Evidence map›Paper›PMID 30420794›Full record

ReviewContrast media & molecular imaging2018

How to Modulate Tumor Hypoxia for Preclinical In Vivo Imaging Research.

Sven De Bruycker, Christel Vangestel, Steven Staelens, Tim Van den Wyngaert, Sigrid Stroobants

Open access · hybridAbstract readReview
In one paragraph

Review in Contrast media & molecular imaging, 2018. 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
0.4field-weighted citation impact, top 43% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  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

5 authors at 1 institution in 1 country.

Sven De BruyckerMolecular Imaging Center Antwerp (MICA), University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.ORCID 0000-0002-7560-5130
Christel VangestelMolecular Imaging Center Antwerp (MICA), University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.
Steven StaelensMolecular Imaging Center Antwerp (MICA), University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.
Tim Van den WyngaertMolecular Imaging Center Antwerp (MICA), University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.
Sigrid StroobantsMolecular Imaging Center Antwerp (MICA), University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Antwerp, Belgium.ORCID 0000-0002-6450-9944
University of Antwerp · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor hypoxia is related with tumor aggressiveness, chemo- and radiotherapy resistance, and thus a poor clinical outcome. Therefore, over the past decades, every effort has been made to develop strategies to battle the negative prognostic influence of tumor hypoxia. For appropriate patient selection and follow-up, noninvasive imaging biomarkers such as positron emission tomography (PET) radiolabeled ligands are unprecedentedly needed. Importantly, before being able to implement these new therapies and potential biomarkers into the clinical setting, preclinical in vivo validation in adequate animal models is indispensable. In this review, we provide an overview of the different attempts that have been made to create differential hypoxic in vivo cancer models with a particular focus on their applicability in PET imaging studies.

Indexed as

Tumor HypoxiaAnimalsBiomarkers, TumorDisease Models, AnimalDrug Evaluation, PreclinicalHumansNeoplasmsPositron-Emission TomographyRadiopharmaceuticalsBiomarkers, TumorRadiopharmaceuticals

Identifiers

PMID30420794
PMCPMC6211155
OpenAlexW2896295544

What OpenQuestion 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 OpenQuestion graph.