Cellular Automaton Simulation of Tumour Growth – Equivocal Relationships between Simulation Parameters and Morphologic Pattern Features

Author:

Smolle Josef1

Affiliation:

1. Department of Dermatology, University of Graz, Auenbruggerplatz 8, A‐8036 Graz, Austria

Abstract

Objective:To develop an interpretation procedure which estimates simulation parameters (tumour cell motility, tumour cell adhesion, autocrine and paracrine growth control, stroma destruction) of simulated patterns solely based on morphometric features of the morphologic pattern.Methods:A cellular automaton computer simulation program was developed which produces morphologic patterns by growth of a seed of tumour cells. At the beginning of each simulation run certain simulation parameters are assigned to the tumour cells. After the run has been completed, the resulting pattern is evaluated by a set of morphometric features. Simulation parameters and resulting morphometric features of 27,800 simulations were stored in a database and were used for the evaluation of potential relationships.Results:Correlation analysis showed highly significant correlations between morphometric features on the one hand and the preset simulation parameters (tumour cell motility, tumour cell adhesion, autocrine and paracrine growth control, stroma destruction) on the other. Correlation coefficients, however, varied from 0.72 to 0.99. When only one simulation parameter varied while all others were kept constant, morphometric features yielded a highly reliable estimate of the particular simulation parameter. When variability was extended to 4 simulation parameters, morphometric features were less effective in estimating the setting of the parameters. Though in all patterns tested several possible simulation parameter constellations could be ruled out, morphometric features were usually compatible with more than one set of simulation parameters thus preventing a straightforward interpretation.Conclusions:Though simulation parameters significantly and reproducibly influence the resulting morphologic pattern as characterized by morphometric features, estimates of the simulation parameters based on morphometric features yield equivocal results.

Publisher

Hindawi Limited

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An on-lattice agent-based Monte Carlo model simulating the growth kinetics of multicellular tumor spheroids;Physica Medica;2020-09

2. Machine Learning for In Silico Modeling of Tumor Growth;Lecture Notes in Computer Science;2016

3. Optimal chemotherapy schedules from tumor entropy;Computational and Applied Mathematics;2015-09-15

4. A multiple time-scale computational model of a tumor and its micro environment;Mathematical Biosciences and Engineering;2013

5. Cellular automata models of tumour natural shrinkage;Physica A: Statistical Mechanics and its Applications;2011-06

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