Evidence map›Paper›PMID 40503095›Full record

ArticleResearch in diagnostic and interventional imaging2025

Deep learning reconstruction in biparametric prostate MRI: Impact on qualitative and radiomics analyses.

Jérémy Dana, Evan McNabb, Juan Castro, Ibtisam Al-Qanoobi, Yoshie Omiya, Kenny Ah-Lan, Véronique Fortier, Giovanni Artho, Caroline Reinhold, Simon Gauvin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research in diagnostic and interventional imaging, 2025. 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

10 authors.

Jérémy DanaMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Evan McNabbResearch Institute of the McGill University Health Centre, Augmented Intelligence & Precision Health Laboratory (AIPHL), Montréal, Canada.
Juan CastroMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Ibtisam Al-QanoobiMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Yoshie OmiyaMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Kenny Ah-LanMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Véronique FortierMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Giovanni ArthoMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Caroline ReinholdMcGill University, Department of Diagnostic Radiology, Montréal, Canada.
Simon GauvinMcGill University, Department of Diagnostic Radiology, Montréal, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To assess the impact of a commercially available deep learning reconstruction (DLR) algorithm on qualitative and radiomics analyses in prostate MRI. Methods: This single-centre retrospective cohort included 25 consecutive patients who underwent a prostate MRI (1.5 T) in 2022. T2-weighted (T2WI), diffusion-weighted (DWI; b = 50, 1000, extrapolated 2000 s/mm Results: PI-QUAL score was not significantly different ( Conclusion: Radiomics features were significantly different on DLR images which should encourage caution for clinical and research purposes. DLR algorithm decreases noise while preserving overall image quality.

Indexed as

Deep learning reconstructionProstate MRIQualitative analysisRadiomics

Identifiers

PMID40503095
PMCPMC12150108

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.