A computational model for the cancer field effect

Bibliographic Details
Title: A computational model for the cancer field effect
Authors: Karl Deutscher, Thomas Hillen, Jay Newby
Source: Frontiers in Artificial Intelligence, Vol 6 (2023)
Publisher Information: Frontiers Media S.A., 2023.
Publication Year: 2023
Collection: LCC:Electronic computers. Computer science
Subject Terms: cancer field effect, field cancerization, carcinogenesis, head and neck squamous cell carcinoma, computational modeling, hybrid cellular automaton, Electronic computers. Computer science, QA75.5-76.95
More Details: IntroductionThe Cancer Field Effect describes an area of pre-cancerous cells that results from continued exposure to carcinogens. Cells in the cancer field can easily develop into cancer. Removal of the main tumor mass might leave the cancer field behind, increasing risk of recurrence.MethodsThe model we propose for the cancer field effect is a hybrid cellular automaton (CA), which includes a multi-layer perceptron (MLP) to compute the effects of the carcinogens on the gene expression of the genes related to cancer development. We use carcinogen interactions that are typically associated with smoking and alcohol consumption and their effect on cancer fields of the tongue.ResultsUsing simulations we support the understanding that tobacco smoking is a potent carcinogen, which can be reinforced by alcohol consumption. The effect of alcohol alone is significantly less than the effect of tobacco. We further observe that pairing tumor excision with field removal delays recurrence compared to tumor excision alone. We track cell lineages and find that, in most cases, a polyclonal field develops, where the number of distinct cell lineages decreases over time as some lineages become dominant over others. Finally, we find tumor masses rarely form via monoclonal origin.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2624-8212
Relation: https://www.frontiersin.org/articles/10.3389/frai.2023.1060879/full; https://doaj.org/toc/2624-8212
DOI: 10.3389/frai.2023.1060879
Access URL: https://doaj.org/article/91daa4c2eb1445d1b8b10c5a56b0b8c4
Accession Number: edsdoj.91daa4c2eb1445d1b8b10c5a56b0b8c4
Database: Directory of Open Access Journals
More Details
ISSN:26248212
DOI:10.3389/frai.2023.1060879
Published in:Frontiers in Artificial Intelligence
Language:English