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

Journal: Computers in biology and medicine
PMID: 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.

Authors

  • Francisco M Garcia-Moreno
    Department of Software Engineering, Computer Science School, University of Granada, 18014 Granada, Spain.
  • Jesús Ruiz-Espigares
    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.
  • Miguel A Gutiérrez-Naranjo
    Department of Computer Science and Artificial Intelligence, University of Seville, Seville, Spain. Electronic address: magutier@us.es.
  • Juan Antonio Marchal
    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.

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