Practical Understanding of Cancer Model Identifiability in Clinical Applications

Abstract

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

Structural and practical identifiability of contrast transport models for DCE-MRI

“…Looking at the corresponding compensating profiles, we observe that v p estimation is not influenced by variation, while v e and K trans show variability in response to . This result is reasonable in relation to the results in (14): the expression for v p is independent from the other parameters, whereas K trans , v e , and expressions depends on common coe cients (i.e., a 1 , a 2 , and a 4 ).…”

“…Therefore, there is a need to investigate how the kinetic parameters involved in the class of nested models used in DCE-MRI analysis can be determined, and what affects their identifiability, even in simple cases. Model identifiability becomes especially important for biological systems due to limited availability and quality of the data available [12, 13, 14]. Moreover, the amount and the quality of the data have a strong impact on the parameter identifiability of some parameters and, thus, on model outcomes and predictions.…”

Convergence, Sampling and Total Order Estimator Effects on Parameter Orthogonality in Global Sensitivity Analysis

“…Each subsection displays convergence of a single parameter for brevity. 2 . Physiologically realistic time series solutions generated from the model can be found in appendix A, figure 12.…”

“…Practical identifiability accounts for the role of noise and sampling frequency inter alia in hindering the ability uniquely to estimate inputs. These issues notwithstanding, the study of unique parameter estimation is very important to the complex models increasingly used in life sciences, which encompass pharmacology, epidemiology and cardiovascular applications [2, 3, 4]. Assuming one can identify inputs representative of the data, we arrive at model personalisation - a process of effectively calibrating a life science model using data available from an individual subject or patient.…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

“…This avenue holds promise for personalized cancer care, customizing treatments based on individual patient traits and diverse tumor characteristics, including size, type, growth rate, heterogeneity, genetics, and immune composition [37,54]. For mathematical models to operate with precision, the accurate identification of critical parameters hinges on the acquisition of frequent and precise data [55]. ODE-based equations are commonly employed in mechanistic and deterministic models where underlying mechanisms and deterministic relationships are of primary interest (Figure 1).…”