Evidence map›Paper›PMID 42678654›Full record

ArticleAnnals of biomedical engineering2026

Characterizing Tumor Cell Movement in Partial Differential Equation Models of Triple-Negative Breast Cancer Receiving Neoadjuvant Therapy.

Casey E Stowers, Chengyue Wu, Guillermo Lorenzo, David A Hormuth, Clinton Yam, Jingfei Ma, Gaiane M Rauch, Thomas E Yankeelov

Abstract read
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In one paragraph

Article in Annals of biomedical engineering, 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
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

8 authors.

Casey E StowersOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.ORCID http://orcid.org/0000-0002-7157-6248
Chengyue WuOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Guillermo LorenzoOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
David A HormuthOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Clinton YamDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jingfei MaDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Gaiane M RauchDepartment of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Thomas E YankeelovOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA. tey@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
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
Cancer Prevention and Research Institute of Texas RP220225Ministerio de Ciencia, Innovación y Universidades RYC2022-036010-INational Science Foundation DMS 2436499National Science Foundation Graduate Research Fellowship Program DE2137420NCI NIH HHS U01CA142565NCI NIH HHS U01CA174706NCI NIH HHS U24CA226110
6 · The paper itself

Abstract

purposeApproximately half of triple-negative breast cancer (TNBC) patients attain a complete response to neoadjuvant therapy (NAT). Thus, methods to predict and optimize NAT response are essential to improving patient outcomes.

methodsPreviously, mathematical models with a reaction term describing cell growth and death and a diffusion term describing cell invasion have accurately forecasted NAT response but have had limited flexibly in capturing cell movement. We investigate the relative contributions of reaction, diffusion, and advection terms in predicting TNBC response to NAT. We compare a reaction-only model to three models capturing cell movement using advection and diffusion terms that can be coupled to tissue mechanics.

resultsWhen compared to a reaction-only model, the reaction-diffusion model did not improve calibration or prediction accuracy for tumor volume or cell count. For example, the median absolute difference between the predicted and measured percent change in tumor volume across the cohort was 9.1% for both models. While computationally burdensome, the reaction-diffusion-advection model provided significantly (p < 0.05) more accurate calibrations and offered a small (e.g., 4-5% reduction in tumor volume error), but not significant, improvement in predictive accuracy for non-responding patients.

conclusionThese results suggest (i) a reaction-only model can provide fast predictions with equivalent accuracy to a reaction-diffusion model but cannot characterize an expanding tumor and (ii) the reaction-diffusion-advection model is the most accurate but is computationally burdensome. These insights could inform computational methods balancing the efficiency of the reaction-only model with the accuracy of the reaction-diffusion-advection model to guide clinical NAT decisions.

Indexed as

Magnetic resonance imagingModel calibrationReaction–diffusion–advection equation

Identifiers

PMID42678654

What OpenQuestion holds

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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.