title: A "universal" model of metastatic cancer, its parametric forms and their identification: what can be learned from site-specific volumes of metastases.
abstract: We develop a methodology for estimating unobservable characteristics of the individual natural history of metastatic cancer from the volume of the primary tumor and site-specific volumes of metastases measured before, or shortly after, the start of treatment. In particular, we address the question as to what information about natural history of cancer can and cannot be gained from this type of data. Estimation of the natural history of cancer is based on parameterization of a very general mathematical model of cancer progression accounting for primary tumor growth, shedding of metastases, their selection, latency and growth in a given secondary site. This parameterization assumes Gompertz (and, as a limiting case, exponential) growth of the primary tumor, exponential growth of metastases, and exponential distribution of metastasis latency times. We find identifiable parameters of this model and give a rigorous proof of their identifiability. As an illustration, we analyze a clinical case of renal cancer patient who developed 55 lung metastases whose volumes were measured through laborious reading of CT images. The model with maximum likelihood parameters provided an excellent fit to this data. We uncovered many aspects of this patient's cancer natural history and showed that, according to the model, onset of metastasis occurred long before primary tumor became clinically detectable.
authors: Department of Mathematics and Statistics, Idaho State University, 921 S. 8th Avenue, Stop 8085, Pocatello, ID, 83209-8085, USA. hanin@isu.edu.
authors: Leonid
authors: L
authors: Hanin
authors: , Hoher Weg 6, 06120, Halle (Saale), Germany.
authors: Karen
authors: K
authors: Seidel
authors: Department of Diagnostic Radiology, Martin Luther University of Halle-Wittenberg, Ernst Grube Strasse 40, 06114, Halle, Germany.
authors: Dietrich
authors: D
authors: Stoevesandt
mesh_terms: D049109:Cell Proliferation; D018450:Disease Progression; D006801:Humans; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000556:secondary; D008297:Male; D055641:Mathematical Concepts; D008875:Middle Aged; D008954:Models, Biological; D009362:Neoplasm Metastasis / Q000000981:diagnostic imaging / Q000473:pathology*; D014057:Tomography, X-Ray Computed
keywords: Exponential tumor growth; Gompertz law of tumor growth; Metastatic latency; Model identifiability; Natural history of cancer; Poisson process