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        Lymph node yield in node-negative patients predicts cancer specific survival following radical cystectomy for transitional cell carcinoma

        Jack Crozier,Nathan Papa,Marlon Perera,Michael Stewart,Jeremy Goad,Shomik Sengupta,Damien Bolton,Nathan Lawrentschuk 대한비뇨의학회 2017 Investigative and Clinical Urology Vol.58 No.6

        Purpose: To determine the oncological implications of increased nodal dissection in node-negative bladder cancer during radical cystectomy in a contemporary Australian series. Materials and Methods: We performed a multicenter retrospective study, including more than 40 surgeons across 5 sites over a 10-year period. We identified 353 patients with primary bladder cancer undergoing radical cystectomy. Extent of lymphadenectomy was defined as follows; limited pelvic lymph node dissection (PLND) (perivesical, pelvic, and obturator), standard PLND (internal and external iliac) and extended PLND (common iliac). Multivariable cox proportional hazards and logistic regression models were used to determine LNY effect on cancer-specific survival. Results: Over the study period, the extent of dissection and lymph node yield increased considerably. In node-negative patients, lymph node yield (LNY) conferred a significantly improved cancer-specific survival. Compared to cases where LNY of 1 to 5 nodes were taken, the hazard ratio (HR) for 6 to 15 nodes harvested was 0.78 (95% confidence interval [CI], 0.43–1.39) and for greater than 15 nodes the HR was 0.31 (95% CI, 0.17–0.57), adjusted for age, sex, T stage, margin status, and year of surgery. The predicted probability of cancer-specific death within 2 years of cystectomy was 16% (95% CI, 13%–19%) with 10 nodes harvested, falling to 5.5% (95% CI, 0%–12%) with 30 nodes taken. Increasing harvest in all PLND templates conferred a survival benefit. Conclusions: The findings of the current study highlight the improved oncological outcomes with increased LNY, irrespective of the dissection template. Further prospective research is needed to aid LND data interpretation.

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