Journal ArticleDOI
Prospective Multi-Institutional Study Evaluating the Performance of Prostate Cancer Risk Calculators
Robert K. Nam,Michael W. Kattan,Joseph L. Chin,John Trachtenberg,Rajiv K. Singal,Ricardo A. Rendon,Laurence Klotz,Linda Sugar,Christopher Sherman,Jonathan I. Izawa,David R. Bell,Aleksandra Stanimirovic,Vasundara Venkateswaran,Eleftherios P. Diamandis,Changhong Yu,D. Andrew Loblaw,Steven A. Narod +16 more
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The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30%.Abstract:
Purpose Prostate cancer risk calculators incorporate many factors to evaluate an individual’s risk for prostate cancer We validated two common North American–based, prostate cancer risk calculators Patients and Methods We conducted a prospective, multi-institutional study of 2,130 patients who underwent a prostate biopsy for prostate cancer detection from five centers We evaluated the performance of the Sunnybrook nomogram– based prostate cancer risk calculator (SRC) and the Prostate Cancer Prevention Trial (PCPT) – based risk calculator (PRC) to predict the presence of any cancer and high-grade cancer We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models Results Of the 2,130 patients, 867 men (407%) were found to have cancer, and 1,263 (593%) did not have cancer Of the patients with cancer, 403 (465%) had a Gleason score of 7 or more The area under the [concentration-time] curve (AUC) for the SRC was 067 (95% CI, 065 to 069); the AUC for the PRC was 061 (95% CI, 059 to 064) The AUC was higher for predicting aggressive disease from the SRC (072; 95% CI, 070 to 075) compared with that from the PRC (067; 95% CI, 064 to 070) Decision curve analyses showed that the SRC performed better than the PRC for risk thresholds of more than 30% for any cancer and more than 15% for aggressive cancer Conclusion The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30% Further research is needed to improve the AUCs of the risk calculators, particularly for higher-grade cancer J Clin Oncol 29:2959-2964 © 2011 by American Society of Clinical Oncologyread more
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Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.
Karel G.M. Moons,Douglas G. Altman,Johannes B. Reitsma,John P. A. Ioannidis,Petra Macaskill,Ewout W. Steyerberg,Andrew J. Vickers,David F. Ransohoff,Gary S. Collins +8 more
TL;DR: In virtually all medical domains, diagnostic and prognostic multivariable prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making.
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Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests.
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References
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Journal ArticleDOI
Screening and Prostate-Cancer Mortality in a Randomized European Study
Fritz H. Schröder,Jonas Hugosson,Monique J. Roobol,Stefano Ciatto,Vera Nelen,Maciej Kwiatkowski,Marcos Lujan,Hans Lilja,Marco Zappa,Louis Denis,Franz Recker,A. Berenguer,Liisa Määttänen,Chris H. Bangma,Gunnar Aus,Arnauld Villers,Xavier Rebillard,Theodorus van der Kwast,Bert G. Blijenberg,Sue Moss,Harry J. de Koning,Anssi Auvinen +21 more
TL;DR: PSA-based screening reduced the rate of death from prostate cancer by 20% but was associated with a high risk of overdiagnosis.
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Assessing the performance of prediction models: a framework for traditional and novel measures.
Ewout W. Steyerberg,Andrew J. Vickers,Nancy R. Cook,Thomas A. Gerds,Mithat Gonen,Nancy A Obuchowski,Michael J. Pencina,Michael W. Kattan +7 more
TL;DR: It is suggested that reporting discrimination and calibration will always be important for a prediction model and decision-analytic measures should be reported if the predictive model is to be used for clinical decisions.
Journal ArticleDOI
Decision curve analysis: a novel method for evaluating prediction models.
Andrew J. Vickers,Elena B. Elkin +1 more
TL;DR: Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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The American Urological Association symptom index for benign prostatic hyperplasia. The Measurement Committee of the American Urological Association.
Michael J. Barry,Floyd J. Fowler,Michael P. O'Leary,Reginald C. Bruskewitz,Holtgrewe Hl,Winston K. Mebust,Abraham T.K. Cockett +6 more
TL;DR: The AUA symptom index for benign prostatic hyperplasia was developed and validated by a multidisciplinary measurement committee of the American Urological Association and is clinically sensible, reliable, valid and responsive.
Journal ArticleDOI
Mortality Results from a Randomized Prostate-Cancer Screening Trial
Gerald L. Andriole,E. David Crawford,Robert L. Grubb,Saundra S. Buys,David Chia,Timothy R. Church,Mona N. Fouad,Edward P. Gelmann,Paul A. Kvale,Douglas J. Reding,Joel L. Weissfeld,Lance A. Yokochi,Jonathan D. Clapp,Joshua M. Rathmell,Thomas L. Riley,Richard B. Hayes,Barnett S. Kramer,Anthony B. Miller,Paul F. Pinsky,Philip C. Prorok,John K. Gohagan,Christine D. Berg +21 more
TL;DR: After 7 to 10 years of follow-up, the rate of death from prostate cancer was very low and did not differ significantly between the two study groups.
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