Systematic Review of Cost-Effectiveness Models in Prostate Cancer: Exploring New Developments in Testing and Diagnosis.

<bold>Objectives: </bold>Recent innovations in prostate cancer diagnosis include new biomarkers and more accurate biopsy methods. This study assesses the evidence base on cost-effectiveness of these developments (eg, Prostate Health Index and magnetic resonance imaging [MRI]-guided biopsy) and identifies areas of improvement for future cost-effectiveness models.<bold>Methods: </bold>A systematic review using the National Health Service Economic Evaluation Database, MEDLINE, Embase, Health Technology Assessment databases, National Institute for Health and Care Excellence guidelines, and United Kingdom National Screening Committee guidance was performed, between 2009 and 2021. Relevant data were extracted on study type, model inputs, modeling methods and cost-effectiveness conclusions, and results narratively synthesized.<bold>Results: </bold>A total of 22 model-based economic evaluations were included. A total of 11 compared the cost-effectiveness of new biomarkers to prostate-specific antigen testing alone and all found biomarkers to be cost saving. A total of 8 compared MRI-guided biopsy methods to transrectal ultrasound-guided methods and found MRI-guided methods to be most cost-effective. Newer detection methods showed a reduction in unnecessary biopsies and overtreatment. The most cost-effective follow-up strategy in men with a negative initial biopsy was uncertain. Many studies did not model for stage or grade of cancer, cancer progression, or the entire testing and treatment pathway. Few fully accounted for uncertainty.<bold>Conclusions: </bold>This review brings together the cost-effectiveness literature for novel diagnostic methods in prostate cancer, showing that most studies have found new methods to be more cost-effective than standard of care. Several limitations of the models were identified, however, limiting the reliability of the results. Areas for further development include accurately modeling the impact of early diagnostic tests on long-term outcomes of prostate cancer and fully accounting for uncertainty. [ABSTRACT FROM AUTHOR]