Literature DB >> 33159431

Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies.

Weiyu Li1, Brian T Denton1,2, Daan Nieboer3, Peter R Carroll4, Monique J Roobol5, Todd M Morgan2.   

Abstract

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohorts. For the four AS cohorts, the estimated annual progression rate was between 6%-13%. The estimated probability of a biopsy successfully sampling undiagnosed non-favorable risk cancer (biopsy sensitivity) was between 71% and 80%. In the simulation study of patients diagnosed with favorable risk cancer at age 50, the mean number of biopsies performed before age 75 was between 4.11 and 12.60, depending on the biopsy strategy. The mean delay time to detection of non-favorable risk cancer was between 0.38 and 2.17 years. Biopsy undersampling and progression varied considerably across study cohorts. There was no single best biopsy protocol that is optimal for all cohorts, because of the variation in biopsy under-sampling error and annual progression rates across cohorts. All strategies demonstrated diminishing benefits from additional biopsies.
© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Entities:  

Keywords:  active surveillance; biopsy; biopsy under-sampling; cancer progression; hidden Markov model; prostate cancer

Mesh:

Substances:

Year:  2020        PMID: 33159431      PMCID: PMC7774732          DOI: 10.1002/cam4.3549

Source DB:  PubMed          Journal:  Cancer Med        ISSN: 2045-7634            Impact factor:   4.452


  10 in total

1.  Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts.

Authors:  Lurdes Y T Inoue; Daniel W Lin; Lisa F Newcomb; Amy S Leonardson; Donna Ankerst; Roman Gulati; H Ballentine Carter; Bruce J Trock; Peter R Carroll; Matthew R Cooperberg; Janet E Cowan; Laurence H Klotz; Alexandre Mamedov; David F Penson; Ruth Etzioni
Journal:  Ann Intern Med       Date:  2017-11-28       Impact factor: 25.391

2.  Incidence of initial local therapy among men with lower-risk prostate cancer in the United States.

Authors:  David C Miller; Stephen B Gruber; Brent K Hollenbeck; James E Montie; John T Wei
Journal:  J Natl Cancer Inst       Date:  2006-08-16       Impact factor: 13.506

3.  Optimizing active surveillance strategies to balance the competing goals of early detection of grade progression and minimizing harm from biopsies.

Authors:  Christine L Barnett; Gregory B Auffenberg; Zian Cheng; Fan Yang; Jiachen Wang; John T Wei; David C Miller; James E Montie; Mufaddal Mamawala; Brian T Denton
Journal:  Cancer       Date:  2017-11-13       Impact factor: 6.860

4.  Early prostate cancer--which treatment do men prefer and why?

Authors:  Carmel N Anandadas; Noel W Clarke; Susan E Davidson; Patrick H O'Reilly; John P Logue; Lynne Gilmore; Ric Swindell; Richard J Brough; Guy D Wemyss-Holden; Maurice W Lau; Pradip M Javle; Vijay A C Ramani; James P Wylie; Gerald N Collins; Stephen Brown; Richard A Cowan
Journal:  BJU Int       Date:  2010-11-17       Impact factor: 5.588

5.  Active surveillance program for prostate cancer: an update of the Johns Hopkins experience.

Authors:  Jeffrey J Tosoian; Bruce J Trock; Patricia Landis; Zhaoyong Feng; Jonathan I Epstein; Alan W Partin; Patrick C Walsh; H Ballentine Carter
Journal:  J Clin Oncol       Date:  2011-04-04       Impact factor: 44.544

6.  Active surveillance for low-risk prostate cancer worldwide: the PRIAS study.

Authors:  Meelan Bul; Xiaoye Zhu; Riccardo Valdagni; Tom Pickles; Yoshiyuki Kakehi; Antti Rannikko; Anders Bjartell; Deric K van der Schoot; Erik B Cornel; Giario N Conti; Egbert R Boevé; Frédéric Staerman; Jenneke J Vis-Maters; Henk Vergunst; Joris J Jaspars; Petra Strölin; Erik van Muilekom; Fritz H Schröder; Chris H Bangma; Monique J Roobol
Journal:  Eur Urol       Date:  2012-11-12       Impact factor: 20.096

7.  Active surveillance for the management of prostate cancer in a contemporary cohort.

Authors:  Marc A Dall'Era; Badrinath R Konety; Janet E Cowan; Katsuto Shinohara; Frank Stauf; Matthew R Cooperberg; Maxwell V Meng; Christopher J Kane; Nanette Perez; Viraj A Master; Peter R Carroll
Journal:  Cancer       Date:  2008-06-15       Impact factor: 6.860

8.  A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer.

Authors:  Rebecca Yates Coley; Aaron J Fisher; Mufaddal Mamawala; Herbert Ballentine Carter; Kenneth J Pienta; Scott L Zeger
Journal:  Biometrics       Date:  2016-08-22       Impact factor: 2.571

9.  The Movember Foundation's GAP3 cohort: a profile of the largest global prostate cancer active surveillance database to date.

Authors:  Sophie M Bruinsma; Liying Zhang; Monique J Roobol; Chris H Bangma; Ewout W Steyerberg; Daan Nieboer; Mieke Van Hemelrijck
Journal:  BJU Int       Date:  2018-01-18       Impact factor: 5.588

10.  Clinical results of long-term follow-up of a large, active surveillance cohort with localized prostate cancer.

Authors:  Laurence Klotz; Liying Zhang; Adam Lam; Robert Nam; Alexandre Mamedov; Andrew Loblaw
Journal:  J Clin Oncol       Date:  2009-11-16       Impact factor: 44.544

  10 in total
  1 in total

Review 1.  Active surveillance for prostate cancer: selection criteria, guidelines, and outcomes.

Authors:  Colton H Walker; Kathryn A Marchetti; Udit Singhal; Todd M Morgan
Journal:  World J Urol       Date:  2021-03-02       Impact factor: 4.226

  1 in total

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