Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real-world data and machine learning model algorithms.

Journal: Cancer cytopathology
PMID: 40289395

Abstract

BACKGROUND: Accurate diagnosis of neuroendocrine neoplasms (NENs) is challenging, especially in poorly differentiated neuroendocrine carcinomas (NECs). This study was aimed to search the best or best combination of neuroendocrine markers in the diagnostic workup of NENs via analysis of the real-world data and machine learning algorithms.

Authors

  • Haiming Tang
    Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.
  • Haoran Xia
    School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA.
  • Nanfei Sun
    Department of Management Information System, College of Business, University of Houston Clear Lake, Houston, Texas, USA.
  • Patricia V Hernandez
    Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Minhua Wang
    Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.
  • Adebowale J Adeniran
    Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.
  • Guoping Cai
    Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.

Keywords

External Resources

Popular Topics
Recent Journals