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  • Title: Predicting lymph node invasion in patients treated with robot-assisted radical prostatectomy.
    Author: Abdollah F, Klett DE, Sammon JD, Dalela D, Sood A, Hsu L, Diaz M, Gupta N, Peabody JO, Trinh QD, Menon M.
    Journal: Can J Urol; 2016 Feb; 23(1):8141-50. PubMed ID: 26892054.
    Abstract:
    INTRODUCTION: To develop a nomogram to predict lymph node invasion (LNI) in the contemporary North American patient treated with robot-assisted radical prostatectomy (RARP). MATERIALS AND METHODS: We included 2,007 patients treated with RARP and pelvic lymph node dissection (PLND) at a single institution between 2008 and 2012. D'Amico low risk patients underwent an obturator and hypogastric PLND, while extended PLND was reserved for intermediate/high risk patients. Logistic regression analysis tested the relationship between LNI and all available predictors. Independent predictors of LNI were used to develop a novel nomogram. Discrimination, calibration and decision-curve analysis were used to analyze the performance of our novel nomogram, and compare it to open radical prostatectomy (ORP)-based models, namely the Godoy nomogram. RESULTS: Overall, 5.3% of our patients harbored LNI. Median number of lymph nodes removed was 6.0 (interquartile range: 4-11). The most parsimonious multivariable model to predict LNI consisted of the following independent predictors: PSA value, clinical stage, and primary and secondary Gleason scores (all p ≤ 0.02). The discrimination of our novel model was 86.2%, and its calibration was virtually optimal. Using a 2% nomogram cut off, 58% of patients would be spared PLND, while missing only 9.4% of individuals with LNI. The novel nomogram compared favorably to the Godoy nomogram, when discrimination, calibration and net-benefit were used as benchmarks. CONCLUSIONS: Approximately 5% of contemporary North American patients harbor LNI at RARP. Our novel nomogram can accurately identify these patients, and this may help to improve patient selection, and avoid unnecessary PLND in the majority of patients.
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