OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data.

Journal: Cellular oncology (Dordrecht, Netherlands)
PMID: 38805131

Abstract

PURPOSE: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based models in combination with accurate real-time monitoring are required.

Authors

  • Nathalia Ferreira
    Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany.
  • Ajinkya Kulkarni
    Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany.
  • David Agorku
    Miltenyi Biotec B.V. & Co. KG, Bergisch Gladbach, Germany.
  • Teona Midelashvili
    Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075, Göttingen, Germany.
  • Olaf Hardt
    Miltenyi Biotec B.V. & Co. KG, Bergisch Gladbach, Germany.
  • Tobias J Legler
    Department of Transfusion Medicine, University Medical Center Göttingen, Göttingen, Germany.
  • Philipp Ströbel
    Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany.
  • Lena-Christin Conradi
    Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075, Göttingen, Germany.
  • Frauke Alves
    Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany.
  • Fernanda Ramos-Gomes
    Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany.
  • M Andrea Markus
    Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany. markus@mpinat.mpg.de.

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