Evidence map›Paper›PMID 39017990›Full record

ReviewPhysical and engineering sciences in medicine2024

Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review.

Allison M Ng, Kelly M MacKinnon, Alistair A Cook, Rebecca A D'Alonzo, Pejman Rowshanfarzad, Anna K Nowak, Suki Gill, Martin A Ebert

Abstract readReview
In one paragraph

Review in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

8 authors.

Allison M NgSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Kelly M MacKinnonSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Alistair A CookNational Centre for Asbestos Related Diseases, The University of Western Australia, Crawley, WA, Australia.
Rebecca A D'AlonzoSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Pejman RowshanfarzadSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Anna K NowakNational Centre for Asbestos Related Diseases, The University of Western Australia, Crawley, WA, Australia.
Suki GillSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Martin A EbertSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia. Martin.Ebert@health.wa.gov.au.ORCID http://orcid.org/0000-0002-6875-0719

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

Indexed as

Computer SimulationImmunotherapyNeoplasmsCombined Modality TherapyHumansCancer immunotherapyMathematical modellingRadiotherapyTreatment response

Identifiers

PMID39017990
PMCPMC11666662

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.