Evidence map›Paper›PMID 41258200›Full record

ArticleScientific data2025

A longitudinal MRI dataset of brain metastases with tumor segmentations, clinical & radiomic data.

Dimitra Flouri, Christos-Panagiotis Papanikas, Georgios C Manikis, Eleftherios Ioannou, Georgia Karaoli, Elisavet Papageorgiou, Anastasia Constantinidou, Loizos Siakallis, Marilena Theodorou, Vasileios Vavourakis

Abstract readDataset
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

10 authors.

Dimitra FlouriIn Silico Modelling Group, Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.ORCID 0000-0002-8846-3573
Christos-Panagiotis PapanikasIn Silico Modelling Group, Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
Georgios C ManikisIn Silico Modelling Group, Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
Eleftherios IoannouIn Silico Modelling Group, Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
Georgia KaraoliBank of Cyprus Oncology Center, Nicosia, Cyprus.ORCID 0009-0007-4851-3285
Elisavet PapageorgiouBank of Cyprus Oncology Center, Nicosia, Cyprus.
Anastasia ConstantinidouBank of Cyprus Oncology Center, Nicosia, Cyprus.
Loizos SiakallisMedical School, University of Cyprus, Nicosia, Cyprus.
Marilena TheodorouBank of Cyprus Oncology Center, Nicosia, Cyprus. marilena.theodorou@bococ.org.cy.
Vasileios VavourakisIn Silico Modelling Group, Department of Mechanical & Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus. vavourakis.vasileios@ucy.ac.cy.ORCID 0000-0002-4102-2084

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain metastases (BM) are a severe complication of multiple primary malignancies and constitute the most frequent tumors of the central nervous system. Clinical imaging plays a crucial role in the management of BMs, serving as an essential tool for accurate diagnosis, effective treatment planning, and systematic follow-up care. However, progress in improving BM diagnosis and treatment is hindered by the limited availability of high-quality, annotated imaging datasets that reflect clinical variability in morphology, treatment planning, and patient characteristics. This study presents a comprehensive longitudinal dataset of 40 patients, including 744 MRI scans (baseline and follow-up), as well as 45 radiotherapy plans and 45 computed tomography scans. Detailed segmentations of 65 BMs are provided, defining three key tumor regions: enhancing tumor, edema, and necrotic core, offering a precise representation of tumor morphology for advanced quantitative analysis. This data-sharing initiative aims to support research efforts in automated BM detection, lesion segmentation, disease assessment, treatment planning, while contributing to the development of pertinent predictive and prognostic tools to enhance clinical decision-making.

Indexed as

Brain NeoplasmsMagnetic Resonance ImagingHumansLongitudinal StudiesRadiomicsTomography, X-Ray Computed

Identifiers

PMID41258200
PMCPMC12630624

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.