ArticleJournal of clinical bioinformatics2013
Mathematical models for translational and clinical oncology.
Article in Journal of clinical bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Optimizing cancer therapy: a review of the multifaceted effects of metronomic chemotherapy.Frontiers in cell and developmental biology · 2024Review
- Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2023Review
- Optimizing combination therapy in a murine model of HER2+ breast cancer.Computer methods in applied mechanics and engineering · 2022Article
- Clonal evolution driven by superdriver mutations.BMC evolutionary biology · 2020Article
- Introduction to Mathematical Oncology.JCO clinical cancer informatics · 2019Article
- Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology.Frontiers in immunology · 2019Review
- Diagnostic assessment of osteosarcoma chemoresistance based on Virtual Clinical Trials.Medical hypotheses · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In the context of translational and clinical oncology, mathematical models can provide novel insights into tumor-related processes and can support clinical oncologists in the design of the treatment regime, dosage, schedule, toxicity and drug-sensitivity. In this review we present an overview of mathematical models in this field beginning with carcinogenesis and proceeding to the different cancer treatments. By doing so we intended to highlight recent developments and emphasize the power of such theoretical work.We first highlight mathematical models for translational oncology comprising epidemiologic and statistical models, mechanistic models for carcinogenesis and tumor growth, as well as evolutionary dynamics models which can help to describe and overcome a major problem in the clinic: therapy resistance. Next we review models for clinical oncology with a special emphasis on therapy including chemotherapy, targeted therapy, radiotherapy, immunotherapy and interaction of cancer cells with the immune system.As evident from the published studies, mathematical modeling and computational simulation provided valuable insights into the molecular mechanisms of cancer, and can help to improve diagnosis and prognosis of the disease, and pinpoint novel therapeutic targets.
Identifiers
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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.