title: Predicting the risk of high-grade bladder cancer using noninvasive data.
abstract: OBJECTIVE
  To examine the hypothesis that the risk of high-grade bladder cancer can be predicted using noninvasively obtained data.
  
  
  METHODS
  We retrospectively analyzed the database of 431 patients that had transurethral resection of first-time bladder tumors between June 1998 and December 2009. Pre-operative parameters evaluated were: patients' age; gender; sonographic tumor diameter, number and location of tumor inside the bladder; presence of hydronephrosis, and results of urinary cytology. Parameters that showed significance in multivariate analysis were incorporated into the nomogram.
  
  
  RESULTS
  Multivariate analysis of the data showed that patient's age, the presence of hydronephrosis, sonographic tumor diameter (risk of a high-grade tumor: 14, 29, 43.3, 55.7 and 69.4% at diameters: 0.5-1.5, 1.6-2, 2.1-2.5, 2.6-3 and >3 cm, respectively), location of tumor in the bladder (risk of high-grade tumor: 28.8, 47, 67.5 and 90.5% in the lateral walls, posterior/base, anterior and dome, respectively), and urinary cytology were all highly significant and independent predictors of high-grade tumors. A nomogram constructed using these variables scored an area of 0.853 in the ROC curve.
  
  
  CONCLUSIONS
  The risk of high-grade bladder tumor can be accurately predicted using non-invasively obtained information. This prediction can help to triage patients with newly detected bladder cancer for biopsy.
authors.affiliation: Department of Urology, Hadassah Hebrew University Hospital, Jerusalem, Israel. nkgudigar @ gmail.com
authors.forename: Nandakishore
authors.initials: N
authors.lastname: Shapur
authors.forename: Dov
authors.initials: D
authors.lastname: Pode
authors.forename: Ran
authors.initials: R
authors.lastname: Katz
authors.forename: Amos
authors.initials: A
authors.lastname: Shapiro
authors.forename: Vladimir
authors.initials: V
authors.lastname: Yutkin
authors.forename: Galina
authors.initials: G
authors.lastname: Pizov
authors.forename: Liat
authors.initials: L
authors.lastname: Appelbaum
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Zorn
authors.forename: Mordechai
authors.initials: M
authors.lastname: Duvdevani
authors.forename: Ezekiel H
authors.initials: EH
authors.lastname: Landau
authors.forename: Ofer N
authors.initials: ON
authors.lastname: Gofrit
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy; D005260:Female; D006801:Humans; D008297:Male; D008495:Medical Oncology / Q000379:methods; D008875:Middle Aged; D015999:Multivariate Analysis; D012372:ROC Curve; D012189:Retrospective Studies; D012306:Risk; D012680:Sensitivity and Specificity; D016896:Treatment Outcome; D014463:Ultrasonography / Q000379:methods; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery*; D014572:Urology / Q000379:methods