Using deep learning for predicting the dynamic evolution of breast cancer migration.

Autor: Garcia-Moreno FM; Department of Software Engineering, Computer Science School, University of Granada, C/ Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain; Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain. Electronic address: fmgarmor@ugr.es., Ruiz-Espigares J; Biopathology and Regenerative Medicine Institute (IBIMER), Centre for Biomedical Research (CIBM), University of Granada, Granada, E-18016, Spain; Excellence Research Unit 'Modeling Nature' (MNat), University of Granada, Granada, 18016, Spain; Department of Human Anatomy and Embryology, Faculty of Medicine, University of Granada, Granada, E-18016, Spain; Biosanitary Research Institute of Granada (ibs.GRANADA), University Hospitals of Granada-University of Granada, Granada, E-18071, Spain., Gutiérrez-Naranjo MA; Department of Computer Sciences and Artificial Intelligence, University of Sevilla, Avda. Reina Mercedes, s/n, Sevilla, 41012, Spain., Marchal JA; Biopathology and Regenerative Medicine Institute (IBIMER), Centre for Biomedical Research (CIBM), University of Granada, Granada, E-18016, Spain; Excellence Research Unit 'Modeling Nature' (MNat), University of Granada, Granada, 18016, Spain; Department of Human Anatomy and Embryology, Faculty of Medicine, University of Granada, Granada, E-18016, Spain; Biosanitary Research Institute of Granada (ibs.GRANADA), University Hospitals of Granada-University of Granada, Granada, E-18071, Spain.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Sep; Vol. 180, pp. 108890. Date of Electronic Publication: 2024 Jul 27.
DOI: 10.1016/j.compbiomed.2024.108890
Abstrakt: Background: Breast cancer (BC) remains a prevalent health concern, with metastasis as the main driver of mortality. A detailed understanding of metastatic processes, particularly cell migration, is fundamental to improve therapeutic strategies. The wound healing assay, a traditional two-dimensional (2D) model, offers insights into cell migration but presents scalability issues due to data scarcity, arising from its manual and labor-intensive nature.
Method: To overcome these limitations, this study introduces the Prediction Wound Progression Framework (PWPF), an innovative approach utilizing Deep Learning (DL) and artificial data generation. The PWPF comprises a DL model initially trained on artificial data that simulates wound healing in MCF-7 BC cell monolayers and spheres, which is subsequently fine-tuned on real-world data.
Results: Our results underscore the model's effectiveness in analyzing and predicting cell migration dynamics within the wound healing context, thus enhancing the usability of 2D models. The PWPF significantly contributes to a better understanding of cell migration processes in BC and expands the possibilities for research into wound healing mechanisms.
Conclusions: These advancements in automated cell migration analysis hold the potential for more comprehensive and scalable studies in the future. Our dataset, models, and code are publicly available at https://github.com/frangam/wound-healing.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Juan Antonio Marchal reports financial support was provided by University of Granada. Miguel Angel Guterrez-Naranjo reports financial support was provided by University of Seville. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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