Evidence map›Paper›PMID 41266453›Full record

ArticleScientific data2025

An Integrated Dataset of Metastatic Breast Cancer to the Brain with Imaging, Radiomics, and Tumor Genetics.

Birra R Taha, David J Wu, Luke T Sabal, Megan Kollitz, Lindsey Sloan, B Aika Shoo, Jianling Yuan, Matthew Hunt, Michael C Park, David Darrow and 2 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 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. 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

12 authors.

Birra R TahaDepartment of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA. taha@umn.edu.
David J WuDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Luke T SabalDepartment of Radiology, University of Iowa, Iowa City, Iowa, USA.ORCID 0000-0002-7114-1375
Megan KollitzDepartment of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
Lindsey SloanDepartment of Radiation Oncology, University of Minnesota, Minneapolis, Minnesota, USA.
B Aika ShooDepartment of Radiation Oncology, University of Minnesota, Minneapolis, Minnesota, USA.
Jianling YuanDepartment of Radiation Oncology, University of Minnesota, Minneapolis, Minnesota, USA.
Matthew HuntDepartment of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA.
Michael C ParkDepartment of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA.
David DarrowDepartment of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA.ORCID 0000-0001-9335-0584
Andrew S VenteicherDepartment of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA.
Yoichi WatanabeDepartment of Radiation Oncology, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces a unique magnetic resonance imaging dataset focusing on metastatic breast cancer to the brain, a significant clinical challenge in cancer treatment. Comprising 297 T1-weighted post-contrast images from 165 patients, this dataset from the University of Minnesota Medical Center is the first dedicated to breast cancer brain metastases. This collection includes expert-reviewed lesion segmentations with original image files, genetic markers, and an extensive array of tumor-derived radiomic features. The dataset's uniqueness lies in its detailed focus on metastatic breast cancer to the brain-offering a rich resource for advanced image-based tumor phenotyping and the vast potential for radiogenomic-based predictions based on machine learning model development. The inclusion of clinician-reviewed tumor segmentations and radiomic features, encompassing shape and texture characteristics, enhances the dataset's utility. This dataset aims to facilitate a deeper understanding of breast cancer metastasis to the brain, promote advancements in precision medicine, and improve patient care.

Indexed as

Brain NeoplasmsBreast NeoplasmsFemaleHumansMagnetic Resonance ImagingRadiomics

Identifiers

PMID41266453
PMCPMC12635208

What OpenQuestion holds

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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.