Evidence map›Paper›PMID 40494626›Full record

ArticleAJNR. American journal of neuroradiology2025

Automated Diffusion Analysis for Noninvasive Prediction of

Jiaming Wu, Stefanie C Thust, Stephen J Wastling, Gehad Abdalla, Massimo Benenati, John A Maynard, Sebastian Brandner, Ferran Prados Carrasco, Frederik Barkhof

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AJNR. American journal of neuroradiology, 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. Ultra-Fast IntraoperativeInternational journal of molecular sciences · 2025
    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

9 authors.

Jiaming WuFrom the Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering(J.W., F.P.C., F.B.), University College London, London, United Kingdom.
Stefanie C ThustNeuroradiological Academic Unit (J.W., S.C.T., S.J.W., J.A.M., F.B.), UCL Queen Square Institute of Neurology, University College London, London, United Kingdom stefanie.thust@nottingham.ac.uk.ORCID https://orcid.org/0000-0001-5136-6000
Stephen J WastlingNeuroradiological Academic Unit (J.W., S.C.T., S.J.W., J.A.M., F.B.), UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-4648-9932
Gehad AbdallaLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery (S.J.W., G.A., M.B., J.A.M., F.B.), University College London Hospitals NHS Foundation Trust, London, United Kingdom.
Massimo BenenatiLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery (S.J.W., G.A., M.B., J.A.M., F.B.), University College London Hospitals NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0001-8976-5939
John A MaynardNeuroradiological Academic Unit (J.W., S.C.T., S.J.W., J.A.M., F.B.), UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
Sebastian BrandnerDepartment of Neurodegenerative Disease, UCL Institute of Neurology, and Division of Neuropathology, National Hospital for Neurology and Neurosurgery (S.B.), University College London NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0002-9821-0342
Ferran Prados CarrascoFrom the Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering(J.W., F.P.C., F.B.), University College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7872-0142
Frederik BarkhofFrom the Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering(J.W., F.P.C., F.B.), University College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-3543-3706

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeGlioma molecular characterization is essential for risk stratification and treatment planning. Noninvasive imaging biomarkers such as ADC values have shown potential for predicting glioma genotypes. However, manual segmentation of gliomas is time-consuming and operator-dependent. To address this limitation, we aimed to establish a single-sequence-derived automatic ADC extraction pipeline by using T2-weighted imaging to support glioma MATERIALS AND

methodsGlioma volumes from a hospital data set (University College London Hospitals [UCLH];

resultsnnUNet segmentation achieved a median Dice of 0.85 on BraTS data, and 0.83 on UCLH data. For the best performing metric (normalized

conclusionsThe T2-weighted trained nnUNet algorithm achieved ADC readouts for IDH genotyping with a performance statistically equivalent to human observers. This approach could support rapid ADC-based identification of glioblastoma at an early disease stage, even with limited input data. Artificial intelligence level of evidence: 5A.

Indexed as

Brain NeoplasmsDiffusion Magnetic Resonance ImagingGliomaIsocitrate DehydrogenaseAdultAgedDeep LearningFemaleGenotypeHumansMaleMiddle AgedNeoplasm GradingIsocitrate Dehydrogenase

Identifiers

PMID40494626
PMCPMC12633691

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.