Tumor dynamic models are vital for evaluating oncology treatments and guiding clinical drug development decisions. However, few studies rigorously assess their predictive capabilities, especially when forecasting tumor trajectories from clinical trials with short or inconsistent follow-up across treatment arms. Poor predictive performance or biases related to follow-up time could potentially limit the general utility of tumor growth inhibition (TGI) models. This study quantitatively evaluates prediction bias across several established tumor dynamic models, comparing five classical pharmacometric TGI models with the deep learning-based Tumor Dynamic Neural-ODE (TDNODE) framework. Using time-truncated clinical trial data from 3106 patients with non-small cell lung cancer (NSCLC) across four completed atezolizumab phase III studies, we consistently observed moderate-to-high positive bias in the predictions from pharmacometric models, particularly with more limited follow-up. By examining the structures of these models and comparing them to observed data, we highlight how the assumed kinetic patterns potentially lead to biased parameter estimation and systemic overestimation of tumor size when applied to immature datasets. In contrast, the TDNODE framework, using deep learning, demonstrated promising early results, exhibiting improved predictive performance in the same evaluations. These findings underscore the critical need to address prediction bias in tumor dynamic modeling with immature data and to consider alternative approaches to established paradigms for certain drug development applications. This study also generally demonstrates the potential of novel methods, such as deep learning, to potentially enhance the reliability of tumor dynamics modeling, especially in challenging early-phase clinical decision-making scenarios.
Turner et al. (Wed,) studied this question.