Evidence map›Paper›PMID 42409342›Full record

ArticleMagnetic resonance imaging2026

Repeatability of an MRI protocol for generating habitats based on cellularity, perfusion, and hypoxia in a murine model of glioma.

Ayesha Das, David A Hormuth, John Virostko, Patrik Parker, C Chad Quarles, Thomas E Yankeelov

Abstract read
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In one paragraph

Article in Magnetic resonance imaging, 2026. 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
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0citing papers in PubMed
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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

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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

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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

6 authors.

Ayesha DasDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, United States.
David A HormuthOden Institute for Computational Science and Engineering, The University of Texas at Austin, Austin, TX 78712, United States; UT Austin Cancer Research Center, The University of Texas at Austin, Austin, TX 78712, United States.
John VirostkoDepartment of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, United States; UT Austin Cancer Research Center, The University of Texas at Austin, Austin, TX 78712, United States.
Patrik ParkerDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, United States.
C Chad QuarlesDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, United States.
Thomas E YankeelovDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, United States; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, United States; Oden Institute for Computational Science and Engineering, The University of Texas at Austin, Austin, TX 78712, United States; UT Austin Cancer Research Center, The University of Texas at Austin, Austin, TX 78712, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, United States. Electronic address: tey@utexas.edu.

Funding

Imaging-based tumor forecasting to predict brain tumor progression and response to therapyR01CA260003 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI QUARLES, CHRISTOPHER CHAD, YANKEELOV, THOMAS E · 2022 to 2025
$3.2M
Comprehensive Training Program in Imaging Science and InformaticsT32EB007507 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI MARKEY, MIA K, RYLANDER, HENRY GRADY · 2009 to 2024
$2.7M
NCI NIH HHS R01 CA260003NIBIB NIH HHS T32 EB007507
6 · The paper itself

Abstract

backgroundHypoxia, cellularity, and vascularity are well-established as important drivers of tumor growth and treatment response. Diffusion weighted (DW-), dynamic contrast enhanced magnetic (DCE-), and oxygen enhanced (OE-) MRI techniques have been developed to quantitatively map these three characteristics. We have developed and implemented a multi-parametric MRI protocol to identify tumor subregions that share similar biological features or "habitats".

methodsWe performed test-retest DW-, DCE-, and OE-MRI measurements on Wistar rats bearing a C6 glioma (n = 24). MRI data was summarized as apparent diffusion coefficient (ADC) maps from DWI-MRI, area under the curve (AUC) from DCE-MRI, and normalized change in T

resultsAcross all habitat construction approaches, Pearson and concordance correlation coefficients were both above 0.90 when comparing the test and retest habitat compositions.

conclusionThe combination of the MRI protocol and our data-driven approach can reliably generate habitats that capture distinct cellularity, perfusion-related information, and hypoxia patterns with a high degree of repeatability.

Indexed as

Brain NeoplasmsGliomaMagnetic Resonance ImagingAnimalsContrast MediaDiffusion Magnetic Resonance ImagingDisease Models, AnimalDynamic Contrast Enhanced Magnetic Resonance ImagingHypoxiaImage Processing, Computer-AssistedMaleMiceOxygenPerfusion Magnetic Resonance ImagingRatsRats, WistarContrast MediaOxygenClusteringDiffusion MRIDynamic contrast enhanced MRIGliomaMultiparametric MRIOxygen enhanced MRI

Identifiers

PMID42409342
PMCPMC13689712

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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.