ArticleJournal of clinical bioinformatics2012
A dynamic model for tumour growth and metastasis formation.
Article in Journal of clinical bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Entropy, complexity, and Markov diagrams for random walk cancer models.Scientific reports · 2014Article
- Investigation of the essential role of platelet-tumor cell interactions in metastasis progression using an agent-based model.Theoretical biology & medical modelling · 2014Article
- The dormancy dilemma: quiescence versus balanced proliferation.Cancer research · 2013Review
- Spreaders and sponges define metastasis in lung cancer: a Markov chain Monte Carlo mathematical model.Cancer research · 2013Article
- Modeling boundary conditions for balanced proliferation in metastatic latency.Clinical cancer research : an official journal of the American Association for Cancer Research · 2013Article
- Cancer bioinformatics: a new approach to systems clinical medicine.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A simple and fast computational model to describe the dynamics of tumour growth and metastasis formation is presented. The model is based on the calculation of successive generations of tumour cells and enables one to describe biologically important entities like tumour volume, time point of 1st metastatic growth or number of metastatic colonies at a given time. The model entirely relies on the chronology of these successive events of the metastatic cascade. The simulation calculations were performed for two embedded growth models to describe the Gompertzian like growth behaviour of tumours. The initial training of the models was carried out using an analytical solution for the size distribution of metastases of a hepatocellular carcinoma. We then show the applicability of our models to clinical data from the Munich Cancer Registry. Growth and dissemination characteristics of metastatic cells originating from cells in the primary breast cancer can be modelled thus showing its ability to perform systematic analyses relevant for clinical breast cancer research and treatment. In particular, our calculations show that generally metastases formation has already been initiated before the primary can be detected clinically.
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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.