Evidence map›Paper›PMID 33917080›Full record

ArticleCancers2021

Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.

David A Hormuth, Angela M Jarrett, Tessa Davis, Thomas E Yankeelov

Abstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
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  6. 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
  7. Article
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  12. Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023
    Review
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  18. Review
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

4 authors.

David A HormuthOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.ORCID 0000-0002-9643-1694
Angela M JarrettOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.ORCID 0000-0002-6346-9557
Tessa DavisDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
Thomas E YankeelovOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.

Funding

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
Clinical Development of Rhenium Nanoliposomes (RNL186) for GlioblastomaR01CA235800 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI BRENNER, ANDREW JACOB · 2019 to 2023
$3.0M
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
American Association of Physicists in Medicine 2018-19 Seed GrantCancer Prevention and Research Institute of Texas RR160005NCI NIH HHS R01CA235800NCI NIH HHS U01CA174706NCI NIH HHS U01CA253540NCI NIH HHS U24CA226110
6 · The paper itself

Abstract

Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model's forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.

Indexed as

computational oncologyglioblastomamagnetic resonance imagingperfusionU87

Identifiers

PMID33917080
PMCPMC8067722

What OpenQuestion holds

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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.