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  • Title: Indication to pelvic lymph nodes dissection for prostate cancer: the role of multiparametric magnetic resonance imaging when the risk of lymph nodes invasion according to Briganti updated nomogram is <5.
    Author: Porpiglia F, Manfredi M, Mele F, Bertolo R, Bollito E, Gned D, De Pascale A, Russo F, Passera R, Fiori C, De Luca S.
    Journal: Prostate Cancer Prostatic Dis; 2018 Apr; 21(1):85-91. PubMed ID: 29472629.
    Abstract:
    BACKGROUND: The Briganti updated nomogram (BN) is the most popular predictive model aiming to predict the presence of lymph node invasion (LNI) in patients with prostate cancer (PCa), but it lacks information obtained by preoperative imaging. The primary aim of the study was to evaluate the role of multiparametric prostate magnetic resonance imaging (mp-MRI) in the indication to perform pelvic lymph nodes dissection (PLND) or not in patients with risk of LNI according to BN below 5%. METHODS: Since March 2012 and September 2016, 310 patients who underwent a preoperative mp-MRI for staging purpose and subsequent robot-assisted extended PLND (RAEPLND) were retrospectively evaluated. Mp-MRIs were prospectively analyzed by two experienced radiologists. The imaging parameters analyzed were the presence of extracapsular extension (ECE), seminal vesicles invasion (SVI) and predominant Gleason pattern 4 (pG4). All patients underwent RAEPLND by two experienced surgeons with a standardized technique. A dedicated uropathologist performed all pathological analysis. Univariate analysis and multivariate logistic regression analysis were used in order to identify the predictors of LNI in patients with PCa. RESULTS: In the overall population, 57 (18.4%) patients had histologically proven pN1 disease. 48/250 patients (19.2%) with a risk of LNI ≥5% as calculated by the BN were staged pN1 at final histopathological analysis. 9/60 patients (15.0%) with a risk of LNI <5% as calculated by BN, who underwent RAEPLND anyway according to the findings at mp-MRI, were staged pN1 at final histopathological analysis. At multivariate logistic regression analysis, all the three mp-MRI parameters were significant independent predictors of LNI after RAEPLND. CONCLUSIONS: The role of mp-MRI seemed to be crucial in patients with a risk of LNI <5% as calculated by the BN. The presence of ECE, SVI, or pG4 at mp-MRI was found to be an independent predictor of LNI by itself.
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