Evidence map›Paper›PMID 40061122›Full record

ArticleArXiv2025

Validating the predictions of mathematical models describing tumor growth and treatment response.

Guillermo Lorenzo, David A Hormuth, Chengyue Wu, Graham Pash, Anirban Chaudhuri, Ernesto A B F Lima, Lois C Okereke, Reshmi Patel, Karen Willcox, Thomas E Yankeelov

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 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

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

10 authors.

Guillermo LorenzoGroup of Numerical Methods in Engineering, Department of Mathematics, University of A Coruña, Spain.
David A HormuthOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Chengyue WuOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Graham PashOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Anirban ChaudhuriOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Ernesto A B F LimaOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Lois C OkerekeOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Reshmi PatelOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Karen WillcoxOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Thomas E YankeelovOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.

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
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
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
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
Mathematical modeling and molecular imaging to maximize response while minimizing toxicities from systemic therapies in preclinical models of breast cancerR01CA276540 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SORACE, ANNA C, YANKEELOV, THOMAS E · 2023 to 2025
$1.4M
NCI NIH HHS R01 CA240589NCI NIH HHS R01 CA260003NCI NIH HHS R01 CA276540NCI NIH HHS U01 CA253540NCI NIH HHS U24 CA226110NIBIB NIH HHS T32 EB007507
6 · The paper itself

Abstract

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained

Indexed as

clinical validationdigital twinsexperimental validationmathematical oncologymechanistic modelingmodel selectionmodel validationPredictive oncologytumor forecastinguncertainty quantification

Identifiers

PMID40061122
PMCPMC11888553

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.