Predicting Patient-Specific Tumor Dynamics: How Many Measurements Are Necessary?

Simple Summary Accurately predicting tumor growth is an important component in effectively treating patients; unfortunately, acquiring sufficient data to correctly predict when a patient will progress on treatment often comes too late. In this study, we investigated the sufficient number of tumor volume measurements required to accurately predict logistic tumor growth. The model was calibrated to tumor volume data from 18 untreated breast cancer patients using a varying number of measurements. We found the number of data points necessary to be a function of the noise level and the acceptable error of the to-be-determined model parameters. This study will provide a metric by which clinicians can determine when sufficient data have been collected to confidently predict patient-specific growth dynamics, which will be aimed at assisting treatment decision-making. Acquiring sufficient data is imperative to accurately predict tumor growth dynamics and effectively treat patients. The aim of this study was to investigate the number of volume measurements necessary to predict breast tumor growth dynamics using the logistic growth model. The model was calibrated to tumor volume data from 18 untreated breast cancer patients using a varying number of measurements interpolated at clinically relevant timepoints with different levels of noise (0-20%). Error-to-model parameters and the data were compared to determine the sufficient number of measurements needed to accurately determine growth dynamics. We found that without noise, three tumor volume measurements are necessary and sufficient to estimate patient-specific model parameters. More measurements were required as the level of noise increased. Estimating the tumor growth dynamics was shown to depend on the tumor growth rate, clinical noise level, and acceptable error of the to-be-determined parameters. Understanding the relationship between these factors provides a metric by which clinicians can determine when sufficient data have been collected to confidently predict patient-specific tumor growth dynamics and recommend appropriate treatment options.