title: Multi-institutional validation of a new renal cancer-specific survival nomogram.
abstract: OBJECTIVE
  We tested the hypothesis that the prediction of renal cancer-specific survival can be improved if traditional predictor variables are used within a prognostic nomogram.
  
  
  METHODS
  Two cohorts of patients treated with either radical or partial nephrectomy for renal cortical tumors were used: one (n = 2,530) for nomogram development and for internal validation (200 bootstrap resamples), and a second (n = 1,422) for external validation. Cox proportional hazards regression analyses modeled the 2002 TNM stages, tumor size, Fuhrman grade, histologic subtype, local symptoms, age, and sex. The accuracy of the nomogram was compared with an established staging scheme.
  
  
  RESULTS
  Cancer-specific mortality was observed in 598 (23.6%) patients, whereas 200 (7.9%) died as a result of other causes. Follow-up ranged from 0.1 to 286 months (median, 38.8 months). External validation of the nomogram at 1, 2, 5, and 10 years after nephrectomy revealed predictive accuracy of 87.8%, 89.2%, 86.7%, and 88.8%, respectively. Conversely, the alternative staging scheme predicting at 2 and 5 years was less accurate, as evidenced by 86.1% (P = .006) and 83.9% (P = .02) estimates.
  
  
  CONCLUSIONS
  The new nomogram is more contemporary, provides predictions that reach further in time and, compared with its alternative, which predicts at 2 and 5 years, generates 3.1% and 2.8% more accurate predictions, respectively.
authors.affiliation: Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Quebec, Canada. pierre.karakiewicz@umontreal.ca
authors.forename: Pierre I
authors.initials: PI
authors.lastname: Karakiewicz
authors.forename: Alberto
authors.initials: A
authors.lastname: Briganti
authors.forename: Felix K-H
authors.initials: FK
authors.lastname: Chun
authors.forename: Quoc-Dien
authors.initials: QD
authors.lastname: Trinh
authors.forename: Paul
authors.initials: P
authors.lastname: Perrotte
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Ficarra
authors.forename: Luca
authors.initials: L
authors.lastname: Cindolo
authors.forename: Alexandre
authors.initials: A
authors.lastname: De la Taille
authors.forename: Jacques
authors.initials: J
authors.lastname: Tostain
authors.forename: Peter F A
authors.initials: PF
authors.lastname: Mulders
authors.forename: Laurent
authors.initials: L
authors.lastname: Salomon
authors.forename: Richard
authors.initials: R
authors.lastname: Zigeuner
authors.forename: Tommaso
authors.initials: T
authors.lastname: Prayer-Galetti
authors.forename: Denis
authors.initials: D
authors.lastname: Chautard
authors.forename: Antoine
authors.initials: A
authors.lastname: Valeri
authors.forename: Eric
authors.initials: E
authors.lastname: Lechevallier
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Descotes
authors.forename: Herve
authors.initials: H
authors.lastname: Lang
authors.forename: Arnaud
authors.initials: A
authors.lastname: Mejean
authors.forename: Jean-Jacques
authors.initials: JJ
authors.lastname: Patard
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000401:mortality* / Q000473:pathology / Q000601:surgery; D008297:Male; D008875:Middle Aged; D009392:Nephrectomy; D049451:Nomograms; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D016019:Survival Analysis