Population Modeling of Tumor Growth Curves, the Reduced Gompertz Model and Prediction of the Age of a Tumor

Vaghi, Cristina; Rodallec, Anne; Fanciullino, Raphaëlle; Ciccolini, Joseph; Mochel, Jonathan P.; Mastri, Michalis; Ebos, John M.L.; Poignard, Clair; Benzekry, Sébastien

doi:10.1007/978-3-030-35210-3_7

Cited by 2 publications

(2 citation statements)

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“…each is priming on a type of tumor), and c) the small amount and irregular sampling of the data (e.g. 15 measurements with at days 6,10,13,17,19,21,24,28,31,34,38,41,45,48 for breast cancer growth in [26]). Although such competing canonical tumor growth models are commonly used, how to decide which of the models to use for which tumor types is still an open question.…”

Section: Model Equationmentioning

confidence: 99%

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Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics

Axenie¹,

Kurz

2020

Preprint

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Neoplastic processes are described by complex and heterogeneous dynamics. The interaction of neoplastic cells with their environment describes tumor growth and is critical for the initiation of cancer invasion. Despite the large spectrum of tumor growth models, there is no clear guidance on how to choose the most appropriate model for a particular cancer and how this will impact its subsequent use in therapy planning. Such models need parametrization that is dependent on tumor biology and hardly generalize to other tumor types and their variability. Moreover, the datasets are small in size due to the limited or expensive measurement methods. Alleviating the limitations that incomplete biological descriptions, the diversity of tumor types, and the small size of the data bring to mechanistic models, we introduce Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics (GLUECK) a novel, data-driven model based on a neural network capable of unsupervised learning of cancer growth curves. Employing mechanisms of competition, cooperation, and correlation in neural networks, GLUECK learns the temporal evolution of the input data along with the underlying distribution of the input space. We demonstrate the superior accuracy of GLUECK, against four typically used tumor growth models, in extracting growth curves from a four clinical tumor datasets. Our experiments show that, without any modification, GLUECK can learn the underlying growth curves being versatile between and within tumor types.

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Section: Model Equationmentioning

confidence: 99%

“…Finally, the work in [24] performed a quantitative analysis of tumor growth kinetics using nonlinear mixed-effects modeling on traditional mechanistic models. Using Bayesian inference, they inferred tumor volume from few (caliper and fluorescence) measurements.…”

Section: Predictive Models Of Tumor Growthmentioning

confidence: 99%