title: [Establishment of regression model predicting the probability of the capsular penetration of prostate cancer].
abstract: OBJECTIVE
  To establish the regression model predicting the probability of the capsular penetration according to the Chinese prostate cancer samples.
  
  
  METHODS
  Men enrolled in the Fudan University Shanghai Cancer Centre and undergoing radical prostatectomy between January 2006 and April 2010 were used to establish the predicting model. According to the pathology after radical prostatectomy, all cases were divided into two groups: organ confined disease group and locally advanced disease group, the difference of which were whether had the capsular penetration. The cases with regional lymph node metastasis were excluded. Serum prostate specific antigen level, Gleason grade, clinical stage were collected. Multiple Logistic regression model was established according to preoperative clinical data and postoperative pathological data to predict the incidence of capsular penetration. Receiver operating characteristic curve was used for the internal validation of the model.
  
  
  RESULTS
  83 Chinese men were identified in the organ confined disease group with the age of 66.8 ± 5.8 years, and 36 in the locally advanced disease group with the age of 66.0 ± 6.8 years. The difference of the age between the two groups were of no statistic significance (t = 0.650, P = 0.517). The serum prostate specific antigen level (Wilcoxon W = 4562.0, P = 0.016), Gleason score (Wilcoxon W = 4586.5, P = 0.016), and clinical stage (Wilcoxon W = 4444.5, P = 0.001) of the locally advanced disease group were higher than the other group. The equation of the multiple Logistic regression model was Logit P = 0.488 × Gleason score + 0.104 × clinical stage -6.187, with the freedom degree of two and the likelihood ratio χ(2) test of 11.263 (P = 0.001). The area under the ROC curve (AUC) for capsular penetration was 0.696 (P = 0.001), with the 95% confidence interval of 0.598 - 0.793.
  
  
  CONCLUSIONS
  The multiple Logistic regression model based on the Chinese population can accurately predict the probability of capsular penetration of the prostate cancer and work on well with high internal accuracy when clinical decisions are made.
authors.affiliation: Department of Urology, Fudan University Shanghai Cancer Centre, Shanghai 200032, China.
authors.forename: Wen-jun
authors.initials: WJ
authors.lastname: Xiao
authors.forename: Ding-wei
authors.initials: DW
authors.lastname: Ye
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: Zhao-fu
authors.initials: ZF
authors.lastname: Wang
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
authors.forename: Hai-liang
authors.initials: HL
authors.lastname: Zhang
authors.forename: Yi-jun
authors.initials: YJ
authors.lastname: Shen
authors.forename: Yao
authors.initials: Y
authors.lastname: Zhu
authors.forename: Yi-ping
authors.initials: YP
authors.lastname: Zhu
authors.forename: Guo-hai
authors.initials: GH
authors.lastname: Shi
authors.forename: Chun-guang
authors.initials: CG
authors.lastname: Ma
authors.forename: Xiao-jian
authors.initials: XJ
authors.lastname: Qin
authors.forename: Guo-wen
authors.initials: GW
authors.lastname: Lin
mesh_terms: D000368:Aged; D044466:Asian People; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000473:pathology*; D012189:Retrospective Studies