Evidence map›Paper›PMID 40853469›Full record

ArticleAbdominal radiology (New York)2026

Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.

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

Abstract readMulticenter Study
In one paragraph

Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

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

9 authors.

Hirotsugu NakaiDepartment of Radiology, Mayo Clinic, Rochester, USA.
Adam T FroemmingDepartment of Radiology, Mayo Clinic, Rochester, USA.
Akira KawashimaDepartment of Radiology, Mayo Clinic, Scottsdale, AZ, USA.
Jordan D LeGoutDepartment of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Yasuhisa KurataDepartment of Radiology, Mayo Clinic, Rochester, USA.
Jacob N GloeDepartment of Radiology, Mayo Clinic, Rochester, USA.
Eric A BorischDepartment of Radiology, Mayo Clinic, Rochester, USA.
Stephen J RiedererDepartment of Radiology, Mayo Clinic, Rochester, USA.
Naoki TakahashiDepartment of Radiology, Mayo Clinic, Rochester, USA. Takahashi.Naoki@mayo.edu.

Funding

Super Resolution for Improved Multislice Prostate MRIR01EB031790 · NIBIB · MAYO CLINIC ROCHESTER · PI RIEDERER, STEPHEN J · 2022 to 2025
$2.3M
NIBIB NIH HHS R01 EB031790
6 · The paper itself

Abstract

objectivesTo determine whether deep learning (DL)-based image quality (IQ) assessment of T2-weighted images (T2WI) could be biased by the presence of clinically significant prostate cancer (csPCa).

methodsIn this three-center retrospective study, five abdominal radiologists categorized IQ of 2,105 transverse T2WI series into optimal, mild, moderate, and severe degradation. An IQ classification model was developed using 1,719 series (development set). The agreement between the model and radiologists was assessed using the remaining 386 series with a quadratic weighted kappa. The model was applied to 11,723 examinations that were not included in the development set and without documented prostate cancer at the time of MRI (patient age, 65.5 ± 8.3 years [mean ± standard deviation]). Examinations categorized as mild to severe degradation were used as target groups, whereas those as optimal were used to construct matched control groups. Case-control matching was performed to mitigate the effects of pre-MRI confounding factors, such as age and prostate-specific antigen value. The proportion of patients with csPCa was compared between the target and matched control groups using the chi-squared test.

resultsThe agreement between the model and radiologists was moderate with a quadratic weighted kappa of 0.53. The mild-moderate IQ-degraded groups had significantly higher csPCa proportions than the matched control groups with optimal IQ: moderate (N = 126) vs. optimal (N = 504), 26.3% vs. 22.7%, respectively, difference = 3.6% [95% confidence interval: 0.4%, 6.8%], p = 0.03; mild (N = 1,399) vs. optimal (N = 1,399), 22.9% vs. 20.2%, respectively, difference = 2.7% [0.7%, 4.7%], p = 0.008.

conclusionThe DL-based IQ tended to be worse in patients with csPCa, raising concerns about its clinical application.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingProstatic NeoplasmsAgedBiasHumansMaleMiddle AgedProstateRetrospective StudiesCancer screeningDeep learningImage quality enhancementsMagnetic resonance imagingProstate cancer

Identifiers

PMID40853469
PMCPMC12713002

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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.