Estimating Tumor Growth Rates In Vivo.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Talkington, A; Durrett, R

Published Date

  • October 2015

Published In

Volume / Issue

  • 77 / 10

Start / End Page

  • 1934 - 1954

PubMed ID

  • 26481497

Pubmed Central ID

  • 26481497

Electronic International Standard Serial Number (EISSN)

  • 1522-9602

International Standard Serial Number (ISSN)

  • 0092-8240

Digital Object Identifier (DOI)

  • 10.1007/s11538-015-0110-8

Language

  • eng