Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

Journal: The American Journal of dermatopathology
PMID: 40105309

Abstract

BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supported by immunohistochemical staining, is the gold standard for detecting LN metastases. However, the process is labor-intensive, requiring the analysis of multiple tissue levels, which increases both time and cost. With the growing integration of artificial intelligence (AI) into clinical workflows, there is potential to streamline this process, enhancing efficiency and accuracy.

Authors

  • Nada Shaker
    Department of Pathology, University of California San Francisco, UCSF, San Francisco, CA.
  • Sean Niu
    Department of Dermatology and Pathology, Wake Forest University, School of Medicine, Medical Center Boulevard, Winston-Salem, NC.
  • Heath Blankenship
    Department of Dermatology and Pathology, Wake Forest University, School of Medicine, Medical Center Boulevard, Winston-Salem, NC.
  • Nuha Shaker
    Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA; and.
  • Hossam Arafat
    Spatial X Diagnostics, London, United Kingdom.
  • Raed Sbenaty
    Spatial X Diagnostics, London, United Kingdom.
  • Ahmed Yones
    Spatial X Diagnostics, London, United Kingdom.
  • Mohammad Shaker
    Spatial X Diagnostics, London, United Kingdom.
  • Noor Shaker
    Spatial X Diagnostics, London, United Kingdom.
  • Omar P Sangueza
    Dermatopathology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.

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