title: Prediction of patient-specific risk and percentile cohort risk of pathological stage outcome using continuous prostate-specific antigen measurement, clinical stage and biopsy Gleason score.
abstract: OBJECTIVE
  • To develop a '2010 Partin Nomogram' with total prostate-specific antigen (tPSA) as a continuous biomarker, in light of the fact that the current 2007 Partin Tables restrict the application of tPSA as a non-continuous biomarker by creating 'groups' for risk stratification with tPSA levels (ng/mL) of 0-2.5, 2.6-4.0, 4.1-6.0, 6.1-10.0 and >10.0. • To use a 'predictiveness curve' to calculate the percentile risk of a patient among the cohort.
  
  
  METHODS
  • In all, 5730 and 1646 patients were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE), respectively. • Multinomial logistic regression analysis was performed to create a model for predicting the risk of the four non-ordered pathological stages, i.e. organ-confined disease (OC), extraprostatic extension (EPE), and seminal vesicle (SV+) and lymph node (LN+) involvement. • Patient-specific risk was modelled as a function of the B-spline basis of tPSA (with knots at the first, second and third quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5-6, 3 + 4 = 7, 4 + 3 = 7, 8-10).
  
  
  RESULTS
  • The '2010 Partin Nomogram' calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's preoperative clinical stage, tPSA and biopsy Gleason score. • While having similar performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients' pathological stage than the 2007 Partin Tables model. • The use of 'predictiveness curves' has also made it possible to obtain the percentile risk of a patient among the cohort and to gauge the impact of risk thresholds for making decisions regarding radical prostatectomy.
  
  
  CONCLUSIONS
  • The '2010 Partin Nomogram' using tPSA as a continuous biomarker together with the corresponding 'predictiveness curve' will help clinicians and patients to make improved treatment decisions.
authors.affiliation: Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Huang
authors.forename: Sumit
authors.initials: S
authors.lastname: Isharwal
authors.forename: Alexander
authors.initials: A
authors.lastname: Haese
authors.forename: Felix K H
authors.initials: FK
authors.lastname: Chun
authors.forename: Danil V
authors.initials: DV
authors.lastname: Makarov
authors.forename: Ziding
authors.initials: Z
authors.lastname: Feng
authors.forename: Misop
authors.initials: M
authors.lastname: Han
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Humphreys
authors.forename: Jonathan I
authors.initials: JI
authors.lastname: Epstein
authors.forename: Alan W
authors.initials: AW
authors.lastname: Partin
authors.forename: Robert W
authors.initials: RW
authors.lastname: Veltri
mesh_terms: D001706:Biopsy; D004812:Epidemiologic Methods; D006801:Humans; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging / Q000379:methods; D049451:Nomograms; D017430:Prostate-Specific Antigen / Q000097:blood*; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000097:blood* / Q000473:pathology* / Q000601:surgery
chemical_list: D017430:Prostate-Specific Antigen