Literature DB >> 25636656

The Next Generation of Clinical Decision Making Tools: Development of a Real-Time Prediction Tool for Outcome of Prostate Biopsy in Response to a Continuously Evolving Prostate Cancer Landscape.

Andreas N Strobl1, Ian M Thompson2, Andrew J Vickers3, Donna P Ankerst4.   

Abstract

PURPOSE: We evaluate whether annual updating of the PCPT Risk Calculator would improve institutional validation compared to static use of the PCPT Risk Calculator alone.
MATERIALS AND METHODS: Data from 5 international cohorts including SABOR, Cleveland Clinic, ProtecT, Tyrol and Durham VA, comprising 18,400 biopsies, were used to evaluate an institution specific annual recalibration of the PCPT Risk Calculator. Using all prior years as a training set and the current year as the test set, annual recalibrations of the PCPT Risk Calculator were compared to static use of the PCPT Risk Calculator in terms of AUC and the Hosmer-Lemeshow goodness of fit statistic.
RESULTS: For predicting high grade disease the median AUC (higher is better) of the recalibrated PCPT Risk Calculator (static PCPT Risk Calculator) across all test years for the 5 cohorts was 67.3 (67.5), 65.0 (60.4), 73.4 (73.4), 73.9 (74.1) and 69.6 (67.2), respectively, and median Hosmer-Lemeshow goodness of fit statistics indicated better fit for recalibration compared to the static PCPT Risk Calculator for Cleveland Clinic, ProtecT and the Durham VA but not for SABOR and Tyrol. For predicting overall cancer median AUC was 63.5 (62.7), 61.0 (57.3), 62.1 (62.5), 66.9 (67.3) and 68.5 (65.5), respectively, and median Hosmer-Lemeshow goodness of fit statistics indicated a better fit for recalibration in all cohorts except for Tyrol.
CONCLUSIONS: A simple method has been provided to tailor the PCPT Risk Calculator to individual hospitals to optimize its accuracy for the patient population at hand.
Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  biopsy; digital rectal examination; prostate; prostate-specific antigen; risk assessment

Mesh:

Year:  2015        PMID: 25636656      PMCID: PMC4475467          DOI: 10.1016/j.juro.2015.01.092

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  19 in total

1.  Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial.

Authors:  Ian M Thompson; Donna Pauler Ankerst; Chen Chi; Phyllis J Goodman; Catherine M Tangen; M Scott Lucia; Ziding Feng; Howard L Parnes; Charles A Coltman
Journal:  J Natl Cancer Inst       Date:  2006-04-19       Impact factor: 13.506

2.  Interexaminer variability of digital rectal examination in detecting prostate cancer.

Authors:  D S Smith; W J Catalona
Journal:  Urology       Date:  1995-01       Impact factor: 2.649

3.  Statistical process control for validating a classification tree model for predicting mortality--a novel approach towards temporal validation.

Authors:  Lilian Minne; Saeid Eslami; Nicolette de Keizer; Evert de Jonge; Sophia E de Rooij; Ameen Abu-Hanna
Journal:  J Biomed Inform       Date:  2011-08-31       Impact factor: 6.317

4.  Prostate Cancer Prevention Trial risk calculator 2.0 for the prediction of low- vs high-grade prostate cancer.

Authors:  Donna P Ankerst; Josef Hoefler; Sebastian Bock; Phyllis J Goodman; Andrew Vickers; Javier Hernandez; Lori J Sokoll; Martin G Sanda; John T Wei; Robin J Leach; Ian M Thompson
Journal:  Urology       Date:  2014-06       Impact factor: 2.649

5.  The relationship between prostate-specific antigen and prostate cancer risk: the Prostate Biopsy Collaborative Group.

Authors:  Andrew J Vickers; Angel M Cronin; Monique J Roobol; Jonas Hugosson; J Stephen Jones; Michael W Kattan; Eric Klein; Freddie Hamdy; David Neal; Jenny Donovan; Dipen J Parekh; Donna Ankerst; George Bartsch; Helmut Klocker; Wolfgang Horninger; Amine Benchikh; Gilles Salama; Arnauld Villers; Steve J Freedland; Daniel M Moreira; Fritz H Schröder; Hans Lilja
Journal:  Clin Cancer Res       Date:  2010-08-24       Impact factor: 12.531

