Evidence map›Paper›PMID 42836157›Full record

ArticleThe Lancet medical imaging & theranostics2026

Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.

James K Ruffle, Samia Mohinta, Guilherme Pombo, Asthik Biswas, Alan Campbell, Indran Davagnanam, David Doig, Ahmed Hammam, Harpreet Hyare, Farrah Jabeen and 6 more

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In one paragraph

Article in The Lancet medical imaging & theranostics, 2026. 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

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

16 authors.

James K RuffleDepartment of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.
Samia MohintaDepartment of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.
Guilherme PomboNVIDIA, Reading, UK, UK.
Asthik BiswasGreat Ormond Street Hospital for Children, London, UK.
Alan CampbellImaging Department, Royal National Orthopaedic Hospital, Stanmore, Middlesex, UK.
Indran DavagnanamDepartment of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.
David DoigLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK.
Ahmed HammamLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK.
Harpreet HyareDepartment of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.
Farrah JabeenLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK.
Emma LimDepartment of Imaging, Imperial College Healthcare NHS Trust, London, UK.
Dermot MallonDepartment of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.
Stephanie OwenLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK.
Sophie WilkinsonDepartment of Radiology, University College Hospitals NHS Foundation Trust, London, UK.
Sebastian BrandnerDivision of Neuropathology and Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, UK.
Parashkev NachevDepartment of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background: Brain tumour imaging assessment typically requires both pre-contrast and post-contrast MRI, but gadolinium administration is not always desirable-for example, in patients requiring frequent follow-up, or those with renal impairment or allergy, or those who are of pediatric age [0-18 years]. We aimed to develop and validate a deep learning model to predict brain tumour contrast enhancement from non-contrast MRI sequences alone. Methods: In this multicohort, retrospective study, we compiled 11 089 brain MRI studies (acquired in 2006-24) from ten datasets across four countries (the UK, the USA, the Netherlands, and Nigeria) and three continents (North America, Europe, and Africa), spanning adult and paediatric populations with no age restriction, and including glioma, meningioma, metastases, and pre-treatment and post-treatment appearances. Three deep learning architectures (nnU-Net, SegResNet, and SwinUNETR) were trained to detect and segment enhancing tumour tissue from T1-weighted (T1w), T2-weighted (T2w), and T2/fluid-attenuated inversion recovery (FLAIR) sequences alone. Performance was assessed in a held-out test set of 1109 studies, with patient-level enhancement detection as the primary endpoint and voxel-level Dice score as the secondary endpoint. Eleven expert radiologists performed the same novel task on a 564-case subset, each reviewing 100 cases while being blinded to clinical history, previous imaging, and referral indication. Findings: The best performing model, nnU-Net, reached a balanced accuracy of 83·0% (95% CI 79·1-87·2; sensitivity 91·5% [89·9-93·1], specificity 74·4% [66·7-82·5]) for enhancement detection, with R Interpretation: Deep learning can identify contrast-enhancing brain tumours from non-contrast MRI. These models show promise as a triage or decision-support aid-for example, flagging studies in which post-contrast enhancement is likely so that contrast can be added to a non-contrast-only protocol-and could reduce gadolinium dependence in neuro-oncology imaging. Future studies should optimise these models through close collaboration with expert radiologists. Funding: European Society of Radiology; European Institute for Biomedical Imaging Research (EIBIR); Medical Research Council; Wellcome Trust; UCLH NIHR Biomedical Research Centre.

Identifiers

PMID42836157
PMCPMC13637711

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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.