Evidence map›Paper›PMID 40299162›Full record

ArticleEuropean radiology experimental2025

Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.

Jacob N Gloe, Eric A Borisch, Adam T Froemming, Akira Kawashima, Jordan D LeGout, Hirotsugu Nakai, Naoki Takahashi, Stephen J Riederer

Abstract read
In one paragraph

Article in European radiology experimental, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Optimal Selection of the Effective Echo Time (TEJournal of magnetic resonance imaging : JMRI · 2026
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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

8 authors.

Jacob N GloeDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Eric A BorischDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Adam T FroemmingDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Akira KawashimaDepartment of Radiology, Mayo Clinic, Phoenix, AZ, USA.
Jordan D LeGoutDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Hirotsugu NakaiKyoto University, Kyoto, Japan.
Naoki TakahashiDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Stephen J RiedererDepartment of Radiology, Mayo Clinic, Rochester, MN, USA. riederer@mayo.edu.ORCID http://orcid.org/0000-0001-8045-910X

Funding

EXTRAMURAL RES FACIL IMPROVEMENT PROG: POLYCYSTIC KIDNEY DISEASE, RENAL STUDIES,C06RR018898 · NCRR · MAYO CLINIC · PI BURNETT, JOHN C · 2004 to 2004
$2.3M
Super Resolution for Improved Multislice Prostate MRIR01EB031790 · NIBIB · MAYO CLINIC ROCHESTER · PI RIEDERER, STEPHEN J · 2022 to 2025
$2.3M
NCRR NIH HHS C06 RR018898NIBIB NIH HHS R01 EB031790
6 · The paper itself

Abstract

backgroundT2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

methodsThis retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.

resultsThe model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.

conclusionThe DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%. RELEVANCE STATEMENT: DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning. KEY POINTS: Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.

Indexed as

Deep LearningMagnetic Resonance ImagingProstateProstatic NeoplasmsAgedHumansImage Interpretation, Computer-AssistedMaleMiddle AgedRetrospective StudiesArtificial intelligenceDeep learningMagnetic resonance imagingProstateTime management

Identifiers

PMID40299162
PMCPMC12040773

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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.