Abstract
OBJECTIVE: To compare the performance of a systems-based risk assessment tool with standard defined risk groups and the 10-year postoperative nomogram for predicting disease progression, including biochemical relapse and clinical (systemic) failure. PATIENTS AND METHODS: Clinical variables, biometric profiles and outcome results from a training cohort comprising 373 patients in a published postoperative systems-based prognostic model were obtained. Patients were stratified according to D'Amico standard risk groups, Kattan 10-year postoperative nomogram and prognostic scores from the postoperative tissue model. The association of pathological variables and calculated risk groups with biochemical recurrence and clinical (systemic) failure was assessed using the concordance index (C-index) and hazard ratio (HR). RESULTS: Systems-based post-prostatectomy models to predict significant disease progression (post-treatment clinical failure) were more accurate than the D'Amico defined risk groups and the Kattan 10-year postoperative nomogram (systems model: C-index, 0.84; HR, 17.46; P < 0.001 vs D'Amico: C-index, 0.73; HR, 11; P = 0.001; 10-year nomogram: C-index, 0.79; HR, 5.06; P < 0.001). The systems models were also more accurate than standard risk groups for predicting prostate-specific antigen recurrence (systems model: C-index, 0.76; HR, 8.94; P < 0.001 vs D'Amico C- index, 0.70; HR, 4.67; P < 0.001) and showed incremental improvement over the 10-year postoperative nomogram (C-index, 0.75; HR, 5.83; P < 0.001). The postoperative tissue model provided additional risk discrimination over surgical margin status and extracapsular extension for predicting disease outcome, and was most significant for the clinical (systemic) failure endpoint (surgical margin: C-index, 0.58; HR, 1.57; P= 0.2; extracapsular extension: C-index, 0.62; HR, 2.06; P = 0.04). CONCLUSIONS: Risk assessment models that incorporate characteristics from the patient's own tumour specimen are more accurate than clinical-only nomograms for predicting significant disease outcome. Systems-based tools should provide useful information concerning the appropriate receipt of adjuvant therapy in the post-surgical setting.