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

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 estima...

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Autores principales: Li, Weiyu, Denton, Brian T., Nieboer, Daan, Carroll, Peter R., Roobol, Monique J., Morgan, Todd M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774732/
https://www.ncbi.nlm.nih.gov/pubmed/33159431
http://dx.doi.org/10.1002/cam4.3549
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author Li, Weiyu
Denton, Brian T.
Nieboer, Daan
Carroll, Peter R.
Roobol, Monique J.
Morgan, Todd M.
author_facet Li, Weiyu
Denton, Brian T.
Nieboer, Daan
Carroll, Peter R.
Roobol, Monique J.
Morgan, Todd M.
author_sort Li, Weiyu
collection PubMed
description 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.
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spelling pubmed-77747322021-01-05 Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies Li, Weiyu Denton, Brian T. Nieboer, Daan Carroll, Peter R. Roobol, Monique J. Morgan, Todd M. Cancer Med Cancer Prevention 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. John Wiley and Sons Inc. 2020-11-06 /pmc/articles/PMC7774732/ /pubmed/33159431 http://dx.doi.org/10.1002/cam4.3549 Text en © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Li, Weiyu
Denton, Brian T.
Nieboer, Daan
Carroll, Peter R.
Roobol, Monique J.
Morgan, Todd M.
Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
title Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
title_full Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
title_fullStr Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
title_full_unstemmed Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
title_short Comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
title_sort comparison of biopsy under‐sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774732/
https://www.ncbi.nlm.nih.gov/pubmed/33159431
http://dx.doi.org/10.1002/cam4.3549
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