Evidence map›Paper›PMID 40427181›Full record

ArticleCancers2025

An Activation Likelihood Estimation Meta-Analysis of Voxel-Based Morphometry Studies of Chemotherapy-Related Brain Volume Changes in Breast Cancer.

Sonya Utecht, Horacio Gomez-Acevedo, Jonathan Bona, Ellen van der Plas, Fred Prior, Linda J Larson-Prior

Abstract read
In one paragraph

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

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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.

Sonya UtechtDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0009-0000-9960-6130
Horacio Gomez-AcevedoDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-1570-1606
Jonathan BonaDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0003-1402-9616
Ellen van der PlasDepartment of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-7490-6636
Fred PriorDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-6314-5683
Linda J Larson-PriorDepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-9334-048X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesBreast cancer chemotherapy patients and survivors face cognitive side effects that are not fully understood. Neuroimaging can provide a unique way to study these effects; however, it can be difficult to recruit large numbers of subjects. Our meta-analysis aims to synthesize volumetric neuroimaging data to highlight consistent findings in regional brain volume changes to further advance our understanding of the chemotherapy-related cognitive impairments faced by breast cancer patients and survivors.

methodsAn Activation Likelihood Estimation analysis was conducted across the data from eight voxel-based morphometry experiments examining changes in the brains of breast cancer patients and survivors exposed to chemotherapy over time and three voxel-based morphometry experiments comparing chemotherapy-exposed subjects to controls with and without breast cancer.

resultsThere were consistent volume reductions across the whole brain in both experiment groups. The subjects' over-time analysis showed peak consistency among the studies in the right inferior frontal gyrus and the left insula.

conclusionsChemotherapy for non-central nervous system cancers such as breast cancer can cause physical changes throughout the brain that can be quantitatively measured by neuroimaging methodologies and may underlie persistent cognitive deficits in some individuals.

Indexed as

activation likelihood estimate (ALE)brain volumebreast cancercancer-related cognitive impairmentchemotherapychemotherapy-related cognitive impairmentmagnetic resonance imaging (MRI)meta-analysisneuroimagingvoxel-based morphometry (VBM)

Identifiers

PMID40427181
PMCPMC12109750

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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.