Evidence map›Paper›PMID 39507514›Full record

ReviewFrontiers in physiology2024

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine.

Sharvari Kemkar, Mengdi Tao, Alokendra Ghosh, Georgios Stamatakos, Norbert Graf, Kunal Poorey, Uma Balakrishnan, Nathaniel Trask, Ravi Radhakrishnan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Cancer: A bioelectric disease?Clinical and translational medicine · 2026
    Review
  6. Article
  7. Review
  8. Article
  9. Review
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  11. Modeling tumor transport and growth with poroelastic biopolymer networks.bioRxiv : the preprint server for biology · 2025
    Article
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  14. Review
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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

9 authors.

Sharvari KemkarDepartment of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States.
Mengdi TaoDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.
Alokendra GhoshDepartment of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States.
Georgios StamatakosIn Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Zografos, Greece.
Norbert GrafDepartment of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany.
Kunal PooreyDepartment of Systems Biology, Sandia National Laboratories, Livermore, CA, United States.
Uma BalakrishnanDepartment of Quant Modeling and SW Eng, Sandia National Laboratories, Livermore, CA, United States.
Nathaniel TraskDepartment of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States.
Ravi RadhakrishnanDepartment of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States.

Funding

A physical sciences approach to investigate the role of exosomes in metastatic progressionU01CA250044 · NCI · UNIVERSITY OF PENNSYLVANIA · PI GUO, WEI, RADHAKRISHNAN, RAVI · 2021 to 2025
$3.9M
NCI NIH HHS U01 CA250044
6 · The paper itself

Abstract

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and

Indexed as

agent based modelsmachine learing algorithmsmodel interpretability and forecastingmultiphysics modelsverification validation uncertainty quatification

Identifiers

PMID39507514
PMCPMC11537925

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.