Purpose: We have previously have reported a tree structured regression model for predicting SS-ECE. Others recently reported a logistic regression based SS-ECE nomogram. We developed a nomogram and compared the performance and discriminant properties of the tree regression and the nomogram in a contemporary cohort of European patients treated with radical retropubic prostatectomy.
Materials and methods: The cohort consisted of 1,118 patients with pretreatment prostate specific antigen 0.1 to 73.2 ng/ml (median 6.6). Each of the 2,236 prostate lobes was considered separately. Clinical stage, pretreatment PSA, biopsy Gleason sum, percent positive cores and percent cancer in the biopsy specimen were used as predictors in a logistic regression model predicting SS-ECE. Regression coefficients were then used to generate an SS-ECE nomogram. Performance characteristics and discriminant properties of the previously published tree regression were also tested in the same cohort. For internal validation and to decrease overfit bias 200 bootstrap re-samples were applied to accuracy estimates for each method.
Results: ECE was present in 303 of 1,118 radical retropubic prostatectomy specimens (27%) and in 385 lobes (17%). In logistic regression models all variables were statistically significant multivariate predictors of SS-ECE except the percent of positive biopsy cores (p = 0.7). Bootstrap corrected predictive accuracy of the SS-ECE nomogram was 0.840 vs 0.700 for the tree regression model.
Conclusions: Logistic regression based nomogram predictions of SS-ECE are highly accurate and represent a valuable aid for assessing the risk of ECE prior to surgery.