Two Positive Nodes Represent a Significant Cut-off Value for Cancer SpecificSurvival in Patients with Node Positive Prostate Cancer. A New Proposal Based on a Two-Institution Experience on 703 Consecutive N+ Patients Treated with Radical Prostatectomy, Extended Pelvic Lymph Node Dissection and Adjuvant Therapy

Background: Currently, the 2002 American Joint Committee on Cancer (AJCC) staging system of prostate cancer does not include any stratification of patients according to the number of positive nodes. However, node positive (N+) patients share heterogeneous outcomes according to the extent of lymph node invasion (LNI). Objective: To test whether the accuracy of cancer specific survival (CSS) predictions may be improved if node positive patients are stratified according to the number of positive nodes. Design, Setting, and Participants: The study cohort included 703 N+ M0 patients treated with radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND) between September 1988 and January 2003 at two large Academic Institutions. Number of positive nodes was dichotomized according to the most informative cut-off predicting CSS. Kaplan-Meier curves assessed cancer specific survival rates. Predictive accuracy of the current N stage and of the new N classification in predicting CSS was quantified with Harrell's concordance index after adjusting for pathological (T) stage and internally validated with 200 boostraps resamples. Differences in predictive accuracy were compared with the Mantel-Haentzel test. Results and Limitations: Mean follow-up was 113.7 months (median: 112.5, range 3.5-243). The mean number of nodes removed was 13.9 (range: 2-52). The mean number of positive nodes was 2.3 (range: 1-31). The most informative cut-off of positive nodes in predicting CSS was 2. Of all, 532 (75.7%) patients had 2 or less positive nodes, while 171 (24.3%) had more than 2 positive nodes. Patients with 2 or less positive nodes had significantly better CSS outcome at 15 year follow-up compared to patients with more than 2 positive nodes (84% vs 62%; p < 0.001). After adjusting for pathological stage, multivariable predictive accuracy of the new N staging (≤2 or >2 positive nodes) was 65.0% vs 60.1% when the number of positive nodes was not considered (4.9% gain; p < 0.001). Conclusions: We demonstrated that patients with up to 2 positive nodes experienced excellent CSS, which was significantly higher compared to patients with more than 2 positive nodes. Moreover, a significant improvement in CSS prediction was reached when the number of positive nodes was considered. Thus, our results reinforce the need for a stratification of node positive patients according to the number of positive nodes and may warrant consideration in the next revision of the pathologic TNM classification.