Renata Zelic1, Hans Garmo2, Daniela Zugna3, Pär Stattin4, Lorenzo Richiardi3, Olof Akre5, Andreas Pettersson6. 1. Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden. Electronic address: renata.zelic@ki.se. 2. Division of Cancer Studies, Translational Oncology & Urology Research (TOUR), King's College London, London, UK; Akademiska Sjukhuset, Regional Cancer Centre, Uppsala, Sweden. 3. Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, and CPO-Piemonte, Turin, Italy. 4. Department of Surgical Sciences, Uppsala University, Uppsala, Sweden. 5. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Department of Urology, Karolinska University Hospital, Stockholm, Sweden. 6. Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
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.
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.
Authors: Richard J Rebello; Christoph Oing; Karen E Knudsen; Stacy Loeb; David C Johnson; Robert E Reiter; Silke Gillessen; Theodorus Van der Kwast; Robert G Bristow Journal: Nat Rev Dis Primers Date: 2021-02-04 Impact factor: 52.329
Authors: Gaëtan Devos; Wout Devlies; Gert De Meerleer; Marcella Baldewijns; Thomas Gevaert; Lisa Moris; Daimantas Milonas; Hendrik Van Poppel; Charlien Berghen; Wouter Everaerts; Frank Claessens; Steven Joniau Journal: Nat Rev Urol Date: 2021-09-15 Impact factor: 14.432
Authors: Seo Hee Choi; Young Seok Kim; Jesang Yu; Taek-Keun Nam; Jae-Sung Kim; Bum-Sup Jang; Jin Ho Kim; Youngkyong Kim; Bae Kwon Jung; Ah Ram Chang; Young-Hee Park; Sung Uk Lee; Kwan Ho Cho; Jin Hee Kim; Hunjung Kim; Youngmin Choi; Yeon Joo Kim; Dong Soo Lee; Young Ju Shin; Su Jung Shim; Won Park; Jaeho Cho Journal: Cancers (Basel) Date: 2021-05-31 Impact factor: 6.639
Authors: Amar U Kishan; R Jeffrey Karnes; Tahmineh Romero; Jessica K Wong; Giovanni Motterle; Jeffrey J Tosoian; Bruce J Trock; Eric A Klein; Bradley J Stish; Robert T Dess; Daniel E Spratt; Avinash Pilar; Chandana Reddy; Rebecca Levin-Epstein; Trude B Wedde; Wolfgang A Lilleby; Ryan Fiano; Gregory S Merrick; Richard G Stock; D Jeffrey Demanes; Brian J Moran; Michelle Braccioforte; Hartwig Huland; Phuoc T Tran; Santiago Martin; Rafael Martínez-Monge; Daniel J Krauss; Eyad I Abu-Isa; Ridwan Alam; Zeyad Schwen; Albert J Chang; Thomas M Pisansky; Richard Choo; Daniel Y Song; Stephen Greco; Curtiland Deville; Todd McNutt; Theodore L DeWeese; Ashley E Ross; Jay P Ciezki; Paul C Boutros; Nicholas G Nickols; Prashant Bhat; David Shabsovich; Jesus E Juarez; Natalie Chong; Patrick A Kupelian; Anthony V D'Amico; Matthew B Rettig; Alejandro Berlin; Jonathan D Tward; Brian J Davis; Robert E Reiter; Michael L Steinberg; David Elashoff; Eric M Horwitz; Rahul D Tendulkar; Derya Tilki Journal: JAMA Netw Open Date: 2021-07-01
Authors: Lois Kim; Nicholas Boxall; Anne George; Keith Burling; Pete Acher; Jonathan Aning; Stuart McCracken; Toby Page; Vincent J Gnanapragasam Journal: BMC Med Date: 2020-04-17 Impact factor: 8.775
Authors: M G Parry; T E Cowling; A Sujenthiran; J Nossiter; B Berry; P Cathcart; A Aggarwal; H Payne; J van der Meulen; N W Clarke; V J Gnanapragasam Journal: BMC Med Date: 2020-05-28 Impact factor: 8.775
Authors: Lauren M Hurwitz; Ilir Agalliu; Demetrius Albanes; Kathryn Hughes Barry; Sonja I Berndt; Qiuyin Cai; Chu Chen; Iona Cheng; Jeanine M Genkinger; Graham G Giles; Jiaqi Huang; Corinne E Joshu; Tim J Key; Synnove Knutsen; Stella Koutros; Hilde Langseth; Sherly X Li; Robert J MacInnis; Sarah C Markt; Kathryn L Penney; Aurora Perez-Cornago; Thomas E Rohan; Stephanie A Smith-Warner; Meir J Stampfer; Konrad H Stopsack; Catherine M Tangen; Ruth C Travis; Stephanie J Weinstein; Wu Lang PhD; Eric J Jacobs; Lorelei A Mucci; Elizabeth A Platz; Michael B Cook Journal: J Natl Cancer Inst Date: 2021-06-01 Impact factor: 13.506