Estimating Tumor Growth Rates In Vivo.
In this paper, we develop methods for inferring tumor growth rates from the observation of tumor volumes at two time points. We fit power law, exponential, Gompertz, and Spratt’s generalized logistic model to five data sets. Though the data sets are small and there are biases due to the way the samples were ascertained, there is a clear sign of exponential growth for the breast and liver cancers, and a 2/3’s power law (surface growth) for the two neurological cancers.
Anne
A
Talkington
Rick
R
Durrett
D001943:Breast Neoplasms / Q000473:pathology; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000473:pathology; D016015:Logistic Models; D055641:Mathematical Concepts; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology*; D009423:Nervous System Neoplasms / Q000473:pathology