Evidence map›Paper›PMID 36422257›Full record

ArticleMetabolites2022

Multiparametric Magnetic Resonance Imaging and Metabolic Characterization of Patient-Derived Xenograft Models of Clear Cell Renal Cell Carcinoma.

Joao Piraquive Agudelo, Deepti Upadhyay, Dalin Zhang, Hongjuan Zhao, Rosalie Nolley, Jinny Sun, Shubhangi Agarwal, Robert A Bok, Daniel B Vigneron, James D Brooks and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
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

8 citing papers in PubMed, 7 citations in OpenAlex.

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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

13 authors at 2 institutions in 1 country.

Joao Piraquive AgudeloDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
Deepti UpadhyayDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
Dalin ZhangDepartment of Urology, Stanford University, Stanford, CA 94305, USA.
Hongjuan ZhaoDepartment of Urology, Stanford University, Stanford, CA 94305, USA.
Rosalie NolleyDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
Jinny SunDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
Shubhangi AgarwalDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0001-7564-3826
Robert A BokDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
Daniel B VigneronDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
James D BrooksDepartment of Urology, Stanford University, Stanford, CA 94305, USA.
John KurhanewiczDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0002-3544-5339
Donna M PeehlDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
Renuka SriramDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0003-3505-2479
University of California, San Francisco · USStanford University · US

Funding

TR&D3: Open-Source Tools for Processing Hyperpolarized MR DataP41EB013598 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Daniel B Vigneron · 2011 to 2026
$19.9M
Metabolic imaging comparisons of patient-derived models of renal cell carcinomaU01CA217456 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KURHANEWICZ, JOHN, PEEHL, DONNA M. · 2017 to 2021
$3.2M
National Institute of Health CA217456NCI NIH HHS U01 CA217456NIBIB NIH HHS P41 EB013598
6 · The paper itself

Abstract

Patient-derived xenografts (PDX) are high-fidelity cancer models typically credentialled by genomics, transcriptomics and proteomics. Characterization of metabolic reprogramming, a hallmark of cancer, is less frequent. Dysregulated metabolism is a key feature of clear cell renal cell carcinoma (ccRCC) and authentic preclinical models are needed to evaluate novel imaging and therapeutic approaches targeting metabolism. We characterized 5 PDX from high-grade or metastatic ccRCC by multiparametric magnetic resonance imaging (MRI) and steady state metabolic profiling and flux analysis. Similar to MRI of clinical ccRCC, T

Indexed as

hyperpolarized [1-13C]pyruvatemagnetic resonance imagingmetabolismpatient-derived xenograftsrenal cell carcinoma

Identifiers

PMID36422257
PMCPMC9692472
OpenAlexW4309306514

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.