INTRODUCTION: Existing nomograms predicting lymph node involvement (LNI) in prostate cancer (PCa) are based on conventional lymphadenectomy. The aim of the study was to develop the first nomogram for predicting LNI in PCa patients undergoing sentinel guided pelvic lymph node dissection (sPLND). MATERIALS AND METHODS: Analysis was performed on 1,296 patients with PCa who underwent radioisotope guided sPLND and retropubic radical prostatectomy (2005-2010). Median prostate specific antigen (PSA): 7.4 ng/ml (IQR 5.3-11.5 ng/ml). Clinical T-categories: T1: 54.8%, T2: 42.4%, T3: 2.8%. Biopsy Gleason sums: ≤ 6: 55.1%, 7: 39.5%, ≥ 8: 5.4%. Multivariate logistic regression models tested the association between all of the above predictors and LNI. Regression-based coefficients were used to develop a nomogram for predicting LNI. Accuracy was quantified using the area under the curve (AUC). RESULTS: The median number of LNs removed was 10 (IQR 7-13). Overall, 17.8% of patients (n = 231) had LNI. The nomogram had a high predictive accuracy (AUC of 82%). All the variables were statistically significant multivariate predictors of LNI (p = 0.001). Univariate predictive accuracy for PSA, Gleason sum and clinical stage was 69, 75 and 69%, respectively. CONCLUSIONS: The sentinel nomogram can predict LNI at a sPLND very accurately and, for the first time, aid clinicians and patients in making important decisions on the indication of a sPLND. The high rate of LN+ patients underscores the sensitivity of sPLND.
INTRODUCTION: Existing nomograms predicting lymph node involvement (LNI) in prostate cancer (PCa) are based on conventional lymphadenectomy. The aim of the study was to develop the first nomogram for predicting LNI in PCa patients undergoing sentinel guided pelvic lymph node dissection (sPLND). MATERIALS AND METHODS: Analysis was performed on 1,296 patients with PCa who underwent radioisotope guided sPLND and retropubic radical prostatectomy (2005-2010). Median prostate specific antigen (PSA): 7.4 ng/ml (IQR 5.3-11.5 ng/ml). Clinical T-categories: T1: 54.8%, T2: 42.4%, T3: 2.8%. Biopsy Gleason sums: ≤ 6: 55.1%, 7: 39.5%, ≥ 8: 5.4%. Multivariate logistic regression models tested the association between all of the above predictors and LNI. Regression-based coefficients were used to develop a nomogram for predicting LNI. Accuracy was quantified using the area under the curve (AUC). RESULTS: The median number of LNs removed was 10 (IQR 7-13). Overall, 17.8% of patients (n = 231) had LNI. The nomogram had a high predictive accuracy (AUC of 82%). All the variables were statistically significant multivariate predictors of LNI (p = 0.001). Univariate predictive accuracy for PSA, Gleason sum and clinical stage was 69, 75 and 69%, respectively. CONCLUSIONS: The sentinel nomogram can predict LNI at a sPLND very accurately and, for the first time, aid clinicians and patients in making important decisions on the indication of a sPLND. The high rate of LN+ patients underscores the sensitivity of sPLND.
Authors: Nikolaos Grivas; Esther Wit; Corinne Tillier; Erik van Muilekom; Floris Pos; Alexander Winter; Henk van der Poel Journal: Eur J Nucl Med Mol Imaging Date: 2017-08-05 Impact factor: 9.236
Authors: Daniela A Ferraro; Urs J Muehlematter; Helena I Garcia Schüler; Niels J Rupp; Martin Huellner; Michael Messerli; Jan Hendrik Rüschoff; Edwin E G W Ter Voert; Thomas Hermanns; Irene A Burger Journal: Eur J Nucl Med Mol Imaging Date: 2019-09-14 Impact factor: 9.236
Authors: Alexander Winter; Thomas Kneib; Clara Wasylow; Lena Reinhardt; Rolf-Peter Henke; Svenja Engels; Holger Gerullis; Friedhelm Wawroschek Journal: J Cancer Date: 2017-08-22 Impact factor: 4.207
Authors: Bartosz Małkiewicz; Paweł Kiełb; Jakub Karwacki; Róża Czerwińska; Paulina Długosz; Artur Lemiński; Łukasz Nowak; Wojciech Krajewski; Tomasz Szydełko Journal: J Clin Med Date: 2022-04-22 Impact factor: 4.241