• bladder cancer;
  • lymph node density;
  • lymph node dissection;
  • prognosis;
  • radical cystectomy;
  • urothelial carcinoma

Objective:  To identify lymph node density thresholds and their prognostic role in patients who underwent radical cystectomy and pelvic lymph node dissection, and to validate findings in an external series.

Methods:  Between May 2001 and September 2009, data from 750 radical cystectomies carried out at “Regina Elena” National Cancer Institute (Rome, Italy) were collected in a prospectively-maintained database. Once patients who had undergone neoadjuvant treatments and those who had undergone salvage radical cystectomy were excluded from the 210 pN+ patients, 156 patients with urothelial carcinoma were selected for analysis. Optimal cut-off points for age, lymph node count and lymph node density were identified by considering these variables as continuous. External validation of findings was carried out by using data of 154 pN+ patients selected from two prospective series.

Results:  The optimal identified cut-off points were 11% and 30% for lymph node density, nine and 30 nodes for lymph node count, and 73 years for age. Median cancer-specific survival of patients were significantly different in patients with lymph node density <12%, between 12% and 30%, and >30% (71 months, 24 months and 11 months, respectively; P < 0.001). Cancer-specific survival was independently predicted by lymph node density cut-off points (12–30% vs <12%: hazard ratio 1.51, P = 0.047; >30% vs <12%: hazard ratio 2.89, P < 0.001). In the external series, the prognostic effect of lymph node density according to tertiary distribution of risk based on these lymph node density cut-off points was confirmed at Cox multivariable analysis (12–30% vs <12%: hazard ratio 1.5, P = 0.048; >30% vs <12%: hazard ratio 2.5, P = 0.004).

Conclusions:  Lymph node density is the strongest predictor of cancer-specific survival. Identified lymph node density thresholds have shown to be independent predictors of cancer-specific survival in the external validation series.