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Using deep learning for predicting the dynamic evolution of breast cancer migration.
Garcia-Moreno, Francisco M; Ruiz-Espigares, Jesús; Gutiérrez-Naranjo, Miguel A; Marchal, Juan Antonio.
Afiliación
  • 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 Me
  • 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 Me
Comput Biol Med ; 180: 108890, 2024 Sep.
Article en En | MEDLINE | ID: mdl-39068903
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Movimiento Celular / Aprendizaje Profundo Límite: Female / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Movimiento Celular / Aprendizaje Profundo Límite: Female / Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos