title: Age, Gender and R.E.N.A.L. Nephrometry Score do not Improve the Accuracy of a Risk Stratification Algorithm Based on Biopsy and Mass Size for Assigning Surveillance versus Treatment of Renal Tumors.
abstract: OBJECTIVE
  A previously published risk stratification algorithm based on renal mass biopsy and radiographic mass size was useful to designate surveillance vs the need for immediate treatment of small renal masses. Nonetheless, there were some incorrect assignments, most notably when renal mass biopsy indicated low risk malignancy but final pathology revealed high risk malignancy. We studied other factors that might improve the accuracy of this algorithm.
  
  
  METHODS
  For 202 clinically localized small renal masses in a total of 200 patients with available R.E.N.A.L. (radius, exophytic/endophytic, nearness of tumor to collecting system or sinus, anterior/posterior, hilar tumor touching main renal artery or vein and location relative to polar lines) nephrometry score, preoperative renal mass biopsy and final pathology we assessed the accuracy of management assignment (surveillance vs treatment) based on the previously published risk stratification algorithm as confirmed by final pathology. Logistic regression was used to determine whether other factors (age, gender, R.E.N.A.L. score, R.E.N.A.L. score components and nomograms based on R.E.N.A.L. score) could improve assignment.
  
  
  RESULTS
  Of the 202 small renal masses 53 (26%) were assigned to surveillance and 149 (74%) were assigned to treatment by the risk stratification algorithm. Of the 53 lesions assigned to surveillance 25 (47%) had benign/favorable renal mass biopsy histology while in 28 (53%) intermediate renal mass biopsy histology showed a mass size less than 2 cm. Nine of these 53 masses (17%) were incorrectly assigned to surveillance in that final pathology indicated the need for treatment (ie intermediate histology and a mass greater than 2 cm or unfavorable histology). Final pathology confirmed a correct assignment in all 149 masses assigned to treatment. None of the additional parameters assessed improved assignment with statistical significance.
  
  
  CONCLUSIONS
  Age, gender, R.E.N.A.L. nephrometry score, R.E.N.A.L. score components and nomograms or combinations of these factors do not improve the predictive performance of a small renal mass management risk stratification algorithm based on renal mass biopsy and radiographic mass size.
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Takahiro
authors.initials: T
authors.lastname: Osawa
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Khaled S
authors.initials: KS
authors.lastname: Hafez
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: David C
authors.initials: DC
authors.lastname: Miller
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Montgomery
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Todd M
authors.initials: TM
authors.lastname: Morgan
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Ganesh S
authors.initials: GS
authors.lastname: Palapattu
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Alon Z
authors.initials: AZ
authors.lastname: Weizer
authors.affiliation: Department of Radiology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Caoili
authors.affiliation: Department of Radiology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: James H
authors.initials: JH
authors.lastname: Ellis
authors.affiliation: Department of Pathology, University of Michigan Health System, Ann Arbor, Michigan.
authors.forename: Lakshmi P
authors.initials: LP
authors.lastname: Kunju
authors.affiliation: Department of Urology, University of Michigan Health System, Ann Arbor, Michigan. Electronic address: wolfs@umich.edu.
authors.forename: J Stuart
authors.initials: JS
authors.lastname: Wolf
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001706:Biopsy; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000453:epidemiology / Q000473:pathology* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012737:Sex Factors; D047368:Tumor Burden; D057832:Watchful Waiting
keywords: algorithms; biopsy; kidney neoplasms; prognosis; risk