title: Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT).
abstract: OBJECTIVE
  The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model.
  
  
  METHODS
  Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models.
  
  
  RESULTS
  The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (p < 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (r = 0.58, p < 0.01) and %RPV preservation (r = 0.54, p < 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 - 0.55(age) - 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (r = 0.83; p < 0.001). The external validation cohort (n = 21) showed our model outperformed previously reported models.
  
  
  CONCLUSIONS
  Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan. sisotan@med.teikyo-u.ac.jp.
authors.forename: Shuji
authors.initials: S
authors.lastname: Isotani
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Hirofumi
authors.initials: H
authors.lastname: Shimoyama
authors.affiliation: Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Isao
authors.initials: I
authors.lastname: Yokota
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Yasuhiro
authors.initials: Y
authors.lastname: Noma
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Kousuke
authors.initials: K
authors.lastname: Kitamura
authors.affiliation: Department of Urology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
authors.forename: Toshiyuki
authors.initials: T
authors.lastname: China
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Keisuke
authors.initials: K
authors.lastname: Saito
authors.affiliation: Department of Urology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
authors.forename: Shin-ichi
authors.initials: S
authors.lastname: Hisasue
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Hisamitsu
authors.initials: H
authors.lastname: Ide
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Satoru
authors.initials: S
authors.lastname: Muto
authors.affiliation: Department of Urology, School of Medicine, Teikyo University, Kaga 2-11-1, Itabashi-ku, Tokyo, Japan.
authors.forename: Raizo
authors.initials: R
authors.lastname: Yamaguchi
authors.affiliation: Institute of Urology, University of Southern California, Los Angeles, CA, USA.
authors.forename: Osamu
authors.initials: O
authors.lastname: Ukimura
authors.affiliation: Institute of Urology, University of Southern California, Los Angeles, CA, USA.
authors.forename: Inderbir S
authors.initials: IS
authors.lastname: Gill
authors.affiliation: Department of Urology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
authors.forename: Shigeo
authors.initials: S
authors.lastname: Horie
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D015331:Cohort Studies; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000473:pathology*; D007677:Kidney Function Tests; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography / Q000379:methods*; D009392:Nephrectomy; D009929:Organ Size; D011184:Postoperative Period; D011237:Predictive Value of Tests
keywords: Chronic kidney disease; Multidetector computed tomography; Nephrectomy; Prediction model; Renal cortex; Renal function; Volumetry