Document Detail


A method for parametric estimation of the number and size distribution of cell clusters from observations in a section plane.
MedLine Citation:
PMID:  9544509     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The problem of finding the number and size distribution of cell clusters that grow in an organ or tissue from observations of the number and sizes of transections of such cell clusters in a planar section is considered. This problem is closely related to the well-known corpuscle or Wicksell problem in stereology, which deals with transections of spherical objects. However, for most biological applications, it is unrealistic to assume that cell clusters have spherical shapes since they may grow in various ways. We therefore propose a method that allows for more general spatial configurations of the clusters. Under the assumption that a parametric growth model is available for the number and sizes of the cell clusters, expressions are obtained for the probability distributions of the number and sizes of transections of the clusters in a section plane for each point in time. These expressions contain coefficients that are independent of the parametric growth model and time but depend on which model is chosen for the configuration of the cell clusters in space. These results enable us to perform estimation of the parameters of the growth model by maximum likelihood directly on the data instead of having to deal with the inverse problem of estimation of three-dimensional quantities based on two-dimensional data. For realistic choices of the configuration model, it will not be possible to obtain the exact values of the coefficients, but they can easily be approximated by means of computer simulations of the spatial configuration. Monte Carlo simulations were performed to approximate the coefficients for two particular spatial configuration models. For these two configuration models, the proposed method is applied to data on preneoplastic minifoci in rat liver under the assumption of a two-event model of carcinogenesis as the parametric growth model.
Authors:
M C de Gunst; E G Luebeck
Related Documents :
20590179 - Nonequilibrium numerical model of homogeneous condensation in argon and water vapor exp...
16845899 - Modelling multivariate outcomes in hierarchical data, with application to cluster rando...
18718949 - An unsupervised conditional random fields approach for clustering gene expression time ...
17408499 - Simultaneous clustering of gene expression data with clinical chemistry and pathologica...
20033289 - Computer-assisted numerical analysis of colour-group data for dereplication of streptom...
16345119 - Multilevel logistic regression modelling with correlated random effects: application to...
22984789 - Structural equation model trees.
7245019 - Phlebography of the cavernous and intercavernous sinuses.
11497379 - Testing the validity of a critical sulfur and nitrogen load model in southern ontario, ...
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  54     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1998 Mar 
Date Detail:
Created Date:  1998-05-01     Completed Date:  1998-05-01     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  100-12     Citation Subset:  IM    
Affiliation:
Department of Mathematics, Free University of Amsterdam, The Netherlands. degunst@cs.vu.nl
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Algorithms
Animals
Biometry
Carcinogens / toxicity
Cell Count*
Cell Division*
Cocarcinogenesis
Likelihood Functions
Liver / drug effects,  pathology
Liver Neoplasms, Experimental / chemically induced,  pathology
Male
Models, Biological
Nitrosamines / toxicity
Precancerous Conditions / chemically induced,  pathology
Rats
Rats, Wistar
Stochastic Processes
Grant Support
ID/Acronym/Agency:
1 R55 ES07490-01/ES/NIEHS NIH HHS; 5 R01 CA47658-08/CA/NCI NIH HHS
Chemical
Reg. No./Substance:
0/Carcinogens; 0/Nitrosamines; 59-89-2/N-nitrosomorpholine

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Added risk and inverse estimation for count responses in reproductive aquatic toxicology studies.
Next Document:  Change-point analysis of neuron spike train data.