ArticleCancer research2013
Spreaders and sponges define metastasis in lung cancer: a Markov chain Monte Carlo mathematical model.
Article in Cancer research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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.
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Who cites it
44 citing papers in PubMed, 91 citations in OpenAlex.
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- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- A Network Based Model for Predicting Spatial Progression of Metastasis.Bulletin of mathematical biology · 2025Article
- AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response.Bulletin of mathematical biology · 2024Article
- Reuniting philosophy and science to advance cancer research.Biological reviews of the Cambridge Philosophical Society · 2023Article
- Stereotactic magnetic resonance-guided online adaptive radiotherapy of adrenal metastases combines high ablative doses with optimized sparing of organs at risk.Clinical and translational radiation oncology · 2023Article
- Mapping lesion-specific response and progression dynamics and inter-organ variability in metastatic colorectal cancer.Nature communications · 2023Article
- Model-based inference of metastatic seeding rates in de novo metastatic breast cancer reveals the impact of secondary seeding and molecular subtype.Scientific reports · 2022Article
- Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian.BMC bioinformatics · 2022Article
- Prediction of Metastatic Patterns in Bladder Cancer: Spatiotemporal Progression and Development of a Novel, Web-based Platform for Clinical Utility.European urology open science · 2021Article
- Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach.Scientific reports · 2021Article
- Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer.Journal of mathematical biology · 2020Article
- Combination of Brachytherapy with Iodine-125 Seeds and Systemic Chemotherapy versus Systemic Chemotherapy Alone for Synchronous Extracranial Oligometastatic Non-Small Cell Lung Cancer.Cancer management and research · 2020Article
- Modeling the Onset of Symptoms of COVID-19.Frontiers in public health · 2020Article
- Circulating Tumor Cells: High-Throughput Imaging of CTCs and Bioinformatic Analysis.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2020Review
- Single cell correlation analysis of liquid and solid biopsies in metastatic colorectal cancer.Oncotarget · 2019Article
- Consecutive seeding and transfer of genetic diversity in metastasis.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- A Mathematical Framework for Modelling the Metastatic Spread of Cancer.Bulletin of mathematical biology · 2019Article
- Inferring rates of metastatic dissemination using stochastic network models.PLoS computational biology · 2019Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
The classic view of metastatic cancer progression is that it is a unidirectional process initiated at the primary tumor site, progressing to variably distant metastatic sites in a fairly predictable, although not perfectly understood, fashion. A Markov chain Monte Carlo mathematical approach can determine a pathway diagram that classifies metastatic tumors as "spreaders" or "sponges" and orders the timescales of progression from site to site. In light of recent experimental evidence highlighting the potential significance of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large autopsy data sets, to quantify the stochastic, systemic, and often multidirectional aspects of cancer progression. We quantify three types of multidirectional mechanisms of progression: (i) self-seeding of the primary tumor, (ii) reseeding of the primary tumor from a metastatic site (primary reseeding), and (iii) reseeding of metastatic tumors (metastasis reseeding). The model shows that the combined characteristics of the primary and the first metastatic site to which it spreads largely determine the future pathways and timescales of systemic disease.
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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.