ArticleFrontiers in oncology2022
From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Internet of Things-Enhanced Mathematical Oncology: Conceptual Framework for Adaptive Cancer Care Modeling.JMIR cancer · 2026Article
- Mathematical Modeling of the Role of Cytokines in Sindbis Virus Treatment of Glioblastoma.CPT: pharmacometrics & systems pharmacology · 2026Article
- Review
- A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation.BMC cancer · 2025Article
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models.NPJ systems biology and applications · 2024Article
- Minimally sufficient experimental design using identifiability analysis.NPJ systems biology and applications · 2024Article
- Building virtual patients using simulation-based inference.Frontiers in systems biology · 2024Article
- A Continuation Technique for Maximum Likelihood Estimators in Biological Models.Bulletin of mathematical biology · 2023Article
- Myeloid-Derived Suppressor-Cell Dynamics Control Outcomes in the Metastatic Niche.Cancer immunology research · 2023Article
- A practical guide for the generation of model-based virtual clinical trials.Frontiers in systems biology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.
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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.