Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 175

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

How tissue fluidity influences brain tumor progression

Open access
Streitberger, Kaspar-Josche, Lilaj, Ledia, Schrank, Felix, Braun, Jürgen, Hoffmann, Karl-Titus, Reiss-Zimmermann, Martin, Käs, Josef A., Sack, Ingolf·2020·Proceedings of the National Academy of Sciences

Abstract

Significance: Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults, with one of the most dismal prognoses of all cancers. Yet, the reason why GBM grows so fast and aggressively is not understood. Our study provides an intriguingly simple explanation for the infiltrative behavior of GBM by looking at fluidity—a material property that quantifies the viscous behavior of biological tissues measured in vivo by magnetic resonance elastography. We show that fluidity of neurotumors is anomalous in that it decreases as water content increases, enabling GBM to infiltratively finger into normal surrounding brain matter. Fluidity might thus become an important target for cancer research and opens horizons for the diagnosis and treatment of cancer.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1913511116

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 175

Metadata

Authors
Streitberger, K.-J., Lilaj, L., Schrank, F., Braun, J., Hoffmann, K.-T., Reiss-Zimmermann, M., Käs, J. A., & Sack, I.
Venue
Proceedings of the National Academy of Sciences
Published
2019-12-16
Volume
117
Issue
1
Pages
128-134
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2996744075
Cited by
175
References
2
FWCI
8.09
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: How tissue fluidity influences brain tumor progression. https://app.cochat.ai/papers/10.1073/pnas.1913511116
The paper
Streitberger, K.-J., Lilaj, L., Schrank, F., Braun, J., Hoffmann, K.-T., Reiss-Zimmermann, M., Käs, J. A., & Sack, I. (2020). How tissue fluidity influences brain tumor progression. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1913511116

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.