Machine Learning-based Classification of Adrenal Tumors Using Clinical, Hormonal, and Body Composition Data.

Journal: European journal of endocrinology
Published Date: Jul 29, 2025

Abstract

OBJECTIVE: Accurate diagnosis of adrenal tumors, including mild autonomous cortisol secretion (MACS), adrenal Cushing's syndrome (ACS), primary aldosteronism (PA), pheochromocytoma (PCC), and nonfunctioning adrenal adenomas (NFA), is crucial but challenging. We aimed to develop a machine learning (ML)-based single-step diagnostic method for differentiating adrenal tumors by integrating clinical data; serum adrenal hormone profiles (SAP); and body composition data.

Authors

  • Seung Shin Park
    Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, 101 Dae-hak ro, Seoul 03080, Republic of Korea.
  • Jongsung Noh
    Center for Advanced Biomolecular Recognition, Korea Institute of Science and Technology, Seoul, Korea.
  • Jinhee Kim
    Lab of Cell Differentiation Research, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea.
  • Taesung Kim
    Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon, 34141, Korea.
  • Hae Jin Seo
    Center for Advanced Biomolecular Recognition, Korea Institute of Science and Technology, Seoul, Korea.
  • Chang Ho Ahn
    Lunit, Seoul, Korea (the Republic of).
  • Jaegul Choo
  • Man Ho Choi
    Center for Advanced Biomolecular Recognition, Korea Institute of Science and Technology, Seoul, Korea.
  • Jung Hee Kim
    Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.

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