Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Area of Science:

• Mathematical Biology
• Computational Oncology
• Immunotherapy Research

Background:

• Mathematical models are crucial for predicting tumor growth and immune cell interactions.
• The Kuznetsov model is a prominent framework but lacks validation with large-scale patient data.
• Parameter estimation for these models presents a significant challenge for clinical application.

Purpose of the Study:

• To quantitatively fit the Kuznetsov model to a large dataset of 1472 cancer immunotherapy patients.
• To perform a global practical identifiability analysis on the estimated model parameters.
• To assess the model's predictive capabilities for future tumor dynamics.

Main Methods:

• Individual patient parameter estimation for the Kuznetsov model using a dataset of 1472 patients.
• Global practical identifiability analysis to understand parameter value combinations.
• Model extrapolation by omitting recent data points to predict future tumor growth.

Main Results:

• Successful quantitative fitting of the Kuznetsov model to a large patient cohort.
• Demonstrated that multiple parameter combinations can achieve accurate data fitting.
• Showcased the model's ability to extrapolate and predict tumor growth dynamics.

Conclusions:

• The study validates the Kuznetsov model with real-world patient data, overcoming previous limitations.
• Identified potential for global parameter estimation, simplifying model application.
• The model's predictive power supports adaptive treatment strategies in cancer immunotherapy.