Evidence map›Paper›PMID 40161666›Full record

ArticlebioRxiv : the preprint server for biology2025

Interstitial fluid transport dynamics predict glioblastoma invasion and progression.

Cora M Carman-Esparza, Caleb A Stine, Naciye Atay, Kathryn M Kingsmore, Maosen Wang, Ryan T Woodall, Russell C Rockne, Jessica J Cunningham, Jennifer M Munson

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Cora M Carman-EsparzaORCID 0000-0002-9383-9475
Naciye Atay
Kathryn M KingsmoreORCID 0000-0002-9923-4709
Jessica J CunninghamORCID 0000-0001-8704-8848
Jennifer M MunsonORCID 0000-0002-9477-1505

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastoma is characterized by aggressive infiltration into surrounding brain tissue, hindering complete surgical resection and contributing to poor patient outcomes. Identifying tumor-specific invasion patterns is essential for advancing our understanding of glioblastoma progression and improving surgical and radiotherapeutic strategies. Here, we leverage in vivo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively quantify interstitial fluid velocity, direction, and diffusion within and around glioblastomas. We introduce a novel vector-based pathline analysis to trace downstream accumulation of fluid flow originating from the tumor core, providing a spatially explicit perspective on local flow patterns. We find that localized fluid transport metrics predict glioblastoma invasion and progression, offering a new framework to non-invasively identify high-risk regions and guide targeted treatment approaches. One sentence summary: Invasion and progression of glioblastoma can be predicted with interstitial fluid flow patterns via magnetic resonance imaging.

Identifiers

PMID40161666
PMCPMC11952461

What Socratic holds

Textmetadata
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.