ArticleBMC cancer2016
Differences in predictions of ODE models of tumor growth: a cautionary example.
Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Large-scale simulation of coverage and error rate tradeoffs for cancer detection in cell-free DNA whole-genome sequencing.Bioinformatics (Oxford, England) · 2026Article
- Follow-Up Bias in Tumor Dynamic Modeling: A Comparison of Classical and Neural-ODE Approaches.CPT: pharmacometrics & systems pharmacology · 2026Article
- Developing virtual physiology of human tumor tissue for malignancy assessment.NPJ precision oncology · 2026Article
- Optimal control theory as a method for designing multidrug adaptive therapy regimens.NPJ systems biology and applications · 2026Article
- ViVo: A Temporal Modeling Framework That Boosts Statistical Power and Minimizes Animal Usage.Journal of medicinal chemistry · 2026Article
- First-Line Treatment Strategies in IMDC Favourable-Risk Metastatic Clear Cell Renal Cell Carcinoma.Oncology research · 2026Review
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- A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC.CPT: pharmacometrics & systems pharmacology · 2025Article
- Probabilistic modeling of cell cycle dynamics in response to cell cycle targeting chemotherapy drugs to guide treatment strategies.PLoS computational biology · 2025Article
- Proper likelihood functions for parameter estimation in S-shaped models of unperturbed tumor growth.Scientific reports · 2025Article
- Mathematical models of intercellular signaling in breast cancer.Seminars in cancer biology · 2025Review
- Article
- Radiological progression of medulloblastoma from non-existence to symptomatic mass in 42 days: Case report, kinetic analysis, and literature review.Brain & spine · 2025Article
- Assessing tumor volumetric reduction with consideration for setup errors based on mathematical tumor model and microdosimetric kinetic model in single-isocenter VMAT for brain metastases.Physical and engineering sciences in medicine · 2024Article
- Curating models from BioModels: Developing a workflow for creating OMEX files.bioRxiv : the preprint server for biology · 2024Article
- Quantitative evaluation of DNA damage repair dynamics to elucidate predictors of autism vs. cancer in individuals with germline PTEN variants.PLoS computational biology · 2024Article
- Impact of stroma remodeling on forces experienced by cancer cells and stromal cells within a pancreatic tumor tissue.Biomedical engineering online · 2024Article
- Article
- Assessing immunotherapy response: going beyond RECIST by integrating early tumor growth kinetics.Frontiers in immunology · 2024Article
- Modeling Physical Forces Experienced by Cancer and Stromal Cells Within Different Organ-Specific Tumor Tissue.IEEE journal of translational engineering in health and medicine · 2024Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.
methodsWe examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.
resultsWe find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.
conclusionOur results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.
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