Evidence map›Paper›PMID 41918916›Full record

ReviewTherapeutic advances in urology

Cost-effectiveness of PSMA-PET imaging technology in diagnosing and staging of prostate cancer: a systematic review.

Chiranjeev Sanyal, Ricardo Rendon

Abstract readReview
In one paragraph

Review in Therapeutic advances in urology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Chiranjeev SanyalCollege of Pharmacy, Faculty of Health, Dalhousie University, 5968 College St, Halifax, NS B3H 4R2, Canada.ORCID https://orcid.org/0000-0003-3677-8036
Ricardo RendonDepartment of Urology, Dalhousie University, Halifax, NS, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Prostate-specific membrane antigen (PSMA) PET is becoming the preferred imaging technique for prostate cancer (PCa) because of its superior sensitivity and specificity compared to traditional imaging methods. This article evaluates the methodological and reporting quality of literature regarding the cost-effectiveness of PSMA-positron emission tomography (PET)/computed tomography (CT) or MRI for detecting and staging PCa. Objective: To describe the methodological and reporting quality of the published cost-effectiveness studies on PSMA-PET. Methods: MEDLINE and EMBASE were searched from inception to December 05, 2025. Two researchers independently screened all retrieved articles according to inclusion and exclusion criteria. Studies were appraised for methodological quality using the Quality of Health Economic Studies checklist and for reporting quality using the Consolidated Health Economic Evaluation Reporting Standards checklist. Results: A total of seven studies were examined, representing both private and public healthcare systems. Overall, these studies demonstrate high methodological and reporting quality. When compared to existing imaging technologies or standard care practices, PSMA-PET/CT or MRI is cost-effective within country-specific willingness-to-pay thresholds. Conclusion: Cost-effectiveness evaluations may not be widely generalizable due to significant variability among geographical regions concerning resource availability, costs, morbidity and mortality, and standards of practice. As PSMA-PET/CT imaging technology becomes more widely available in additional countries in the coming years, we expect to generate more country-specific data.

Indexed as

cancer stagingMarkov modelprostate cancerPSMA PETQALY

Identifiers

PMID41918916
PMCPMC13033869

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.