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  • 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
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.

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PAR ID:
10454620
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Cancer Medicine
Volume:
9
Issue:
24
ISSN:
2045-7634
Format(s):
Medium: X Size: p. 9611-9619
Size(s):
p. 9611-9619
Sponsoring Org:
National Science Foundation
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