Evidence map›Paper›PMID 38783912›Full record

ArticlePolish journal of radiology2024

Assessing the effectiveness of MRI,

Mayur K Virarkar, Stephen K Gruschkus, Gregory C Ravizzini, Sai Swarupa R Vulasala, Sanaz Javadi, Priya Bhosale

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Polish journal of radiology, 2024. 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. Review
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

6 authors.

Mayur K VirarkarDepartment of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, USA.
Stephen K GruschkusDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
Gregory C RavizziniDepartment of Nuclear Medicine, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.
Sai Swarupa R VulasalaDepartment of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, USA.
Sanaz JavadiDepartment of Abdominal Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.
Priya BhosaleDepartment of Abdominal Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The primary objective of this study was to evaluate the discriminatory utility of magnetic resonance imaging (MRI), Material and methods: Patients were characterised for clinical features such as Gleason score, PSA at surgery, PSA at follow-up, follow-up MRI result, follow-up PET result, follow-up SUV Results: The study found that MRI had the highest concordance (96%), sensitivity (100%), specificity (91%), positive predictive value (93%), and negative predictive value (100%) among the diagnostic modalities. The AUC for MRI was 0.9545, indicating a high discriminatory ability for detecting prostate cancer local recurrence. When combined, PET and SUV Conclusions: The analysis suggests that MRI is the most effective imaging modality for detecting local prostate cancer recurrence, with

Indexed as

18F-fluciclovine PETMRIprostatePSArecurrenceSUV

Identifiers

PMID38783912
PMCPMC11112415

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.