A stochastic model for the sizes of detectable metastases Journal Article


Authors: Hanin, L.; Rose, J.; Zaider, M.
Article Title: A stochastic model for the sizes of detectable metastases
Abstract: A stochastic entirely mechanistic model of metastatic progression of cancer is developed. Based on this model the joint conditional distribution of the ordered sizes of detectable metastases given their number, n, is computed. It is shown that this distribution coincides with the joint distribution of order statistics for a random sample of size n derived from some probability distribution, and a formula for the latter is obtained. This formula is specialized for the case of exponentially growing primary and secondary tumors and exponentially distributed metastasis promotion times, and identifiability of model parameters is ascertained. These results allow for estimation of the natural history of cancer. As an example, it is estimated for a breast cancer patient with 31 bone metastases of known sizes. The proposed model for the sizes of detectable metastases provided an excellent fit to these data. © 2006 Elsevier Ltd. All rights reserved.
Keywords: bone neoplasms; primary tumor; disease course; bone metastasis; cancer diagnosis; neoplasms; metastasis; breast cancer; breast neoplasms; probability; cancer size; data analysis; computer simulation; neoplasm invasiveness; neoplasms, second primary; tumor growth; tumor; stochastic model; stochastic processes; theoretical model; sample size; statistical parameters; numerical model; statistical distribution; stochasticity; random sample; cancer natural history; model identifiability; poisson process
Journal Title: Journal of Theoretical Biology
Volume: 243
Issue: 3
ISSN: 0022-5193
Publisher: Elsevier Inc.  
Date Published: 2006-12-07
Start Page: 407
End Page: 417
Language: English
DOI: 10.1016/j.jtbi.2006.07.005
PUBMED: 16930629
PROVIDER: scopus
DOI/URL:
Notes: --- - "Cited By (since 1996): 9" - "Export Date: 4 June 2012" - "CODEN: JTBIA" - "Source: Scopus"
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Marco Zaider
    171 Zaider