Evidence map›Paper›PMID 36408159›Full record

ArticleFrontiers in oncology2022

Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer.

Mirjam Gerwing, Emily Hoffmann, Katharina Kronenberg, Uwe Hansen, Max Masthoff, Anne Helfen, Christiane Geyer, Lydia Wachsmuth, Carsten Höltke, Bastian Maus and 10 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

10 citing papers in PubMed, 16 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

20 authors at 3 institutions in 1 country.

Mirjam GerwingClinic of Radiology, University of Münster, Münster, Germany.
Emily HoffmannClinic of Radiology, University of Münster, Münster, Germany.
Katharina KronenbergInstitute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
Uwe HansenInstitute for Musculoskeletal Medicine, University of Münster, Münster, Germany.
Max MasthoffClinic of Radiology, University of Münster, Münster, Germany.
Anne HelfenClinic of Radiology, University of Münster, Münster, Germany.
Christiane GeyerClinic of Radiology, University of Münster, Münster, Germany.
Lydia WachsmuthClinic of Radiology, University of Münster, Münster, Germany.
Carsten HöltkeClinic of Radiology, University of Münster, Münster, Germany.
Bastian MausClinic of Radiology, University of Münster, Münster, Germany.
Verena HoerrClinic of Radiology, University of Münster, Münster, Germany.
Tobias KrählingClinic of Radiology, University of Münster, Münster, Germany.
Lena HiddeßenInstitute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
Walter HeindelClinic of Radiology, University of Münster, Münster, Germany.
Uwe KarstInstitute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
Melanie A KimmDepartment of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany.
Regina SchinnerDepartment of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany.
Michel EisenblätterClinic of Radiology, University of Münster, Münster, Germany.
Cornelius FaberClinic of Radiology, University of Münster, Münster, Germany.
Moritz WildgruberClinic of Radiology, University of Münster, Münster, Germany.
University of Münster · DELudwig-Maximilians-Universität München · DEUniversity of Freiburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) approach. Methods: The highly metastatic 4T1 breast cancer model was compared to the non-metastatic 67NR model. Imaging was conducted on a 9.4 T small animal MRI. The protocol was used to characterize tumors regarding their structural composition, including heterogeneity, intratumoral edema and hemorrhage, as well as endothelial permeability using apparent diffusion coefficient (ADC), T1/T2 mapping and dynamic contrast-enhanced (DCE) imaging. Mice were assessed on either day three, six or nine, with an i.v. injection of the albumin-binding contrast agent gadofosveset. Ex vivo validation of the results was performed with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), histology, immunhistochemistry and electron microscopy. Results: Significant differences in tumor composition were observed over time and between 4T1 and 67NR tumors. 4T1 tumors showed distorted blood vessels with a thin endothelial layer, resulting in a slower increase in signal intensity after injection of the contrast agent. Higher permeability was further reflected in higher K Conclusions: Multiparametric MRI as presented in this study enables for the estimation of malignant potential in the two studied tumor models

Indexed as

LA-ICP-MSMRIoncologic imagingtumor heterogeneitytumor vasculature

Identifiers

PMID36408159
PMCPMC9667047
OpenAlexW4308123343

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Registered trials

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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.