Evidence map›Paper›PMID 41145512›Full record

ArticleNPJ breast cancer2025

Breast density in MRI: an AI-based quantification and relationship to assessment in mammography.

Yaqian Chen, Lin Li, Hanxue Gu, Haoyu Dong, Derek L Nguyen, Allan D Kirk, Maciej A Mazurowski, E Shelley Hwang

Abstract read
In one paragraph

Article in NPJ breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. GuidedMorph: Two-Stage Deformable Registration for Breast MRI.IEEE journal of biomedical and health informatics · 2026
    Article
  3. Review
  4. 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

8 authors.

Yaqian Chen *Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
Lin Li *Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
Hanxue GuDepartment of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
Haoyu DongDepartment of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
Derek L NguyenDepartment of Radiology, Duke University School of Medicine, Durham, NC, USA.
Allan D KirkDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA.
Maciej A MazurowskiDepartment of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
E Shelley HwangDepartment of Surgery, Duke University School of Medicine, Durham, NC, USA. shelley.hwang@duke.edu.

Funding

National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health R01EB031575
6 · The paper itself

Abstract

Mammographic breast density is a well-established risk factor for breast cancer. Recently, there has been interest in breast MRI as an adjunct to mammography, as this modality provides an orthogonal and highly quantitative assessment of breast tissue. However, its 3D nature poses analytic challenges related to delineating and aggregating complex structures across slices. Here, we applied an in-house machine-learning algorithm to assess breast density on normal breasts in three MRI datasets. Breast density was consistent across different datasets (0.104-0.114). Analysis across different age groups also demonstrated strong consistency across datasets and confirmed a trend of decreasing density with age as reported in previous studies. MR breast density was correlated with mammographic breast density, although some notable differences suggest that certain breast density components are captured only on MRI. Future work will determine how to best integrate MR breast density with current tools to improve future breast cancer risk prediction.

Identifiers

PMID41145512
PMCPMC12559757

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Registered trials

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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.