Machine learning-derived clinical decision algorithm for the diagnosis of hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism.

Journal: European radiology
PMID:

Abstract

PURPOSE: To train and validate machine learning-derived clinical decision algorithm (CDA) for the diagnosis of hyperfunctioning parathyroid glands using preoperative variables to facilitate surgical planning.

Authors

  • Randy Yeh
    Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • Jennifer H Kuo
    Division of GI/Endocrine Surgery, Department of Surgery, College of Physicians & Surgeons, Columbia University, New York, NY, USA.
  • Bernice Huang
    Division of GI/Endocrine Surgery, Department of Surgery, College of Physicians & Surgeons, Columbia University, New York, NY, USA.
  • Parnian Shobeiri
    School of Medicine, Tehran University of Medical Science, Tehran, Iran.
  • James A Lee
    Endocrine Surgery, Columbia University Irving Medical Center, 161 Fort Washington Avenue, New York, NY 10032, USA. Electronic address: jal74@cumc.columbia.edu.
  • Yu-Kwang Donovan Tay
    Division of Endocrinology, Department of Medicine, College of Physicians & Surgeons, Columbia University, New York, NY, USA.
  • Gaia Tabacco
    Division of Endocrinology, Department of Medicine, College of Physicians & Surgeons, Columbia University, New York, NY, USA.
  • John P Bilezikian
    Division of Endocrinology, Department of Medicine, College of Physicians & Surgeons, Columbia University, New York, NY, USA.
  • Laurent Dercle
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032; Gustave Roussy, Université Paris-Saclay, Université Paris-Saclay, Département D'imagerie Médicale, Villejuif, France.