Evidence map›Paper›PMID 40116986›Full record

ArticleJournal of neuro-oncology2025

Early and late structural brain changes after radiation therapy: an MRI study in glioma patients.

Erika Joselyn Ludeña Maza, Juliana Fernandes Pavoni, Renata Ferranti Leoni

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Article in Journal of neuro-oncology, 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

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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

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

3 authors.

Erika Joselyn Ludeña MazaDepartment of Physics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil.
Juliana Fernandes PavoniDepartment of Physics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil.
Renata Ferranti LeoniDepartment of Physics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil. leonirf@usp.br.ORCID http://orcid.org/0000-0002-4568-0746

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeGiven the increasing survival rate of patients with brain tumors, it is relevant to investigate the relationship between radiation dose and the severity of lesions in healthy tissue. This study evaluated the effects of radiotherapy (RT) on healthy brain tissue in glioma patients using quantitative magnetic resonance imaging (MRI) to identify radiation-sensitive brain regions and support personalized RT strategies.

methodsA retrospective analysis included 75 MRIs from 25 patients (Group A) and 39 MRIs from 13 patients (Group B). All underwent postoperative RT with doses ranging from 25 to 60 Gy. MRIs were collected multiple times: before RT, up to 8 months post-RT (Group A), and between 9 and 16 months post-RT (Group B). A linear mixed-effects model identified predictors of volume reduction, cortical thinning, and increased apparent diffusion coefficient (ADC) after RT.

resultsSignificant ADC increases were observed in the hemisphere ipsilateral to the tumor site after RT, in the cuneus, inferior parietal, paracentral, pericalcarine, parahippocampal, precuneus, and superior parietal cortices. When controlling for the confounding factors (sex, age, tumor type, scanner, imaging interval, total dose, and regional dose), ADC changes correlated with dose in contralateral middle temporal, par opercularis, and lingual gyri.

conclusionsBrain volume reduction, cortical thinning, and, mainly, ADC increase were observed in healthy brain regions, indicating potential treatment sequelae. These findings emphasize the importance of developing neuroprotective strategies for radiation-sensitive brain regions and individualized treatment approaches to mitigate cognitive and functional impacts, enhancing patient care and quality of life.

Indexed as

BrainBrain NeoplasmsGliomaAdultAgedFemaleFollow-Up StudiesHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesYoung AdultBrain volumeCortical thicknessDiffusionGliomaLinear mixed-effect modelMagnetic resonance imaging

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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.