title: A preoperative prognostic model for patients treated with nephrectomy for renal cell carcinoma.
abstract: BACKGROUND
  Currently two pretreatment prognostic models with limited accuracy (65-67%) can be used to predict survival in patients with localized renal cell carcinoma (RCC).
  
  
  OBJECTIVE
  We set out to develop a more accurate pretreatment model for predicting RCC-specific mortality after nephrectomy for all stages of RCC.
  
  
  METHODS
  The data originated from a series of prospectively recorded contemporary cases of patients treated with radical or partial nephrectomy between 1984 and 2006. Model development was performed using data from 2474 patients from five centers and external validation was performed using data from 1972 patients from seven centers.
  
  
  METHODS
  The probability of RCC-specific mortality was modeled using Cox regression. The significance of the predictors was confirmed using competing risks analyses, which account for mortality from other causes.
  
  
  CONCLUSIONS
  Median follow-up in patients who did not die of RCC-specific causes was 4.2 yr and 3.5 yr in the development and validation cohorts, respectively. The freedom from cancer-specific mortality rates in the nomogram development cohort were 75.4% at 5 yr after nephrectomy and 68.3% at 10 yr after nephrectomy. All variables except gender achieved independent predictor status. In the external validation cohort the nomogram predictions were 88.1% accurate at 1 yr, 86.8% accurate at 2 yr, 86.8% accurate at 5 yr, and 84.2% accurate at 10 yr.
  
  
  CONCLUSIONS
  Our model substantially exceeds the accuracy of the existing pretreatment models. Consequently, the proposed nomogram-based predictions may be used as benchmark data for pretreatment decision making in patients with various stages of RCC.
authors: Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada. pierre.karakiewicz@umontreal.ca
authors: Pierre I
authors: PI
authors: Karakiewicz
authors: Nazareno
authors: N
authors: Suardi
authors: Umberto
authors: U
authors: Capitanio
authors: Claudio
authors: C
authors: Jeldres
authors: Vincenzo
authors: V
authors: Ficarra
authors: Luca
authors: L
authors: Cindolo
authors: Alexandre
authors: A
authors: de la Taille
authors: Jacques
authors: J
authors: Tostain
authors: Peter F A
authors: PF
authors: Mulders
authors: Karim
authors: K
authors: Bensalah
authors: Walter
authors: W
authors: Artibani
authors: Laurent
authors: L
authors: Salomon
authors: Richard
authors: R
authors: Zigeuner
authors: Antoine
authors: A
authors: Valéri
authors: Jean-Luc
authors: JL
authors: Descotes
authors: Jean-Jacques
authors: JJ
authors: Rambeaud
authors: Arnaud
authors: A
authors: Méjean
authors: Francesco
authors: F
authors: Montorsi
authors: Roberto
authors: R
authors: Bertini
authors: Jean-Jacques
authors: JJ
authors: Patard
mesh_terms: D000704:Analysis of Variance; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D002423:Cause of Death; D015331:Cohort Studies; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D009392:Nephrectomy / Q000379:methods* / Q000401:mortality; D011336:Probability; D011379:Prognosis; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D015996:Survival Rate; D017741:Survivors; D013997:Time Factors; D014057:Tomography, X-Ray Computed