Machine Learning Model with Computed Tomography Radiomics and Clinicobiochemical Characteristics Predict the Subtypes of Patients with Primary Aldosteronism.

Journal: Academic radiology
PMID: 38042624

Abstract

RATIONALE AND OBJECTIVES: Adrenal venous sampling (AVS) is the primary method for differentiating between primary aldosterone (PA) subtypes. The aim of study is to develop prediction models for subtyping of patients with PA using computed tomography (CT) radiomics and clinicobiochemical characteristics associated with PA.

Authors

  • Po-Ting Chen
    Department of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
  • Pei-Yan Li
    Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan (P.T.C, P.Y.L., C.M.C.).
  • Kao-Lang Liu
    Department of Medical Imaging, National Taiwan University Cancer Center, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Vin-Cent Wu
    Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan (V.C.W.).
  • Yen-Hung Lin
    Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan (Y.H.L.).
  • Jeff S Chueh
    Department of Urology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan (J.S.C.).
  • Chung-Ming Chen
    Institute of Biomedical Engineering, National Taiwan University, Taipei 100, Taiwan.
  • Chin-Chen Chang
    Department of Computer Science and Information Engineering, National United University, Miaoli 360, Taiwan.

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