ArticleCancer research2016
Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach.
Article in Cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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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
35 citing papers in PubMed.
- Model-Driven Optimization of Subcutaneous Polymer Prodrugs Achieves Cancer Remission in Mice.Advanced healthcare materials · 2026Article
- Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.PLoS computational biology · 2024Article
- Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.Clinical & experimental metastasis · 2024Article
- Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach.PLoS computational biology · 2024Article
- Myeloid-Derived Suppressor-Cell Dynamics Control Outcomes in the Metastatic Niche.Cancer immunology research · 2023Article
- Harnessing immunotherapy to enhance the systemic anti-tumor effects of thermosensitive liposomes.Drug delivery and translational research · 2023Article
- Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control.Frontiers in oncology · 2023Article
- Practical identifiability analysis of a mechanistic model for the time to distant metastatic relapse and its application to renal cell carcinoma.PLoS computational biology · 2022Article
- Current challenges in metastasis research and future innovation for clinical translation.Clinical & experimental metastasis · 2022Article
- Immunization with short peptide particles reveals a functional CD8Journal for immunotherapy of cancer · 2021Article
- Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression.Molecular cancer · 2021Article
- A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment.PLoS computational biology · 2020Article
- The initial engraftment of tumor cells is critical for the future growth pattern: a mathematical study based on simulations and animal experiments.BMC cancer · 2020Article
- A Novel 4-Gene Score to Predict Survival, Distant Metastasis and Response to Neoadjuvant Therapy in Breast Cancer.Cancers · 2020Article
- Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors.PLoS computational biology · 2020Article
- Metastasis Initiation Precedes Detection of Primary Cancer-Analysis of Metastasis GrowthFrontiers in physiology · 2020Article
- An Overview ofBioMed research international · 2020Review
- Assessing the interactions between radiotherapy and antitumour immunity.Nature reviews. Clinical oncology · 2019Review
- Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer.Scientific reports · 2019Article
- A Mathematical Framework for Modelling the Metastatic Spread of Cancer.Bulletin of mathematical biology · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Rapid improvements in the detection and tracking of early-stage tumor progression aim to guide decisions regarding cancer treatments as well as predict metastatic recurrence in patients following surgery. Mathematical models may have the potential to further assist in estimating metastatic risk, particularly when paired with in vivo tumor data that faithfully represent all stages of disease progression. Herein, we describe mathematical analysis that uses data from mouse models of spontaneous metastasis developing after surgical removal of orthotopically implanted primary tumors. Both presurgical (primary tumor) growth and postsurgical (metastatic) growth were quantified using bioluminescence and were then used to generate a mathematical formalism based on general laws of the disease (i.e., dissemination and growth). The model was able to fit and predict pre/postsurgical data at the level of the individual as well as the population. Our approach also enabled retrospective analysis of clinical data describing the probability of metastatic relapse as a function of primary tumor size. In these data-based models, interindividual variability was quantified by a key parameter of intrinsic metastatic potential. Critically, our analysis identified a highly nonlinear relationship between primary tumor size and postsurgical survival, suggesting possible threshold limits for the utility of tumor size as a predictor of metastatic recurrence. These findings represent a novel use of clinically relevant models to assess the impact of surgery on metastatic potential and may guide optimal timing of treatments in neoadjuvant (presurgical) and adjuvant (postsurgical) settings to maximize patient benefit.
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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.