A review of economic evaluations of diagnostic strategies using imaging in men at risk of prostate cancer. Issue 6 (November 2015)

• Record Type: Journal Article
• Authors: Willis, Sarah R.; van der Meulen, Jan; Valerio, Massimo; Miners, Alec; Ahmed, Hashim U.; Emberton, Mark
• Is Part Of: Current opinion in urology. Volume 25:Issue 6(2015:Nov.)
• Publication Date: 2015-11
• Subjects: Urology -- Periodicals; Review Literature -- Bibliography; Review Literature -- Periodicals; Male Urogenital Diseases -- Bibliography; Male Urogenital Diseases -- Periodicals; Female Urogenital Diseases -- Bibliography; Female Urogenital Diseases -- Periodicals; Urologic Diseases -- Bibliography; Urologic Diseases -- Periodicals; Periodicals; 616.6005
• DOI: 10.1097/MOU.0000000000000220
• Languages: English
• ISSNs: 1473-6586

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <sec> <title>Purpose of review</title> <p>The role of imaging, particularly MRI, in diagnosing clinically significant prostate cancer is the focus of a rapidly developing body of clinical research. We identified five economic evaluations of multiparametric magnetic resonance imaging (mpMRI) for diagnosing prostate cancer which report very different results. This review aims to explain why the reported cost-effectiveness of mpMRI varies so widely.</p> </sec> <sec> <title>Findings</title> <p>The studies evaluate the cost-effectiveness of mpMRI within different clinical pathways; before biopsy and after a negative biopsy. Although there were important differences in the questions posed, the studies also employed different assumptions about the impact of prostate cancer and its treatment on survival and quality of life.</p> </sec> <sec> <title>Summary</title> <p>This review highlights the need for a better standard of reporting around key modelling assumptions. Also, a wider range of sensitivity analyses should explore the impact of these structural assumptions on the model results, in addition to the more commonly acknowledged uncertainty around data inputs for the model parameters.</p> </sec> </abstract>