A ``universal model of metastatic cancer, its parametric forms and their identification: what can be learned from site-specific volumes of metastases

DOI10.1007/S00285-015-0928-6zbMath1346.92033OpenAlexW1915745819WikidataQ30316248 ScholiaQ30316248MaRDI QIDQ284068

Karen Seidel, Dietrich Stoevesandt, Leonid G. Hanin

Publication date: 17 May 2016

Published in: Journal of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00285-015-0928-6

zbMATH Keywords

Poisson processexponential tumor growthGompertz lawmetastatic latencymodel identifiabilitynatural history of cancer

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Non-Markovian processes: estimation (62M09) Medical applications (general) (92C50) Distribution theory (60E99) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (4)

A quantitative insight into metastatic relapse of breast cancer ⋮ Implications of immune-mediated metastatic growth on metastatic dormancy, blow-up, early detection, and treatment ⋮ The natural history of renal cell carcinoma with pulmonary metastases illuminated through mathematical modeling ⋮ Suppression of metastasis by primary tumor and acceleration of metastasis following primary tumor resection: A natural law?

Cites Work

• Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology
• A stochastic model for the sizes of detectable metastases
• Does extirpation of the primary breast tumor give boost to growth of metastases? Evidence revealed by mathematical modeling
• Reconstruction of the natural history of metastatic cancer and assessment of the effects of surgery: Gompertzian growth of the primary tumor
• Unnamed Item

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