title: Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10-year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery.
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.
  
  
  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.
authors.affiliation: Aureon Biosciences, Yonkers, NY 10701, USA. michael.donovan@aureon.com
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Donovan
authors.forename: Faisal M
authors.initials: FM
authors.lastname: Khan
authors.forename: Douglas
authors.initials: D
authors.lastname: Powell
authors.forename: Valentina
authors.initials: V
authors.lastname: Bayer-Zubek
authors.forename: Carlos
authors.initials: C
authors.lastname: Cordon-Cardo
authors.forename: Jose
authors.initials: J
authors.lastname: Costa
authors.forename: James
authors.initials: J
authors.lastname: Eastham
authors.forename: Peter
authors.initials: P
authors.lastname: Scardino
mesh_terms: D003131:Combined Modality Therapy; D018450:Disease Progression; D018572:Disease-Free Survival; D006801:Humans; D015994:Incidence; D008297:Male; D009364:Neoplasm Recurrence, Local / Q000453:epidemiology / Q000517:prevention & control; D009367:Neoplasm Staging; D049451:Nomograms; D011184:Postoperative Period; D011237:Predictive Value of Tests; D011379:Prognosis; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000628:therapy; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D015996:Survival Rate / Q000639:trends; D014481:United States / Q000453:epidemiology