005 and December 2014. Patients with pathologically insignificant prostate cancer were defined as having organ-confined disease with tumor volume less than 0.5 cm³ without Gleason scores of 4 or 5. Multivariable logistic regression analysis with a stepwise selection was used to model the relationship between preoperative characteristics and insignificant prostate cancer, and a nomogram to predict the probability of insignificant prostate cancer was created. Receiver operating characteristics (ROC) analysis was performed to assess the predictive value of the model. RESULTS: The final study population consisted of 1343 patients. Among these patients, insignificant prostate cancer was confirmed in 188 men (14.0%) at the time of prostatectomy. Six independent predictors of insignificant prostate cancer were identified: number of positive cores (p < 0.001), maximal single core tumor involvement (p < 0.001), biopsy Gleason score (p < 0.001), prostate volume (p = 0.024), patient age (p < 0.001) and prostate-specific antigen density (p < 0.001) in the multivariable model. A nomogram to predict insignificant prostate cancer was developed using these six preoperative characteristics. The area under the ROC curve for nomogram predictions was 0.87. CONCLUSION: The nomogram developed in this paper identifies the probability of insignificant prostate cancer and gives providers more information to guide their clinical decisions.