Background: Numerous pretreatment risk classification tools are available for prostate cancer. Which tool is best in predicting prostate cancer death is unclear.Objective: To systematically compare the prognostic performance of the most commonly used pretreatment risk stratification tools for prostate cancer.Design, setting, and participants: A nationwide cohort study was conducted, including 154 811 men in Prostate Cancer data Base Sweden (PCBaSe) 4.0 diagnosed with nonmetastatic prostate cancer during 1998-2016 and followed through 2016.Outcome measurements and statistical analysis: We compared the D'Amico, National Institute for Health and Care Excellence (NICE), European Association of Urology (EAU), Genito-Urinary Radiation Oncologists of Canada (GUROC), American Urological Association (AUA), National Comprehensive Cancer Network (NCCN), and Cambridge Prognostic Groups (CPG) risk group systems; the Cancer of the Prostate Risk Assessment (CAPRA) score; and the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram in predicting prostate cancer death by estimating the concordance index (C-index) and the observed versus predicted cumulative incidences at different follow-up times.Results and limitations: A total of 139 515 men were included in the main analysis, of whom 15 961 died from prostate cancer during follow-up. The C-index at 10 yr of follow-up ranged from 0.73 (95% confidence interval [CI]: 0.72-0.73) to 0.81 (95% CI: 0.80-0.81) across the compared tools. The MSKCC nomogram (C-index: 0.81, 95% CI: 0.80-0.81), CAPRA score (C-index: 0.80, 95% CI: 0.79-0.81), and CPG system (C-index: 0.78, 95% CI: 0.78-0.79) performed the best. The order of performance between the tools remained in analyses stratified by primary treatment and year of diagnosis. The predicted cumulative incidences were close to the observed ones, with some underestimation at 5 yr. It is a limitation that the study was conducted solely in a Swedish setting (ie, case mix).Conclusions: The MSKCC nomogram, CAPRA score, and CPG risk grouping system performed better in discriminating prostate cancer death than the D'Amico and D'Amico-derived systems (NICE, GUROC, EAU, AUA, and NCCN). Use of these tools may improve clinical decision making.Patient summary: There are numerous pretreatment risk classification tools that can aid treatment decision for prostate cancer. We systematically compared the prognostic performance of the most commonly used tools in a large cohort of Swedish men with prostate cancer. The Memorial Sloan Kettering Cancer Center nomogram, Cancer of the Prostate Risk Assessment score, and Cambridge Prognostic Groups performed best in predicting prostate cancer death. The use of these tools may improve treatment decisions. (C) 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Predicting Prostate Cancer Death with Different Pretreatment Risk Stratification Tools: A Head-to-head Comparison in a Nationwide Cohort Study

Zelic, Renata;Zugna, Daniela;Richiardi, Lorenzo;
2020-01-01

Abstract

Background: Numerous pretreatment risk classification tools are available for prostate cancer. Which tool is best in predicting prostate cancer death is unclear.Objective: To systematically compare the prognostic performance of the most commonly used pretreatment risk stratification tools for prostate cancer.Design, setting, and participants: A nationwide cohort study was conducted, including 154 811 men in Prostate Cancer data Base Sweden (PCBaSe) 4.0 diagnosed with nonmetastatic prostate cancer during 1998-2016 and followed through 2016.Outcome measurements and statistical analysis: We compared the D'Amico, National Institute for Health and Care Excellence (NICE), European Association of Urology (EAU), Genito-Urinary Radiation Oncologists of Canada (GUROC), American Urological Association (AUA), National Comprehensive Cancer Network (NCCN), and Cambridge Prognostic Groups (CPG) risk group systems; the Cancer of the Prostate Risk Assessment (CAPRA) score; and the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram in predicting prostate cancer death by estimating the concordance index (C-index) and the observed versus predicted cumulative incidences at different follow-up times.Results and limitations: A total of 139 515 men were included in the main analysis, of whom 15 961 died from prostate cancer during follow-up. The C-index at 10 yr of follow-up ranged from 0.73 (95% confidence interval [CI]: 0.72-0.73) to 0.81 (95% CI: 0.80-0.81) across the compared tools. The MSKCC nomogram (C-index: 0.81, 95% CI: 0.80-0.81), CAPRA score (C-index: 0.80, 95% CI: 0.79-0.81), and CPG system (C-index: 0.78, 95% CI: 0.78-0.79) performed the best. The order of performance between the tools remained in analyses stratified by primary treatment and year of diagnosis. The predicted cumulative incidences were close to the observed ones, with some underestimation at 5 yr. It is a limitation that the study was conducted solely in a Swedish setting (ie, case mix).Conclusions: The MSKCC nomogram, CAPRA score, and CPG risk grouping system performed better in discriminating prostate cancer death than the D'Amico and D'Amico-derived systems (NICE, GUROC, EAU, AUA, and NCCN). Use of these tools may improve clinical decision making.Patient summary: There are numerous pretreatment risk classification tools that can aid treatment decision for prostate cancer. We systematically compared the prognostic performance of the most commonly used tools in a large cohort of Swedish men with prostate cancer. The Memorial Sloan Kettering Cancer Center nomogram, Cancer of the Prostate Risk Assessment score, and Cambridge Prognostic Groups performed best in predicting prostate cancer death. The use of these tools may improve treatment decisions. (C) 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.
2020
77
2
180
188
Pretreatment risk stratification; Prognostic model; Prostate cancer
Zelic, Renata; Garmo, Hans; Zugna, Daniela; Stattin, Pär; Richiardi, Lorenzo; Akre, Olof; Pettersson, Andreas
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1885273
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