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Authors: Abdoulaye Sarr 1 ; Petra Miglierini 2 ; Alexandra Fronville 1 and Vincent Rodin 1

Affiliations: 1 Université de Brest, UMR CNRS 6285, Lab-STICC, CID and IHSEV, France ; 2 University Hospital Morvan and Institute of Oncology and Haematology, France

ISBN: 978-989-758-170-0

Keyword(s): Morphogenesis, Tissue Prediction, Tumour Classification, Viability Algorithms.

Related Ontology Subjects/Areas/Topics: Agents ; Algorithms and Software Tools ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Methodologies and Technologies ; Operational Research ; Simulation ; Soft Computing ; Structure Prediction ; Systems Biology

Abstract: Due to the availability of large amount of medical data and the improvements of computers’ capacities, an increase of tools for medical applications has been noted. In the case of cancer, this results in some application and treatment successes in radiotherapy. However, on the one hand, high therapeutic results are yet to be seen, and on the other hand, unpleasant side effects are still widely observed. In the first case, it may arise from the avoidance of any damage to healthy structures implying ineffective treatment, and in the second case it may be, due to lethal doses deposited in the tumour, leading to an unacceptable damage to one or more healthy structures. Thus, it would be useful to simulate the effects of any treatment prior to its application. Thereby, we are focusing on the proposition of computational methods serving to give insights for decisions aid tools in radiotherapy. In this paper, we provide algorithms for tissue growth prediction where cells are elements of a 2D cellular automaton oriented multi-agent system. Then, we propose a novel method to predict and characterize the evolution of a pathological tissue under cells irradiation. We show that the more cells destroyed during the radiotherapy are linked to aggressive cancer cells, the more the treatment lead to an impaired result in terms of growth. By contrast, we highlight that there exists cells less linked to these aggressive cancer cells that are more suitable to target for an effective and efficient radiotherapy. Based on the dominant cells (linked or not linked to aggressive cancer cells), we introduce a novel method to classify tumours. (More)

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Paper citation in several formats:
Sarr, A.; Miglierini, P.; Fronville, A. and Rodin, V. (2016). Directional Cellular Dynamics for Tissue Morphogenesis and Tumour Characterization by Aggressive Cancer Cells Identification.In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 290-295. DOI: 10.5220/0005830702900295

@conference{bioinformatics16,
author={Abdoulaye Sarr. and Petra Miglierini. and Alexandra Fronville. and Vincent Rodin.},
title={Directional Cellular Dynamics for Tissue Morphogenesis and Tumour Characterization by Aggressive Cancer Cells Identification},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={290-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005830702900295},
isbn={978-989-758-170-0},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - Directional Cellular Dynamics for Tissue Morphogenesis and Tumour Characterization by Aggressive Cancer Cells Identification
SN - 978-989-758-170-0
AU - Sarr, A.
AU - Miglierini, P.
AU - Fronville, A.
AU - Rodin, V.
PY - 2016
SP - 290
EP - 295
DO - 10.5220/0005830702900295

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