BACKGROUND: Nowadays a tool able to predict the risk of lymph-node invasion (LNI) in patients underwent target biopsy (TB) only before radical prostatectomy (RP) is still lacking. Our aim is to develop a model based on mp-MRI and target biopsy (TB) alone able to predict the risk of LNI. METHODS: We retrospectively extracted data of patients with preoperative positive mp-MRI and TB only who underwent RARP with ePLND from April 2014 to March 2020. A logistic regression model was performed to evaluate the impact of pre- and intra-operative factors on the risk of LNI. Model discrimination was assessed using an area under (AUC) the ROC curve. A nomogram, and its calibration plot, to predict the risk of LNI were generated based on the logistic model. A validation of the model was done using a similar cohort. RESULTS: 461 patients were included, of which 52 (11.27) had LNI. After logistic regression analysis and multivariable model DRE, PI-RADS, seminal vesicle invasion, PSA and worst GS at I and II target lesions were significant predictors of LNI. The AUC was 0.74 [0.67-0.81] 95% CI. The calibration plot shows that our model is very close to the ideal one which is in the 95% CI. After the creation of a visual nomogram, the cut-off to discriminate between the risk or not of LNI was set with Youden index at 60 points that correspond to a risk of LNI of 7%. The model applied on a similar cohort shown a LH+ of 2.58 [2.17-2.98] 95% CI. CONCLUSIONS: Our nomogram for patients undergoing MRI-TB only takes into account clinical stage, SVI at MRI, biopsy Gleason pattern and PSA and it is able to identify patients with risk of LNI when a score higher than 7% is achieved.
BACKGROUND: Nowadays a tool able to predict the risk of lymph-node invasion (LNI) in patients underwent target biopsy (TB) only before radical prostatectomy (RP) is still lacking. Our aim is to develop a model based on mp-MRI and target biopsy (TB) alone able to predict the risk of LNI. METHODS: We retrospectively extracted data of patients with preoperative positive mp-MRI and TB only who underwent RARP with ePLND from April 2014 to March 2020. A logistic regression model was performed to evaluate the impact of pre- and intra-operative factors on the risk of LNI. Model discrimination was assessed using an area under (AUC) the ROC curve. A nomogram, and its calibration plot, to predict the risk of LNI were generated based on the logistic model. A validation of the model was done using a similar cohort. RESULTS: 461 patients were included, of which 52 (11.27) had LNI. After logistic regression analysis and multivariable model DRE, PI-RADS, seminal vesicle invasion, PSA and worst GS at I and II target lesions were significant predictors of LNI. The AUC was 0.74 [0.67-0.81] 95% CI. The calibration plot shows that our model is very close to the ideal one which is in the 95% CI. After the creation of a visual nomogram, the cut-off to discriminate between the risk or not of LNI was set with Youden index at 60 points that correspond to a risk of LNI of 7%. The model applied on a similar cohort shown a LH+ of 2.58 [2.17-2.98] 95% CI. CONCLUSIONS: Our nomogram for patients undergoing MRI-TB only takes into account clinical stage, SVI at MRI, biopsy Gleason pattern and PSA and it is able to identify patients with risk of LNI when a score higher than 7% is achieved.
Authors: Stavros I Tyritzis; Nikolaos Kalampokis; Nikolaos Grivas; Henk van der Poel; N Peter Wiklund Journal: Minerva Chir Date: 2018-07-23 Impact factor: 1.000
Authors: Marco Bandini; Michele Marchioni; Raisa S Pompe; Zhe Tian; Giorgio Gandaglia; Nicola Fossati; Firas Abdollah; Markus Graefen; Francesco Montorsi; Fred Saad; Shahrokh F Shariat; Alberto Briganti; Pierre I Karakiewicz Journal: BJU Int Date: 2017-11-29 Impact factor: 5.588
Authors: Nicola Fossati; Peter-Paul M Willemse; Thomas Van den Broeck; Roderick C N van den Bergh; Cathy Yuhong Yuan; Erik Briers; Joaquim Bellmunt; Michel Bolla; Philip Cornford; Maria De Santis; Ekelechi MacPepple; Ann M Henry; Malcolm D Mason; Vsevolod B Matveev; Henk G van der Poel; Theo H van der Kwast; Olivier Rouvière; Ivo G Schoots; Thomas Wiegel; Thomas B Lam; Nicolas Mottet; Steven Joniau Journal: Eur Urol Date: 2017-01-24 Impact factor: 20.096
Authors: Nicolas Mottet; Roderick C N van den Bergh; Erik Briers; Thomas Van den Broeck; Marcus G Cumberbatch; Maria De Santis; Stefano Fanti; Nicola Fossati; Giorgio Gandaglia; Silke Gillessen; Nikos Grivas; Jeremy Grummet; Ann M Henry; Theodorus H van der Kwast; Thomas B Lam; Michael Lardas; Matthew Liew; Malcolm D Mason; Lisa Moris; Daniela E Oprea-Lager; Henk G van der Poel; Olivier Rouvière; Ivo G Schoots; Derya Tilki; Thomas Wiegel; Peter-Paul M Willemse; Philip Cornford Journal: Eur Urol Date: 2020-11-07 Impact factor: 20.096
Authors: Marco Sebben; Alessandro Tafuri; Marco Pirozzi; Tania Processali; Riccardo Rizzetto; Nelia Amigoni; Aliasger Shakir; Mario De Michele; Andrea Panunzio; Clara Cerrato; Leone Tiso; Giovanni Novella; Matteo Brunelli; Filippo Migliorini; Vincenzo De Marco; Salvatore Siracusano; Walter Artibani; Antonio B Porcaro Journal: Minerva Urol Nefrol Date: 2019-12-12 Impact factor: 3.720