Evidence map›Paper›PMID 36804605›Full record

ArticleScientific reports2023

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.

Kalina P Slavkova, Sahil H Patel, Zachary Cacini, Anum S Kazerouni, Andrea L Gardner, Thomas E Yankeelov, David A Hormuth

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Article
  10. Article
  11. Article
  12. MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Review
  13. Article
  14. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Kalina P SlavkovaDepartment of Physics, The University of Texas at Austin, Austin, TX, USA.
Sahil H PatelDepartment of Computer Science, Case Western Reserve University, Cleveland, OH, USA.
Zachary CaciniDepartment of Bioengineering, University of Illinois, Urbana-Champaign, IL, USA.
Anum S KazerouniDepartment of Radiology, The University of Washington, Seattle, WA, USA.
Andrea L GardnerDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, USA.
Thomas E YankeelovDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, USA.
David A HormuthThe Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th Street, Austin, TX, 78712, USA. david.hormuth@austin.utexas.edu.

Funding

Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant TherapyU01CA142565 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KARCZMAR, GREGORY S., NANDA, RITA · 2010 to 2019
$4.5M
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
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
Using label-free Raman microscopy to predict therapeutic resistance of TNBC cellsU01CA253540 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI BROCK, AMY, YANKEELOV, THOMAS E · 2020 to 2024
$2.1M
Personalizing immunotherapy in HER2+ breast cancer through quantitative imagingR01CA240589 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SORACE, ANNA C · 2020 to 2023
$1.6M
Interoperable Software Platform for Reproducible Research and Clinical Translation of MRIU24EB029240 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI LUSTIG, MICHAEL, UECKER, MARTIN · 2019 to 2023
$1.6M
NCI NIH HHS R01 CA240589NCI NIH HHS R01 CA260003NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA174706NCI NIH HHS U01 CA253540NCI NIH HHS U24 CA226110NIBIB NIH HHS U24 EB029240
6 · The paper itself

Abstract

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 10

Indexed as

Brain NeoplasmsGliomaAnimalsDisease Models, AnimalFemaleMagnetic Resonance ImagingMiceRatsRats, Wistar

Identifiers

PMID36804605
PMCPMC9941120

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.