ArticleMetabolites2024
Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, and Artificial Intelligence to Interrogate the Metabolic Evolution of Glioblastoma.
Article in Metabolites, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolic Imaging as Future Technology and Innovation in Brain-Tumour Surgery: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs and a week earlier than HPMRS alone. Our model accurately predicted in vivo biomarkers from HPMRS, and the results inferred biological relevance. Additionally, the model showed potential for examining treatment effects. Our model successfully detected tumor progression two weeks earlier than conventional MRIs and accurately predicted in vivo biomarkers using ex vivo information such as conventional MRIs, HPMRS, and tumor size data. The accuracy of these predictions is consistent with biological relevance.
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Identifiers
What OpenQuestion holds
Registered trials
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.