Streamlining diagnosis of melanoma metastasis to the breast: A case report emphasizing distinctive MRI features as crucial diagnostic tool.

Andrew Chan, Hal Berman, Saeed Kamil Zaid, Sandeep Ghai, Supriya Kulkarni, Vivianne Freitas

Abstract readCase Reports

In one paragraph

Article in Radiology case reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Andrew ChanJoint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.

Hal BermanDepartment of Laboratory Medicine and Pathobiology, Laboratory Medicine Program, University of Toronto, University Health Network, 200 Elizabeth Street, 11-E444, Toronto, Ontario M5G 2C4, Canada.

Saeed Kamil ZaidDepartment of Laboratory Medicine and Pathobiology, Laboratory Medicine Program, University of Toronto, University Health Network, 200 Elizabeth Street, 11-E444, Toronto, Ontario M5G 2C4, Canada.

Sandeep GhaiJoint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.

Supriya KulkarniJoint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.

Vivianne FreitasJoint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women's College Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article describes the case of a 60-year-old woman with a remote history of uveal melanoma who presented with breast metastasis. The breast imaging findings are thoroughly discussed, covering conventional imaging, MRI, and PET-CT, with particular emphasis on the unique imaging characteristics of metastatic melanoma on MRI, which is notably different from other breast pathologies due to the paramagnetic properties of melanin. These properties contribute to a characteristic signal pattern that aids in distinguishing melanoma from other breast lesions, facilitating the diagnosis.

Indexed as

Breast neoplasmComputed tomography (PET-CT)Magnetic resonance imaging (MRI)MammographyMetastasis melanomaPositron emission tomographyUltrasound

Identifiers

PMID40492160

DOI10.1016/j.radcr.2025.04.081

PMCPMC12148502

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LicenceCC BY-NC-ND

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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.