SynthesisProstate cancer and prostatic diseases2026
Clinical impact of MRI-based risk calculators for prostate cancer diagnosis: a systematic review and meta-analysis.
Synthesis in Prostate cancer and prostatic diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical impact of MRI-based risk calculators for prostate cancer diagnosis: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Florastamin Uptake on PSMA PET/CT as an Imaging Biomarker of Tumor Aggressiveness in Prostate Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Prostate MRI quality improvement: a Roadmap from the ESUR Prostate MRI Working Group.European radiology · 2026Review
- Drug Discovery Strategies for Kallikrein-Related Peptidases.International journal of molecular sciences · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundProstate cancer (PCa) is the second most common cancer among men worldwide. Current diagnostic methods often lack sufficient sensitivity and specificity, leading to unnecessary biopsy. With growing use of MRI and EAU guideline recommendations, this review synthesised evidence on MRI-based risk calculators (RCs) for PCa diagnosis and compared their performance with traditional clinical RCs.
methodsA systematic search of Embase, Medline, Scopus, Cochrane Library, and Web of Science databases assessed the discriminatory ability of MRI-based RCs using Area Under the Curve (AUC). A meta-analysis was conducted to pool AUC estimates, assess heterogeneity, and compare the differences in discriminatory ability.
resultsOf 2049 papers, 16 met the inclusion criteria. MRI-based RCs showed increased discrimination, with an AUC of 0.84 (95% CI: 0.81-0.86) for clinically significant PCa (csPCa), compared to 0.76 (95% CI: 0.73-0.79) for clinical models, and an AUC of 0.81 (95% CI: 0.78-0.84) for all PCa, compared to 0.74 (95% CI: 0.68-0.79). The pooled logit(AUC) difference was 0.49 units for csPCa and 0.37 units for all PCa. High heterogeneity was noted, likely due to PCa variability, and 31% of the studies had a high or unclear risk of bias, potentially affecting generalisability.
conclusionsMRI-based RCs improve the diagnostic accuracy for PCa with the potential to reduce unnecessary biopsies and optimise healthcare resources, thereby supporting their integration into clinical practice.
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Registered trials
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.