ArticleMagnetic resonance in medicine2019
Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Influence of Intrapancreatic Fat Deposition on Regional and Total Pancreatic T1 Relaxation Times at 3.0 Tesla MRI.Journal of imaging · 2026Article
- Diagnostic and theranostic opportunities in Neuro-oncology.Advances in oncology · 2024Article
- Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2022Article
- Deep learning-based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging.NMR in biomedicine · 2022Article
- Learning-based analysis of amide proton transfer-weighted MRI to identify true progression in glioma patients.NeuroImage. Clinical · 2022Article
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purposeWe sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T
methodsThe T
resultsThe T
conclusionsMachine learning applied to the entire CEST spectrum improved the classification of the three tumor models, relative to classifications that used % CEST values at single saturation frequencies. A similar improvement was not attained with machine learning applied to T
Indexed as
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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.