Evidence mapPaperPMID 41729713Full record

ReviewAnnual review of biomedical engineering2026

Computational Fluid Dynamics Simulations to Inform Cancer Therapeutics.

Emilie Roncali, Amirtahà Taebi

Abstract readReview
In one paragraph

Review in Annual review 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

2 authors.

Emilie RoncaliDepartment of Biomedical Engineering, University of California, Davis, California, USA.
Amirtahà TaebiDepartment of Bioengineering, Lehigh University, Bethlehem, Pennsylvania, USA.

Funding

NCI NIH HHS R21 CA237686
6 · The paper itself

Abstract

Cancer therapies such as chemotherapy, radiopharmaceutical therapy, and transarterial embolization rely on effective drug or radiation delivery through the bloodstream. Understanding how various drugs and particles, which form and size span multiple scales, are transported through blood and tissue is essential for optimizing treatment. Computational fluid dynamics (CFD) is a powerful tool to simulate blood flow and drug transport, solving flow governing equations under biologically realistic conditions. This review explores CFD applications in cancer therapy, focusing on transarterial embolization, tumor perfusion, and organ-on-a-chip systems. In radioembolization, CFD can predict microsphere transport and dose distribution to spare vital functions. Tumor perfusion modeling and organ-on-a-chip systems benefit from CFD by replicating vascular dynamics and drug dispersion. Despite its versatility and established mechanical principles, CFD faces challenges, including the need for patient-specific data, computational demands, and multiscale modeling. This review highlights opportunities for integrating CFD with imaging modalities and artificial intelligence tools to overcome these barriers and advance personalized cancer treatment.

Indexed as

HydrodynamicsNeoplasmsAnimalsAntineoplastic AgentsComputer SimulationDrug Delivery SystemsEmbolization, TherapeuticHumansModels, BiologicalRadioembolization, TherapeuticAntineoplastic Agentscancer therapeuticscomputational fluid dynamicsfluid flowmodelingorgan-on-a-chipparticle transportsimulation

Identifiers

PMID41729713
PMCPMC13333982

What Socratic 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 Socratic graph.