Evidence map›Paper›PMID 40264944›Full record

ArticleNeuro-oncology advances

Synthetic O-(2-

Eric Suero Molina, Mehnaz Tabassum, Ghasem Azemi, Zeynep Özdemir, Wolfgang Roll, Philipp Backhaus, Philipp Schindler, Alex Valls Chavarria, Carlo Russo, Sidong Liu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Eric Suero MolinaMacquarie Neurosurgery & Spine, Macquarie University Hospital, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0001-5652-2105
Mehnaz TabassumCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.
Ghasem AzemiComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Zeynep ÖzdemirDepartment of Neurosurgery, University Hospital Münster, Münster, Germany.
Wolfgang RollDepartment of Nuclear Medicine, University Hospital Münster, Münster, Germany.
Philipp BackhausDepartment of Nuclear Medicine, University Hospital Münster, Münster, Germany.
Philipp SchindlerClinic for Radiology, University Hospital Münster, Münster, Germany.
Alex Valls ChavarriaDepartment of Neurosurgery, University Hospital Münster, Münster, Germany.
Carlo RussoComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Sidong LiuCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.
Walter StummerDepartment of Neurosurgery, University Hospital Münster, Münster, Germany.ORCID https://orcid.org/0000-0002-3460-0640
Antonio Di IevaDepartment of Neurosurgery, Nepean Blue Mountains Local Health District, Kingswood, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Limited amino acid availability for positron emission tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic O-(2- Methods: We trained a deep learning (DL)-based model to segment tumors in MRI, extracted radiomic features using the Python PyRadiomics package, and utilized  a Random Forest classifier to predict high [ Results: We included magnetic resonance imaging (MRI) and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [ Conclusion: For the first time, we demonstrate that predicting high [

Indexed as

[18F]FET-PETdeep learningGANgliomaradiomics

Identifiers

PMID40264944
PMCPMC12012690

What OpenQuestion holds

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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.