ReviewClinical & developmental immunology2010
Theoretical modeling techniques and their impact on tumor immunology.
Review in Clinical & developmental immunology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Mathematical Modeling Support for Lung Cancer Therapy-A Short Review.International journal of molecular sciences · 2023Review
- Concepts and Applications of Information Theory to Immuno-Oncology.Trends in cancer · 2021Review
- Cancer systems immunology.eLife · 2020Review
- Simulating cancer growth with multiscale agent-based modeling.Seminars in cancer biology · 2015Review
- On Study of Immune Response to Tumor Cells in Prey-Predator System.International scholarly research notices · 2014Article
- In silico modeling of the immune system: cellular and molecular scale approaches.BioMed research international · 2014Review
- Bioinformatics for cancer immunology and immunotherapy.Cancer immunology, immunotherapy : CII · 2012Review
- Development of immune-specific interaction potentials and their application in the multi-agent-system VaccImm.PloS one · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.