Timo F W Soeterik1, Harm H E van Melick2, Lea M Dijksman3, Heidi Küsters-Vandevelde4, Saskia Stomps5, Ivo G Schoots6, Douwe H Biesma7, J A Witjes8, Jean-Paul A van Basten9. 1. Department of Value-Based Healthcare, Santeon Group, Utrecht, The Netherlands; Department of Urology, St. Antonius Hospital, Nieuwegein/Utrecht, Netherlands. Electronic address: t.soeterik@antoniusziekenhuis.nl. 2. Department of Urology, St. Antonius Hospital, Nieuwegein/Utrecht, Netherlands. 3. Department of Value-Based Healthcare, St. Antonius Hospital, Nieuwegein/Utrecht, The Netherlands. 4. Department of Pathology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands. 5. Department of Urology, Hospital Group Twente, Hengelo/Almelo, The Netherlands. 6. Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands. 7. Department of Value-Based Healthcare, Santeon Group, Utrecht, The Netherlands. 8. Department of Urology, Radboud University Medical centre, Nijmegen, The Netherlands. 9. Department of Urology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands.
Abstract
BACKGROUND: Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). OBJECTIVE: To develop and externally validate nomograms including multiparametric magnetic resonance imaging (mpMRI) information to predict side-specific EPE. DESIGN, SETTING, AND PARTICIPANTS: A retrospective analysis of 1870 consecutive prostate cancer patients who underwent robot-assisted RP from 2014 to 2018 at three institutions. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Four multivariable logistic regression models were established, including combinations of patient-based and side-specific variables: prostate-specific antigen (PSA) density, highest ipsilateral International Society of Urological Pathology (ISUP) biopsy grade, ipsilateral percentage of positive cores on systematic biopsy, and side-specific clinical stage assessed by both digital rectal examination and mpMRI. Discrimination (area under the curve [AUC]), calibration, and net benefit of these models were assessed in the development cohort and two external validation cohorts. RESULTS AND LIMITATIONS: On external validation, AUCs of the four models ranged from 0.80 (95% confidence interval [CI] 0.68-0.88) to 0.83 (95% CI 0.72-0.90) in cohort 1 and from 0.77 (95% CI 0.62-0.87) to 0.78 (95% CI 0.64-0.88) in cohort 2. The three models including mpMRI staging information resulted in relatively higher AUCs compared with the model without mpMRI information. No major differences between the four models regarding net benefit were established. The model based on PSA density, ISUP grade, and mpMRI T stage was superior in terms of calibration. Using this model with a cut-off of 20%, 1980/2908 (68%) prostatic lobes without EPE would be found eligible for nerve sparing, whereas non-nerve sparing would be advised in 642/832 (77%) lobes with EPE. CONCLUSIONS: Our analysis resulted in a simple and robust nomogram for the prediction of side-specific EPE, which should be used to select patients for nerve-sparing RP. PATIENT SUMMARY: We developed a prediction model that can be used to assess accurately the likelihood of tumour extension outside the prostate. This tool can guide patient selection for safe nerve-sparing surgery.
BACKGROUND: Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). OBJECTIVE: To develop and externally validate nomograms including multiparametric magnetic resonance imaging (mpMRI) information to predict side-specific EPE. DESIGN, SETTING, AND PARTICIPANTS: A retrospective analysis of 1870 consecutive prostate cancer patients who underwent robot-assisted RP from 2014 to 2018 at three institutions. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Four multivariable logistic regression models were established, including combinations of patient-based and side-specific variables: prostate-specific antigen (PSA) density, highest ipsilateral International Society of Urological Pathology (ISUP) biopsy grade, ipsilateral percentage of positive cores on systematic biopsy, and side-specific clinical stage assessed by both digital rectal examination and mpMRI. Discrimination (area under the curve [AUC]), calibration, and net benefit of these models were assessed in the development cohort and two external validation cohorts. RESULTS AND LIMITATIONS: On external validation, AUCs of the four models ranged from 0.80 (95% confidence interval [CI] 0.68-0.88) to 0.83 (95% CI 0.72-0.90) in cohort 1 and from 0.77 (95% CI 0.62-0.87) to 0.78 (95% CI 0.64-0.88) in cohort 2. The three models including mpMRI staging information resulted in relatively higher AUCs compared with the model without mpMRI information. No major differences between the four models regarding net benefit were established. The model based on PSA density, ISUP grade, and mpMRI T stage was superior in terms of calibration. Using this model with a cut-off of 20%, 1980/2908 (68%) prostatic lobes without EPE would be found eligible for nerve sparing, whereas non-nerve sparing would be advised in 642/832 (77%) lobes with EPE. CONCLUSIONS: Our analysis resulted in a simple and robust nomogram for the prediction of side-specific EPE, which should be used to select patients for nerve-sparing RP. PATIENT SUMMARY: We developed a prediction model that can be used to assess accurately the likelihood of tumour extension outside the prostate. This tool can guide patient selection for safe nerve-sparing surgery.
Authors: Andreas G Wibmer; Ines Nikolovski; Joshua Chaim; Yulia Lakhman; Robert A Lefkowitz; Evis Sala; Sigrid V Carlsson; Samson W Fine; Michael W Kattan; Hedvig Hricak; Hebert Alberto Vargas Journal: Radiology Date: 2021-12-21 Impact factor: 11.105
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