A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.

Journal: Hormones (Athens, Greece)
Published Date: Jun 30, 2025

Abstract

AIMS: Adrenal incidentalomas (AIs) encompass a wide range of clinical entities, from incidental benign neoplasms that need to be monitored to aggressive malignancies requiring urgent medical intervention and treatment. The incidence of adrenal tumors is steadily rising, reflecting a growing trend in their prevalence and highlighting the necessity for heightened awareness and advanced diagnostic strategies to address this escalating health concern. This retrospective study was undertaken in order to explore the possibility of developing a decision support system for classifying adrenal tumors as benign and malignant or suspicious for malignancy.

Authors

  • Dimitrios A Binas
    Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece. d.binas@biomed.ntua.gr.
  • Grigoria Betsi
    Endocrinology and Diabetes Clinic, University of Crete School of Medicine, University General Hospital of Heraklion Crete, Iraklio, Greece.
  • Theodore Economopoulos
    Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece.
  • Chrysoula Mytareli
    First Department of Internal Medicine, Center of Excellence for Rare Endocrine Diseases, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece.
  • Charis Bourgioti
    First Department of Radiology ('Aretaieio' Hospital), Medical School, National and Kapodistrian University of Athens, 76 V. Sofias 11523, Athens, Greece.
  • Paraskevi Xekouki
    Endocrinology and Diabetes Clinic, University of Crete School of Medicine, University General Hospital of Heraklion Crete, Iraklio, Greece.
  • Anna Angelousi
    First Department of Internal Medicine, Center of Excellence for Rare Endocrine Diseases, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece.
  • George K Matsopoulos
    Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.

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