Estimating tumor growth rates in vivo
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764475Norton [] took the lethal tumor volume to be N L = 10 12 cells, but used a carrying capacity of 3.1 × 10 12 cells so the tumor size would actually reach N L.To fit the Gompertz model to the Bloom data set [] on mortality from untreated breast cancers, he took the number of cells at detection to be N(0) = 4.8 × 10 9 and assumed a lognormally distributed growth rate with mean ln(r) =−2.9 …
Estimating tumor growth rates in vivo
services.math.duke.edu › ~rtd › cgrowthIn this paper we develop methods for inferring tumor growth rates from the obser-vation 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, several interesting
Estimating tumor growth rates in vivo
www.ncbi.nlm.nih.gov › pmc › articlesIn this data set, four of the tumors, which were stage I but moderately or poorly differentiated, showed much larger growth rates than the others, so we removed them from our analysis. Their doubling times were from 17–31 days, see the diamonds in Figure 5. Among the remaining tumors the maximum time between observations was 0.69 years, with an average of 0.35 years.