2015
DOI: 10.1007/s11538-015-0110-8
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Estimating Tumor Growth Rates In Vivo

Abstract: 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.

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Cited by 125 publications
(104 citation statements)
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“…While tumor growth is exponential under an ideal scenario, factors such as availability of nutrients, oxygen, and space influence and alter the growth of the tumor initially compared to the continued growth of the tumor (Cornelis et al, 2013; Talkington and Durrett, 2015). Simultaneously, other murine immunogenic tumor models are known to spontaneously regress (Penichet et al, 2001; Robinson et al, 2009; Vince et al, 2004), which is a phenomenon known to naturally occur in cancer patients (Jessy, 2011; Saleh et al, 2005; Salman, 2016) .…”
Section: Resultsmentioning
confidence: 99%
“…While tumor growth is exponential under an ideal scenario, factors such as availability of nutrients, oxygen, and space influence and alter the growth of the tumor initially compared to the continued growth of the tumor (Cornelis et al, 2013; Talkington and Durrett, 2015). Simultaneously, other murine immunogenic tumor models are known to spontaneously regress (Penichet et al, 2001; Robinson et al, 2009; Vince et al, 2004), which is a phenomenon known to naturally occur in cancer patients (Jessy, 2011; Saleh et al, 2005; Salman, 2016) .…”
Section: Resultsmentioning
confidence: 99%
“…Note that the presented study is based on a matrix population model with constant elements, which in principle captures an exponential growing population. Even though there are still uncertainties regarding the growth patterns of cell populations in different contexts (cancer or normal, solid or hematologic tumor, in vivo or in vitro) [7,50] and exponential growth is often considered to be unable to capture the biological processes in reality, exponential-like growth models are widely used as default models to describe growing cell populations, especially in early cancer development [21,[51][52][53]. We followed this idea and used it as a starting point to explore the adaptive significance of de-differentiation.…”
Section: Discussionmentioning
confidence: 99%
“…Our model neglects a spatial component of tumor growth (46). This assumption leads to exponential growth in equilibrium, a situation well met in most leukemias (28,47,48).…”
Section: Discussionmentioning
confidence: 99%