Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth

TL;DR

This study compares various mathematical models to describe and predict tumor growth in experimental systems, highlighting their strengths and limitations for biological understanding and potential clinical prognostics.

Contribution

It provides a systematic analysis of nine tumor growth models, evaluating their descriptive and predictive capabilities using experimental data.

Findings

Gompertz and power law best described lung tumor data.

Gompertz and exponential-linear models best captured breast tumor data.

Exponential-linear model showed highest predictive accuracy for breast tumors.

Abstract

Despite internal complexity, tumor growth kinetics follow relatively simple macroscopic laws that have been quantified by mathematical models. To resolve this further, quantitative and discriminant analyses were performed for the purpose of comparing alternative models for their abilities to describe and predict tumor growth. For this we used two in vivo experimental systems, an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: to 1) determine a statistical model for description of the volume measurement error, 2) establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) assess the models ability to forecast future tumor growth. Nine models were compared that included the exponential, power law, Gompertz and (generalized) logistic formalisms. The Gompertz and power law provided the most parsimonious and parametrically identifiable description of the lung data, whereas the breast data were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power for the breast tumor growth curves, with excellent prediction scores (greater than 80$\%$) extending out as far as 12 days. In contrast, for the lung data, none of the models were able to achieve substantial prediction rates (greater than 70$\%$) further than the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement of predictions. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinical setting.