title: Predicting clinical outcomes after radical nephroureterectomy for upper tract urothelial carcinoma.
abstract: BACKGROUND
  Novel prognostic factors for patients after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC) have recently been described.
  
  
  OBJECTIVE
  We tested the prognostic value of pathologic characteristics and developed models to predict the individual probabilities of recurrence-free survival (RFS) and cancer-specific survival (CSS) after RNU.
  
  
  METHODS
  Our study included 2244 patients treated with RNU without neoadjuvant or adjuvant therapy at 23 international institutions. Tumor characteristics included T classification, grade, lymph node status, lymphovascular invasion, tumor architecture, location, and concomitant carcinoma in situ (CIS). The cohort was randomly split for development (12 centers, n=1273) and external validation (11 centers, n=971).
  
  
  METHODS
  All patients underwent RNU.
  
  
  METHODS
  Univariable and multivariable models addressed RFS, CSS, and comparison of discrimination and calibration with American Joint Committee on Cancer (AJCC) stage grouping.
  
  
  CONCLUSIONS
  At a median follow-up of 45 mo, 501 patients (22.3%) experienced disease recurrence and 418 patients (18.6%) died of UTUC. On multivariable analysis, T classification (p for trend <0.001), lymph node metastasis (hazard ratio [HR]: 1.98; p=0.002), lymphovascular invasion (HR: 1.66; p<0.001), sessile tumor architecture (HR: 1.76; p<0.001), and concomitant CIS (HR: 1.33; p=0.035) were associated with disease recurrence. Similarly, T classification (p for trend<0.001), lymph node metastasis (HR: 2.23; p=0.001), lymphovascular invasion (HR: 1.81; p<0.001), and sessile tumor architecture (HR: 1.72; p=0.001) were independently associated with cancer-specific mortality. Our models achieved 76.8% and 81.5% accuracy for predicting RFS and CSS, respectively. In contrast to these well-calibrated models, stratification based upon AJCC stage grouping resulted in a large degree of heterogeneity and did not improve discrimination.
  
  
  CONCLUSIONS
  Using standard pathologic features, we developed highly accurate prognostic models for the prediction of RFS and CSS after RNU for UTUC. These models offer improvements in calibration over AJCC stage grouping and can be used for individualized patient counseling, follow-up scheduling, risk stratification for adjuvant therapies, and inclusion criteria for clinical trials.
authors.affiliation: Department of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY 10065, USA.
authors.forename: Eugene K
authors.initials: EK
authors.lastname: Cha
authors.forename: Shahrokh F
authors.initials: SF
authors.lastname: Shariat
authors.forename: Matthias
authors.initials: M
authors.lastname: Kormaksson
authors.forename: Giacomo
authors.initials: G
authors.lastname: Novara
authors.forename: Thomas F
authors.initials: TF
authors.lastname: Chromecki
authors.forename: Douglas S
authors.initials: DS
authors.lastname: Scherr
authors.forename: Yair
authors.initials: Y
authors.lastname: Lotan
authors.forename: Jay D
authors.initials: JD
authors.lastname: Raman
authors.forename: Wassim
authors.initials: W
authors.lastname: Kassouf
authors.forename: Richard
authors.initials: R
authors.lastname: Zigeuner
authors.forename: Mesut
authors.initials: M
authors.lastname: Remzi
authors.forename: Karim
authors.initials: K
authors.lastname: Bensalah
authors.forename: Alon
authors.initials: A
authors.lastname: Weizer
authors.forename: Eiji
authors.initials: E
authors.lastname: Kikuchi
authors.forename: Christian
authors.initials: C
authors.lastname: Bolenz
authors.forename: Marco
authors.initials: M
authors.lastname: Roscigno
authors.forename: Theresa M
authors.initials: TM
authors.lastname: Koppie
authors.forename: Casey K
authors.initials: CK
authors.lastname: Ng
authors.forename: Hans-Martin
authors.initials: HM
authors.lastname: Fritsche
authors.forename: Kazumasa
authors.initials: K
authors.lastname: Matsumoto
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Walton
authors.forename: Behfar
authors.initials: B
authors.lastname: Ehdaie
authors.forename: Stefan
authors.initials: S
authors.lastname: Tritschler
authors.forename: Harun
authors.initials: H
authors.lastname: Fajkovic
authors.forename: Juan I
authors.initials: JI
authors.lastname: Martínez-Salamanca
authors.forename: Armin
authors.initials: A
authors.lastname: Pycha
authors.forename: Cord
authors.initials: C
authors.lastname: Langner
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Ficarra
authors.forename: Jean-Jacques
authors.initials: JJ
authors.lastname: Patard
authors.forename: Francesco
authors.initials: F
authors.lastname: Montorsi
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Wood
authors.forename: Pierre I
authors.initials: PI
authors.lastname: Karakiewicz
authors.forename: Vitaly
authors.initials: V
authors.lastname: Margulis
mesh_terms: D000368:Aged; D002277:Carcinoma / Q000401:mortality / Q000556:secondary / Q000601:surgery*; D018572:Disease-Free Survival; D005060:Europe; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009364:Neoplasm Recurrence, Local; D009392:Nephrectomy; D009656:North America; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D014513:Ureter / Q000473:pathology / Q000601:surgery*; D014516:Ureteral Neoplasms / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D014571:Urologic Neoplasms / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D013520:Urologic Surgical Procedures; D019459:Urothelium / Q000473:pathology / Q000601:surgery