SynthesisBMC cancer2019
Accuracy of multiparametric magnetic resonance imaging for diagnosing prostate Cancer: a systematic review and meta-analysis.
Synthesis in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 9 of them syntheses that pooled it.
What it found
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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.
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Who cites it
45 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Standalone 29-MHz micro-ultrasound for classifying clinically significant prostate cancer: a systematic review and diagnostic test accuracy meta-analysis of prospective studies.Abdominal radiology (New York) · 2026Pooled it
- The Benefits and Harms of Screening for Prostate Cancer in Adults Aged 18 Years and Older: A Systematic Review.Current oncology (Toronto, Ont.) · 2026Pooled it
- Diagnostic efficacy of [BMC cancer · 2024Pooled it
- The diagnostic accuracy of radiolabeled PSMA-ligand PET for tumour staging in newly diagnosed prostate cancer patients compared to histopathology: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2023Pooled it
- 2022 Update on Prostate Cancer Epidemiology and Risk Factors-A Systematic Review.European urology · 2023Pooled it
- Early Detection of Prostate Cancer: AUA/SUO Guideline Part II: Considerations for a Prostate Biopsy.The Journal of urology · 2023Guideline
- Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with prostate cancer.ESMO open · 2022Guideline
- A systematic review and meta-analysis of Histoscanning™ in prostate cancer diagnostics.World journal of urology · 2021Pooled it
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- MR Virtual Cytometry for Zone-specific Prostate Cancer Characterization with Histologic and Molecular Validation.Radiology. Imaging cancer · 2026Article
- Multiparametric magnetic resonance imaging (MRI) integrating amide proton transfer-weighted (APTw) imaging, synthetic MRI, and multiplexed sensitivity encoding diffusion-weighted imaging (MUSE DWI) in the diagnosis and grading of prostate cancer.Quantitative imaging in medicine and surgery · 2026Article
- 3D-StyleGAN2-ADA: Volumetric Synthesis of Realistic Prostate T2W MRI.Journal of imaging · 2026Article
- The predictive value of multiparametric magnetic resonance imaging in enhancing prostate-specific antigen assessment for prostate cancer: a cross-sectional study.Translational andrology and urology · 2026Article
- Dietary docosahexaenoic acid impairs prostate cancer progression in TRAMP mice in the early stages of disease through modulation of inflammatory microenvironment.Journal of molecular histology · 2025Article
- An overview of utilizing artificial intelligence in localized prostate cancer imaging.Expert review of medical devices · 2025Review
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Tumor dormancy and relapse: understanding the molecular mechanisms of cancer recurrence.Military Medical Research · 2025Review
- Global Trends in Urological Evidence Synthesis: A Bibliometric Analysis of Systematic Reviews and Meta-analyses.Medical journal of the Islamic Republic of Iran · 2025Review
- Negative magnetic resonance imaging cannot be used to omit an initial prostate biopsy - An ambispective study.Prostate international · 2024Article
- Differential outcomes of re-stratified high-risk prostate cancer patients treated with external beam radiation therapy plus high-dose-rate brachytherapy boost.Journal of contemporary brachytherapy · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe application of multiparametric magnetic resonance imaging (mpMRI) for diagnosis of prostate cancer has been recommended by the European Association of Urology (EAU), National Comprehensive Cancer Network (NCCN), and European Society of Urogenital Radiology (ESUR) guidelines. The purpose of this study is to systematically review the literature on assessing the accuracy of mpMRI in patients with suspicion of prostate cancer.
methodWe searched Embase, Pubmed and Cochrane online databases from January 12,000 to October 272,018 to extract articles exploring the possibilities that the pre-biopsy mpMRI can enhance the diagnosis accuracy of prostate cancer. The numbers of true- and false-negative results and true- and false-positive ones were extracted to calculate the corresponding sensitivity and specificity of mpMRI. Study quality was assessed using QUADAS-2 tool. Random effects meta-analysis and a hierarchical summary receiver operating characteristic (HSROC) plot were performed for further study.
resultsAfter searching, we acquired 3741 articles for reference, of which 29 studies with 8503 participants were eligible for inclusion. MpMRI maintained impressive diagnostic value, the area under the HSROC curve was 0.87 (95%CI,0.84-0.90). The sensitivity and specificity for mpMRI were 0.87 [95%CI, 0.81-0.91] and 0.68 [95%CI,0.56-0.79] respectively. The positive likelihood ratio was 2.73 [95%CI 1.90-3.90]; negative likelihood ratio was 0.19 [95% CI 0.14,-0.27]. The risk of publication bias was negligible with P = 0.96.
conclusionResults of the meta-analysis suggest that mpMRI is a sensitive tool to diagnose prostate cancer. However, because of the high heterogeneity existing among the included studies, further studies are needed to apply the results of this meta-analysis in clinic.
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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.