OBJECTIVES: To develop a nomogram for predicting side-specific extracapsular extension (ECE) for planning nerve-sparing radical prostatectomy. MATERIALS AND METHODS: We retrospectively analysed data from 561 patients who underwent robot-assisted radical prostatectomy between February 2014 and October 2015. To develop a side-specific predictive model, we considered the prostatic lobes separately. Four variables were included: prostate-specific antigen; highest ipsilateral biopsy Gleason grade; highest ipsilateral percentage core involvement; and ECE on multiparametric magnetic resonance imaging (mpMRI). A multivariable logistic regression analysis was fitted to predict side-specific ECE. A nomogram was built based on the coefficients of the logit function. Internal validation was performed using 'leave-one-out' cross-validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit. RESULTS: The study population consisted of 829 side-specific cases, after excluding negative biopsy observations (n = 293). ECE was reported on mpMRI and final pathology in 115 (14%) and 142 (17.1%) cases, respectively. Among these, mpMRI was able to predict ECE correctly in 57 (40.1%) cases. All variables in the model except highest percentage core involvement were predictors of ECE (all P ≤ 0.006). All variables were considered for inclusion in the nomogram. After internal validation, the area under the curve was 82.11%. The model demonstrated excellent calibration and improved clinical risk prediction, especially when compared with relying on mpMRI prediction of ECE alone. When retrospectively applying the nomogram-derived probability, using a 20% threshold for performing nerve-sparing, nine out of 14 positive surgical margins (PSMs) at the site of ECE resulted above the threshold. CONCLUSION: We developed an easy-to-use model for the prediction of side-specific ECE, and hope it serves as a tool for planning nerve-sparing radical prostatectomy and in the reduction of PSM in future series.
OBJECTIVES: To develop a nomogram for predicting side-specific extracapsular extension (ECE) for planning nerve-sparing radical prostatectomy. MATERIALS AND METHODS: We retrospectively analysed data from 561 patients who underwent robot-assisted radical prostatectomy between February 2014 and October 2015. To develop a side-specific predictive model, we considered the prostatic lobes separately. Four variables were included: prostate-specific antigen; highest ipsilateral biopsy Gleason grade; highest ipsilateral percentage core involvement; and ECE on multiparametric magnetic resonance imaging (mpMRI). A multivariable logistic regression analysis was fitted to predict side-specific ECE. A nomogram was built based on the coefficients of the logit function. Internal validation was performed using 'leave-one-out' cross-validation. Calibration was graphically investigated. The decision curve analysis was used to evaluate the net clinical benefit. RESULTS: The study population consisted of 829 side-specific cases, after excluding negative biopsy observations (n = 293). ECE was reported on mpMRI and final pathology in 115 (14%) and 142 (17.1%) cases, respectively. Among these, mpMRI was able to predict ECE correctly in 57 (40.1%) cases. All variables in the model except highest percentage core involvement were predictors of ECE (all P ≤ 0.006). All variables were considered for inclusion in the nomogram. After internal validation, the area under the curve was 82.11%. The model demonstrated excellent calibration and improved clinical risk prediction, especially when compared with relying on mpMRI prediction of ECE alone. When retrospectively applying the nomogram-derived probability, using a 20% threshold for performing nerve-sparing, nine out of 14 positive surgical margins (PSMs) at the site of ECE resulted above the threshold. CONCLUSION: We developed an easy-to-use model for the prediction of side-specific ECE, and hope it serves as a tool for planning nerve-sparing radical prostatectomy and in the reduction of PSM in future series.
Authors: Jethro C C Kwong; Adree Khondker; Christopher Tran; Emily Evans; Adrian I Cozma; Ashkan Javidan; Amna Ali; Munir Jamal; Thomas Short; Frank Papanikolaou; John R Srigley; Benjamin Fine; Andrew Feifer Journal: Can Urol Assoc J Date: 2022-06 Impact factor: 2.052
Authors: Francesco Montorsi; Alberto Martini; Giorgio Gandaglia; Nicola Fossati; Armando Stabile; Federico Dehò; Andrea Salonia; Alberto Briganti Journal: World J Urol Date: 2021-03-17 Impact factor: 4.226
Authors: Naomi Morka; Benjamin S Simpson; Rhys Ball; Alex Freeman; Alex Kirkham; Daniel Kelly; Hayley C Whitaker; Mark Emberton; Joseph M Norris Journal: BMJ Open Date: 2021-05-05 Impact factor: 2.692
Authors: Andreas G Wibmer; Michael W Kattan; Francesco Alessandrino; Alexander D J Baur; Lars Boesen; Felipe Boschini Franco; David Bonekamp; Riccardo Campa; Hannes Cash; Violeta Catalá; Sebastien Crouzet; Sounil Dinnoo; James Eastham; Fiona M Fennessy; Kamyar Ghabili; Markus Hohenfellner; Angelique W Levi; Xinge Ji; Vibeke Løgager; Daniel J Margolis; Paul C Moldovan; Valeria Panebianco; Tobias Penzkofer; Philippe Puech; Jan Philipp Radtke; Olivier Rouvière; Heinz-Peter Schlemmer; Preston C Sprenkle; Clare M Tempany; Joan C Vilanova; Jeffrey Weinreb; Hedvig Hricak; Amita Shukla-Dave Journal: Cancers (Basel) Date: 2021-05-27 Impact factor: 6.639