Evidence map›Paper›PMID 40128670›Full record

ArticleBMC medical imaging2025

Structured reporting of gliomas based on VASARI criteria to improve report content and consistency.

Olivia Goodkin, Jiaming Wu, Hugh Pemberton, Ferran Prados, Sjoerd B Vos, Stefanie Thust, John Thornton, Tarek Yousry, Sotirios Bisdas, Frederik Barkhof

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 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

10 authors.

Olivia GoodkinQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Jiaming WuCentre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Hugh PembertonCentre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Ferran PradosQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Sjoerd B VosCentre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Stefanie ThustNeuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
John ThorntonNeuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
Tarek YousryNeuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
Sotirios BisdasNeuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
Frederik BarkhofQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. f.barkhof@ucl.ac.uk.

Funding

National Institute of Health Research (NIHR)
6 · The paper itself

Abstract

purposeGliomas are the commonest malignant brain tumours. Baseline characteristics on structural MRI, such as size, enhancement proportion and eloquent brain involvement inform grading and treatment planning. Currently, free-text imaging reports depend on the individual style and experience of the radiologist. Standardisation may increase consistency of feature reporting.

methodsWe compared 100 baseline free-text reports for glioma MRI scans with a structured feature list based on VASARI criteria and performed a full second read to document which VASARI features were in the baseline report.

resultsWe found that quantitative features including tumour size and proportion of necrosis and oedema/infiltration were commonly not included in free-text reports. Thirty-three percent of reports gave a description of size only, and 38% of reports did not refer to tumour size at all. Detailed information about tumour location including involvement of eloquent areas and infiltration of deep white matter was also missing from the majority of free-text reports. Overall, we graded 6% of reports as having omitted some key VASARI features that would alter patient management.

conclusionsTumour size and anatomical information is often omitted by neuroradiologists. Comparison with a structured report identified key features that would benefit from standardisation and/or quantification. Structured reporting may improve glioma reporting consistency, clinical communication, and treatment decisions.

Indexed as

Brain NeoplasmsGliomaMagnetic Resonance ImagingFemaleHumansMaleGliomaMagnetic resonance imaging (MRI)Structured reporting

Identifiers

PMID40128670
PMCPMC11934815

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Registered trials

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