Evidence map›Paper›PMID 41093713›Full record

ReviewPET clinics2026

Toward Digital Twins for Optimal Radioembolization.

Nisanth Kumar Panneerselvam, Guneet Mummaneni, Emilie Roncali

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in PET clinics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Nisanth Kumar PanneerselvamDepartment of Biomedical Engineering, University of California Davis, Davis, CA, USA.
Guneet MummaneniDepartment of Biomedical Engineering, University of California Davis, Davis, CA, USA.
Emilie RoncaliDepartment of Biomedical Engineering, University of California Davis, Davis, CA, USA; Department of Radiology, University of California Davis Health, Sacramento, CA, USA. Electronic address: eroncali@ucdavis.edu.

Funding

A liver digital twin for personalized cancer therapyU01CA289068 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI RONCALI, EMILIE · 2024 to 2025
$1.1M
NCI NIH HHS U01 CA289068
6 · The paper itself

Abstract

Radioembolization is a liver cancer treatment delivering radioactive microspheres (20-60 μm) to tumors via a catheter in the hepatic arterial tree. Treatment response depends on multiple factors including the complex hepatic artery anatomy, variable blood flow, and microsphere transport. Patient-specific digital twins powered by computational fluid dynamics (CFD) and physics-informed artificial intelligence (AI) methods offer a promising solution to optimize planning. This review discusses core principles of CFD and generative AI applied to radioembolization, emphasizing physics-informed networks and their role in translating digital twins into clinical practice for enhanced personalization and precision in treatment delivery.

Indexed as

Artificial IntelligenceEmbolization, TherapeuticLiver NeoplasmsRadiotherapy Planning, Computer-AssistedHumansHydrodynamicsMicrospheresCFDComputational fluid dynamicsDigital twinsGANsGenerative AIInterventional radiologyPINNsRadioembolization

Identifiers

PMID41093713
PMCPMC13282155

What OpenQuestion holds

Textmetadata
LicenceTDM
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.