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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers
Michael C Luo1, Elpiniki Nikolopoulou2, Jana L Gevertz1
1Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, United States.
Frontiers in Oncology
|May 16, 2022
Summary
Personalized cancer treatment using mathematical models shows sensitivity to fitting methods. Improving model consistency requires specific experimental data and robust average response analysis for reliable patient recommendations.
Area of Science:
- Oncology
- Mathematical Biology
- Immunotherapy
Background:
- Clinical cancer care often uses a one-size-fits-all approach.
- Personalized therapeutic design requires patient-specific data.
- Mathematical modeling offers a powerful tool for treatment personalization.
Purpose of the Study:
- To evaluate the efficacy of personalized fits using a validated mathematical model of murine cancer immunotherapy.
- To investigate the impact of fitting methodology on individualized treatment recommendations.
- To identify strategies for improving the consistency and reliability of personalized cancer therapy models.
Main Methods:
- Utilized a previously validated mathematical model of murine cancer immunotherapy.
- Performed personalized fits by applying different fitting methodologies and cost functions.
- Analyzed the sensitivity of predicted treatment responses to chosen fitting parameters.
- Assessed the impact of additional experimental measurements on model consistency.
- Quantified the robustness of the average response to enhance confidence in personalized recommendations.
Main Results:
- Predicted personalized treatment response is sensitive to the fitting methodology employed.
- A small number of targeted experimental measurements can significantly improve the consistency of personalized fits.
- Quantifying the robustness of the average response increases confidence in individualized treatment recommendations.
Conclusions:
- Current methods for personalized fits in cancer treatment may yield unreliable recommendations due to sensitivity to fitting methodology.
- Strategic acquisition of specific experimental data is crucial for enhancing the consistency of personalized mathematical models.
- Robustness analysis of average responses can bolster confidence in patient-specific therapeutic strategies.
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