title: A classification tree for the prediction of benign versus malignant disease in patients with small renal masses.
abstract: BACKGROUND
  To develop a classification tree for the preoperative prediction of benign versus malignant disease in patients with small renal masses.
  
  
  METHODS
  This is a retrospective study including 395 consecutive patients who underwent surgical treatment for a renal mass < 5 cm in maximum diameter between July 1st 2001 and June 30th 2010. A classification tree to predict the risk of having a benign renal mass preoperatively was developed using recursive partitioning analysis for repeated measures outcomes. Age, sex, volume on preoperative imaging, tumor location (central/peripheral), degree of endophytic component (1%-100%), and tumor axis position were used as potential predictors to develop the model.
  
  
  RESULTS
  Forty-five patients (11.4%) were found to have a benign mass postoperatively. A classification tree has been developed which can predict the risk of benign disease with an accuracy of 88.9% (95% CI: 85.3 to 91.8). The significant prognostic factors in the classification tree are tumor volume, degree of endophytic component and symptoms at diagnosis. As an example of its utilization, a renal mass with a volume of < 5.67 cm3 that is < 45% endophytic has a 52.6% chance of having benign pathology. Conversely, a renal mass with a volume ≥ 5.67 cm3 that is ≥ 35% endophytic has only a 5.3% possibility of being benign.
  
  
  CONCLUSIONS
  A classification tree to predict the risk of benign disease in small renal masses has been developed to aid the clinician when deciding on treatment strategies for small renal masses.
authors.affiliation: Dalhousie University and Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada.
authors.forename: Ricardo A
authors.initials: RA
authors.lastname: Rendon
authors.forename: Ross J
authors.initials: RJ
authors.lastname: Mason
authors.forename: Susan
authors.initials: S
authors.lastname: Kirkland
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Lawen
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdolell
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002965:Classification / Q000379:methods*; D005260:Female; D006801:Humans; D007674:Kidney Diseases / Q000145:classification* / Q000453:epidemiology*; D007680:Kidney Neoplasms / Q000145:classification* / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors; D047368:Tumor Burden