Tom A Hueting1, Erik B Cornel2, Diederik M Somford3, Hanneke Jansen4, Jean-Paul A van Basten3, Rick G Pleijhuis5, Ruben A Korthorst6, Job A M van der Palen7, Hendrik Koffijberg8. 1. Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands. Electronic address: tomhueting@gmail.com. 2. Department of Urology, Ziekenhuisgroep Twente, Hengelo, The Netherlands. 3. Department of urology, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands. 4. Netherlands Comprehensive Cancer Organization, Utrecht, The Netherlands. 5. Department of Internal Medicine, Medisch Spectrum Twente, Enschede, The Netherlands. 6. Department of Urology, Medisch Spectrum Twente, Enschede, The Netherlands. 7. Department of Research Methodology, Measurement and Data Analysis, University of Twente, Enschede, The Netherlands; Medisch spectrum Twente, Enschede, The Netherlands. 8. Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands.
Abstract
BACKGROUND: Multiple statistical models predicting lymph node involvement (LNI) in prostate cancer (PCa) exist to support clinical decision-making regarding extended pelvic lymph node dissection (ePLND). OBJECTIVE: To validate models predicting LNI in Dutch PCa patients. DESIGN, SETTING, AND PARTICIPANTS: Sixteen prediction models were validated using a patient cohort of 1001 men who underwent ePLND. Patient characteristics included serum prostate specific antigen (PSA), cT stage, primary and secondary Gleason scores, number of biopsy cores taken, and number of positive biopsy cores. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Calibration plots were used to visualize over- or underestimation by the models. RESULTS AND LIMITATIONS: LNI was identified in 276 patients (28%). Patients with LNI had higher PSA, higher primary Gleason pattern, higher Gleason score, higher number of nodes harvested, higher number of positive biopsy cores, and higher cT stage compared to patients without LNI. Predictions generated by the 2012 Briganti nomogram (AUC 0.76) and the Memorial Sloan Kettering Cancer Center (MSKCC) web calculator (AUC 0.75) were the most accurate. Calibration had a decisive role in selecting the most accurate models because of overlapping confidence intervals for the AUCs. Underestimation of LNI probability in patients had a predicted probability of <20%. The omission of model updating was a limitation of the study. CONCLUSIONS: Models predicting LNI in PCa patients were externally validated in a Dutch patient cohort. The 2012 Briganti and MSKCC nomograms were identified as the most accurate prediction models available. PATIENT SUMMARY: In this report we looked at how well models were able to predict the risk of prostate cancer spreading to the pelvic lymph nodes. We found that two models performed similarly in predicting the most accurate probabilities.
BACKGROUND: Multiple statistical models predicting lymph node involvement (LNI) in prostate cancer (PCa) exist to support clinical decision-making regarding extended pelvic lymph node dissection (ePLND). OBJECTIVE: To validate models predicting LNI in Dutch PCa patients. DESIGN, SETTING, AND PARTICIPANTS: Sixteen prediction models were validated using a patient cohort of 1001 men who underwent ePLND. Patient characteristics included serum prostate specific antigen (PSA), cT stage, primary and secondary Gleason scores, number of biopsy cores taken, and number of positive biopsy cores. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Calibration plots were used to visualize over- or underestimation by the models. RESULTS AND LIMITATIONS: LNI was identified in 276 patients (28%). Patients with LNI had higher PSA, higher primary Gleason pattern, higher Gleason score, higher number of nodes harvested, higher number of positive biopsy cores, and higher cT stage compared to patients without LNI. Predictions generated by the 2012 Briganti nomogram (AUC 0.76) and the Memorial Sloan Kettering Cancer Center (MSKCC) web calculator (AUC 0.75) were the most accurate. Calibration had a decisive role in selecting the most accurate models because of overlapping confidence intervals for the AUCs. Underestimation of LNI probability in patients had a predicted probability of <20%. The omission of model updating was a limitation of the study. CONCLUSIONS: Models predicting LNI in PCa patients were externally validated in a Dutch patient cohort. The 2012 Briganti and MSKCC nomograms were identified as the most accurate prediction models available. PATIENT SUMMARY: In this report we looked at how well models were able to predict the risk of prostate cancer spreading to the pelvic lymph nodes. We found that two models performed similarly in predicting the most accurate probabilities.
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: Elio Mazzone; Paolo Dell'Oglio; Nikos Grivas; Esther Wit; Maarten Donswijk; Alberto Briganti; Fijs Van Leeuwen; Henk van der Poel Journal: J Nucl Med Date: 2021-02-05 Impact factor: 10.057
Authors: Nicola Frego; Marco Paciotti; Nicolò Maria Buffi; Davide Maffei; Roberto Contieri; Pier Paolo Avolio; Vittorio Fasulo; Alessandro Uleri; Massimo Lazzeri; Rodolfo Hurle; Alberto Saita; Giorgio Ferruccio Guazzoni; Paolo Casale; Giovanni Lughezzani Journal: Front Surg Date: 2022-02-25