title: External validation of a nomogram using RENAL nephrometry score to predict high grade renal cell carcinoma.
abstract: OBJECTIVE
  A novel nomogram using the RENAL ([R]adius maximal diameter in cm, [E]xophytic/endophytic properties, [N]earness of the tumor to the collecting system or sinus in mm, [A]nterior/Posterior, [L]ocation relative to the polar lines and [H]ilar) nephrometry score was developed to predict high grade renal cell carcinoma. It showed good performance in internal evaluation. We externally validated the prediction model.
  
  
  METHODS
  We identified a cohort of 391 Chinese patients in whom renal cell carcinoma was surgically resected at our institution from 2008 to 2011. Fuhrman grade was reviewed by an experienced genitourinary pathologist and radiological images were independently assessed by 2 senior urologists. Using a 2-tiered system high grade disease was defined as Fuhrman grade III/IV. The statistical performance of the prediction model was evaluated by discrimination, calibration and clinical usefulness.
  
  
  RESULTS
  Of the 391 patients 45.5% were considered to have high grade tumors. External validation of the nomogram revealed an AUC of 0.73. The calibration plot showed that the predicted probability of high grade disease had concordance comparable to the observed frequency. On decision curve analysis the prediction model provided a superior net benefit and reduction at a greater than 20% probability threshold.
  
  
  CONCLUSIONS
  We confirm the predictive value of the nomogram using the RENAL nephrometry score to identify high grade renal cell carcinoma in an independent cohort. Further research is required to evaluate its performance using a head-to-head comparison with renal biopsy results.
authors.affiliation: Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
authors.forename: Hong-Kai
authors.initials: HK
authors.lastname: Wang
authors.forename: Yao
authors.initials: Y
authors.lastname: Zhu
authors.forename: Xu-Dong
authors.initials: XD
authors.lastname: Yao
authors.forename: Shi-Lin
authors.initials: SL
authors.lastname: Zhang
authors.forename: Bo
authors.initials: B
authors.lastname: Dai
authors.forename: Hai-Liang
authors.initials: HL
authors.lastname: Zhang
authors.forename: Yi-Jun
authors.initials: YJ
authors.lastname: Shen
authors.forename: Chao-Fu
authors.initials: CF
authors.lastname: Wang
authors.forename: Ding-Wei
authors.initials: DW
authors.lastname: Ye
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002292:Carcinoma, Renal Cell / Q000473:pathology* / Q000601:surgery; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000473:pathology* / Q000601:surgery; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D049451:Nomograms; D012372:ROC Curve; D055815:Young Adult