Evidence map›Paper›PMID 40518301›Full record

ReviewMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2025

Application of Machine Learning to Breast MR Imaging.

Roberto Lo Gullo, Vivien van Veldhuizen, Tina Roa, Panagiotis Kapetas, Jonas Teuwen, Katja Pinker

Abstract readReview
In one paragraph

Review in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 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

6 authors.

Roberto Lo GulloDepartment of Radiology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, New York, NY, USA.ORCID http://orcid.org/0000-0001-6887-292X
Vivien van VeldhuizenAI for Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.ORCID http://orcid.org/0009-0002-9136-8734
Tina RoaDepartment of Radiology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Panagiotis KapetasDepartment of Radiology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, New York, NY, USA.ORCID http://orcid.org/0000-0003-2505-4753
Jonas TeuwenAI for Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.ORCID http://orcid.org/0000-0002-1825-1428
Katja PinkerDepartment of Radiology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, New York, NY, USA.ORCID http://orcid.org/0000-0002-2722-7331

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The demand for breast imaging services continues to grow, driven by expanding indications in breast cancer diagnosis and treatment. This increasing demand underscores the potential role of artificial intelligence (AI) to enhance workflow efficiency as well as to further unlock the abundant imaging data to achieve improvements along the breast cancer pathway. Although AI has made significant advancements in mammography and digital breast tomosynthesis, with commercially available computer-aided detection (CAD systems) widely used for breast cancer screening and detection, its adoption in breast MRI has been slower. This lag is primarily attributed to the inherent complexity of breast MRI examinations and also hence the more limited availability of large, well-annotated publicly available breast MRI datasets. Despite these challenges, interest in AI implementation in breast MRI remains strong, fueled by the expanding use and indications for breast MRI. This article explores the implementation of AI in breast MRI across the breast cancer care pathway, highlighting its potential to revolutionize the way we detect and manage breast cancer. By addressing current challenges and examining emerging AI applications, we aim to provide a comprehensive overview of how AI is reshaping breast MRI and improving outcomes for patients.

Indexed as

Breast NeoplasmsImage Interpretation, Computer-AssistedMachine LearningMagnetic Resonance ImagingArtificial IntelligenceBreastFemaleHumansMammographyartificial intelligencebreastdeep learningmachine learningmagnetic resonance imaging

Identifiers

PMID40518301
PMCPMC12263444

What OpenQuestion holds

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LicenceCC BY-NC-ND
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 OpenQuestion graph.