Extent of pelvic lymph node dissection and the impact of standard template dissection on nomogram prediction of lymph node involvement.

Abstract

Our current lymph node involvement (LNI) nomogram was created using patients receiving both limited and standard lymph node dissection (LND). Over time, refinements in technique could affect the diagnostic yield from LND. Our aim was to validate our existing LNI nomogram or develop a new nomogram with updated prediction coefficients that reflect the current standard LND template during radical prostatectomy (RP). We hypothesized that the existing nomogram would demonstrate good discrimination but poor calibration in a contemporary series of standard LND. A retrospective analysis of 4176 consecutive primary RP patients was performed, including open procedures (3097 patients from 2000 to 2008) and laparoscopic procedures (1079 patients from 2005 to 2008). After excluding 127 patients (3%) with limited LND, 10 (0.2%) with pretreatment prostate-specific antigen (PSA) >50 ng/ml, and 318 (8%) with incomplete data, the final cohort totaled 3721 patients. The nomograms were evaluated using receiver operating characteristic analysis, calibration plots, and decision-curve analysis. Patients received open or laparoscopic (conventional and robot-assisted) RP and standard LND in our center. Assessments were obtained using preoperative PSA, biopsy Gleason score, and clinical stage. The median number of nodes removed was 11, with ∼60% of patients having at least 10 nodes removed (n=2224). Overall, 5.2% of patients (n=194) had positive lymph nodes. The new nomogram had very high discriminative accuracy (area under the curve: 0.862). The decision-curve analysis showed that the new nomogram had the highest clinical net benefit for all reasonable threshold probabilities. The new nomogram shows improved calibration when predicting lymph node invasion in a contemporary cohort of patients with prostate cancer exclusively treated with RP and standard LND. This nomogram will be used as the preferred predictive model for counseling patients and developing studies at our institution.