ReviewCancers2021
Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed.
- Article
- Evolutionary Double-Bind Treatment Using Radiation Therapy and Natural Killer Cell-Based Immunotherapy in Prostate Cancer.International journal of radiation oncology, biology, physics · 2026Article
- Exploring tumorigenesis as a process of phenotypic state-space exploration and selective funneling.Evolution, medicine, and public health · 2026Article
- Fuzzy and fractional analysis of cancer tumor dynamics with depression effects on chemotherapy.Scientific reports · 2025Article
- MRI-based habitat imaging predicts high-risk molecular subtypes and early risk assessment of lower-grade gliomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- In vitro competition between two transmissible cancers and potential implications for their host, the Tasmanian devil.Evolutionary applications · 2024Article
- Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.Briefings in bioinformatics · 2024Article
- Metastasis Models: Thermodynamics and Complexity.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies.Frontiers in genetics · 2024Review
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- A Darwinian perspective on tumor immune evasion.Biochimica et biophysica acta. Reviews on cancer · 2022Review
- Adaptive Control of Tumor Growth.Cancer control : journal of the Moffitt Cancer CenterReview
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka-Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design.
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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.