13/08/2025
Early assessment of efficacy and dose optimization remain critical challenges in the development of anticancer therapies.
The present research sought to develop a methodological framework for the optimization of tumor models, offering a basis for more accurate predictions of tumor dynamics.
Five widely used tumor size models were evaluated, of which only three—the Bi-Exponential (BiExp), the Linear-Exponential (LExp), and Claret’s Tumor Growth Inhibition (TGI) model—demonstrated reproducibility of the base model during a repeated cross-validation approach.
Furthermore, extrapolation from 3 to 16 months revealed outlier predictions for the BiExp and TGI models, while the LExp model showed higher consistency, suggesting that models utilizing an exponential growth function may have a more limited extrapolation range than those assuming linear growth.
All three models showed high accuracy in distinguishing RECIST-based objective responders, while accuracy in predicting the emergence of acquired resistance remained uniformly low.
