title: Parenchymal Volumetric Assessment as a Predictive Tool to Determine Renal Function Benefit of Nephron-Sparing Surgery Compared with Radical Nephrectomy.
abstract: OBJECTIVE
  To develop a preoperative prediction model using a computer-assisted volumetric assessment of potential spared parenchyma to estimate the probability of chronic kidney disease (CKD, estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m(2)) 6 months from extirpative renal surgery (nephron-sparing surgery [NSS] or radical nephrectomy [RN]).
  
  
  METHODS
  Retrospective analysis of patients who underwent NSS or RN at our institution from January 2000 to June 2013 with a compatible CT scan 6-month renal function follow-up was performed. Primary outcome was defined as the accuracy of 6-month postoperative eGFR compared with actual postoperative eGFR based on root mean square error (RMSE). Models were constructed using renal volumes and externally validated. A clinical tool was developed on the best model after a given surgical procedure using area under the curve (AUC).
  
  
  RESULTS
  We identified 130 (51 radical, 79 partial) patients with a median age of 58 years (interquartile range [IQR] 48-67) and preoperative eGFR of 82.1 (IQR 65.9-104.3); postoperative CKD (eGFR <60) developed in 42% (55/130). We performed various linear regression models to predict postoperative eGFR. The Quadratic model was the highest performing model, which relied only on preoperative GFR and the volumetric data for a RMSE of 15.3 on external validation corresponding to a clinical tool with an AUC of 0.89.
  
  
  CONCLUSIONS
  Volumetric-based assessment provides information to predict postoperative eGFR. A tool based on this equation may assist surgical counseling regarding renal functional outcomes before renal tumor surgical procedures.
authors.affiliation: 1 Department of Urology, UC San Diego Health , La Jolla, California.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Liss
authors.affiliation: 2 Department of Bioengineering, UC San Diego Health , La Jolla, California.
authors.forename: Robert
authors.initials: R
authors.lastname: DeConde
authors.affiliation: 3 Department of Radiology, UC San Diego Health , La Jolla, California.
authors.forename: Dominique
authors.initials: D
authors.lastname: Caovan
authors.affiliation: 4 Department of Mathematics, Technical University Munich , Garching, Germany .
authors.forename: Joseph
authors.initials: J
authors.lastname: Hofler
authors.affiliation: 3 Department of Radiology, UC San Diego Health , La Jolla, California.
authors.forename: Michael
authors.initials: M
authors.lastname: Gabe
authors.affiliation: 1 Department of Urology, UC San Diego Health , La Jolla, California.
authors.forename: Kerrin L
authors.initials: KL
authors.lastname: Palazzi
authors.affiliation: 1 Department of Urology, UC San Diego Health , La Jolla, California.
authors.forename: Nishant D
authors.initials: ND
authors.lastname: Patel
authors.affiliation: 1 Department of Urology, UC San Diego Health , La Jolla, California.
authors.forename: Hak J
authors.initials: HJ
authors.lastname: Lee
authors.affiliation: 2 Department of Bioengineering, UC San Diego Health , La Jolla, California.
authors.forename: Trey
authors.initials: T
authors.lastname: Ideker
authors.affiliation: 5 Department of Urology, University Hospitals Leuven , Leuven, Belgium.
authors.forename: Hendrik
authors.initials: H
authors.lastname: Van Poppel
authors.affiliation: 3 Department of Radiology, UC San Diego Health , La Jolla, California.
authors.forename: David
authors.initials: D
authors.lastname: Karow
authors.affiliation: 6 Department of Radiology, University Hospitals Leuven , Leuven, Belgium .
authors.forename: Michael
authors.initials: M
authors.lastname: Aertsen
authors.affiliation: 3 Department of Radiology, UC San Diego Health , La Jolla, California.
authors.forename: Giovanna
authors.initials: G
authors.lastname: Casola
authors.affiliation: 1 Department of Urology, UC San Diego Health , La Jolla, California.
authors.forename: Ithaar H
authors.initials: IH
authors.lastname: Derweesh
mesh_terms: D000368:Aged; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery*; D003661:Decision Support Techniques; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007668:Kidney / Q000000981:diagnostic imaging* / Q000601:surgery; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000601:surgery*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D009392:Nephrectomy / Q000379:methods*; D009399:Nephrons; D009929:Organ Size; D059351:Organ Sparing Treatments / Q000379:methods*; D011183:Postoperative Complications / Q000453:epidemiology*; D011859:Radiography; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology*; D012189:Retrospective Studies; D047368:Tumor Burden