6.  A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension.

Authors:  Tessa S S Genders; Ewout W Steyerberg; Hatem Alkadhi; Sebastian Leschka; Lotus Desbiolles; Koen Nieman; Tjebbe W Galema; W Bob Meijboom; Nico R Mollet; Pim J de Feyter; Filippo Cademartiri; Erica Maffei; Marc Dewey; Elke Zimmermann; Michael Laule; Francesca Pugliese; Rossella Barbagallo; Valentin Sinitsyn; Jan Bogaert; Kaatje Goetschalckx; U Joseph Schoepf; Garrett W Rowe; Joanne D Schuijf; Jeroen J Bax; Fleur R de Graaf; Juhani Knuuti; Sami Kajander; Carlos A G van Mieghem; Matthijs F L Meijs; Maarten J Cramer; Deepa Gopalan; Gudrun Feuchtner; Guy Friedrich; Gabriel P Krestin; M G Myriam Hunink
Journal:  Eur Heart J       Date:  2011-03-02       Impact factor: 29.983

7.  National trends in the management of low and intermediate risk prostate cancer in the United States.

Authors:  Adam B Weiner; Sanjay G Patel; Ruth Etzioni; Scott E Eggener
Journal:  J Urol       Date:  2014-08-05       Impact factor: 7.450

8.  Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings.

Authors:  P Schuetz; M Koller; M Christ-Crain; E Steyerberg; D Stolz; C Müller; H C Bucher; R Bingisser; M Tamm; B Müller
Journal:  Epidemiol Infect       Date:  2008-02-27       Impact factor: 2.451

9.  Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart.

Authors:  Lei Chen; Andrew M Tonkin; Lynelle Moon; Paul Mitchell; Annette Dobson; Graham Giles; Michael Hobbs; Patrick J Phillips; Jonathan E Shaw; David Simmons; Leon A Simons; Anthony P Fitzgerald; Guy De Backer; Dirk De Bacquer
Journal:  Eur J Cardiovasc Prev Rehabil       Date:  2009-10

10.  Updating methods improved the performance of a clinical prediction model in new patients.

Authors:  K J M Janssen; K G M Moons; C J Kalkman; D E Grobbee; Y Vergouwe
Journal:  J Clin Epidemiol       Date:  2007-11-26       Impact factor: 6.437

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  5 in total

1.  Integration of magnetic resonance imaging into prostate cancer nomograms.

Authors:  Garrett J Brinkley; Andrew M Fang; Soroush Rais-Bahrami
Journal:  Ther Adv Urol       Date:  2022-05-13

2.  Next-generation prostate cancer risk calculator for primary care physicians.

Authors:  Robert K Nam; Raj Satkunavisam; Joseph L Chin; Jonathan Izawa; John Trachtenberg; Ricardo Rendon; David Bell; Rajiv Singal; Christopher Sherman; Linda Sugar; Kevin Chagin; Michael W Kattan
Journal:  Can Urol Assoc J       Date:  2017-12-01       Impact factor: 1.862

3.  Precision Medicine in Active Surveillance for Prostate Cancer: Development of the Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator.

Authors:  Donna P Ankerst; Jing Xia; Ian M Thompson; Josef Hoefler; Lisa F Newcomb; James D Brooks; Peter R Carroll; William J Ellis; Martin E Gleave; Raymond S Lance; Peter S Nelson; Andrew A Wagner; John T Wei; Ruth Etzioni; Daniel W Lin
Journal:  Eur Urol       Date:  2015-03-25       Impact factor: 20.096

4.  A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort.

Authors:  Amirhossein Jalali; Robert W Foley; Robert M Maweni; Keefe Murphy; Dara J Lundon; Thomas Lynch; Richard Power; Frank O'Brien; Kieran J O'Malley; David J Galvin; Garrett C Durkan; T Brendan Murphy; R William Watson
Journal:  BMC Med Inform Decis Mak       Date:  2020-07-03       Impact factor: 2.796

5.  Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools.

Authors:  Johanna Tolksdorf; Michael W Kattan; Stephen A Boorjian; Stephen J Freedland; Karim Saba; Cedric Poyet; Lourdes Guerrios; Amanda De Hoedt; Michael A Liss; Robin J Leach; Javier Hernandez; Emily Vertosick; Andrew J Vickers; Donna P Ankerst
Journal:  BMC Med Res Methodol       Date:  2019-10-15       Impact factor: 4.615

  5 in total

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