Prediction of molecular subclasses of uveal melanoma by deep learning using routine haematoxylin-eosin-stained tissue slides.

Journal: Histopathology
PMID: 38952117

Abstract

AIMS: Uveal melanoma has a high propensity to metastasize. Prognosis is associated with specific driver mutations and copy number variations, and these can only be obtained after genetic testing. In this study we evaluated the efficacy of patient outcome prediction using deep learning on haematoxylin and eosin (HE)-stained primary uveal melanoma slides in comparison to molecular testing.

Authors

  • Farhan Akram
    Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Daniël P de Bruyn
    Ophthalmology, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Quincy C C van den Bosch
    Ophthalmology, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Teodora E Trandafir
    Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Thierry P P van den Bosch
    Department of Pathology and Clinical Bioinformatics, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Rob M Verdijk
    Department of Pathology and Clinical Bioinformatics, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Annelies de Klein
    Ophthalmology, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Emine Kiliç
    Ophthalmology, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Andrew P Stubbs
    Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Erwin Brosens
    Clinical Genetics, Erasmus MC Rotterdam, Rotterdam, the Netherlands.
  • Jan H von der Thüsen
    Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands. Electronic address: j.vonderthusen@erasmusmc.nl.

Keywords

External Resources

Popular Topics
Recent Journals