Using prognosis to guide inclusion criteria, define standardised endpoints and stratify follow-up in active surveillance for prostate cancer.

Autor: Gnanapragasam VJ; Academic Urology Group, Department of Surgery, University of Cambridge, Cambridge, UK.; Department of Urology, Cambridge University Hospitals NHS Trust, Cambridge, UK.; Cambridge Urology Translational Research and Clinical Trials Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK., Barrett T; Department of Radiology, University of Cambridge, Cambridge, UK., Thankapannair V; Department of Urology, Cambridge University Hospitals NHS Trust, Cambridge, UK., Thurtle D; Academic Urology Group, Department of Surgery, University of Cambridge, Cambridge, UK., Rubio-Briones J; Fundacion Insituto Valenciano de Oncologica, Valencia, Spain., Domínguez-Escrig J; Fundacion Insituto Valenciano de Oncologica, Valencia, Spain., Bratt O; Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden., Statin P; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden., Muir K; Department of Public Health and Epidemiology, University of Manchester, Manchester, UK., Lophatananon A; Department of Public Health and Epidemiology, University of Manchester, Manchester, UK.
Jazyk: angličtina
Zdroj: BJU international [BJU Int] 2019 Nov; Vol. 124 (5), pp. 758-767. Date of Electronic Publication: 2019 Jun 02.
DOI: 10.1111/bju.14800
Abstrakt: Objectives: To test whether using disease prognosis can inform a rational approach to active surveillance (AS) for early prostate cancer.
Patients and Methods: We previously developed the Cambridge Prognostics Groups (CPG) classification, a five-tiered model that uses prostate-specific antigen (PSA), Grade Group and Stage to predict cancer survival outcomes. We applied the CPG model to a UK and a Swedish prostate cancer cohort to test differences in prostate cancer mortality (PCM) in men managed conservatively or by upfront treatment in CPG2 and 3 (which subdivides the intermediate-risk classification) vs CPG1 (low-risk). We then applied the CPG model to a contemporary UK AS cohort, which was optimally characterised at baseline for disease burden, to identify predictors of true prognostic progression. Results were re-tested in an external AS cohort from Spain.
Results: In a UK cohort (n = 3659) the 10-year PCM was 2.3% in CPG1, 1.5%/3.5% in treated/untreated CPG2, and 1.9%/8.6% in treated/untreated CPG3. In the Swedish cohort (n = 27 942) the10-year PCM was 1.0% in CPG1, 2.2%/2.7% in treated/untreated CPG2, and 6.1%/12.5% in treated/untreated CPG3. We then tested using progression to CPG3 as a hard endpoint in a modern AS cohort (n = 133). During follow-up (median 3.5 years) only 6% (eight of 133) progressed to CPG3. Predictors of progression were a PSA density ≥0.15 ng/mL/mL and CPG2 at diagnosis. Progression occurred in 1%, 8% and 21% of men with neither factor, only one, or both, respectively. In an independent Spanish AS cohort (n = 143) the corresponding rates were 3%, 10% and 14%, respectively.
Conclusion: Using disease prognosis allows a rational approach to inclusion criteria, discontinuation triggers and risk-stratified management in AS.
(© 2019 The Authors. BJU International © 2019 BJU International Published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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