Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach.

Journal: European radiology
Published Date: Feb 14, 2025

Abstract

OBJECTIVE: This study aims to develop and validate a predictive model for thyroid nodule malignancy risks using clinical and ultrasonography features and a machine learning (ML) approach.

Authors

  • Seyed Mahdi Hosseini Sarkhosh
    Department of Industrial Engineering, University of Garmsar, Garmsar, Iran. sm.hosseini@fmgarmsar.ac.ir.
  • Nooshin Shirzad
    Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Mahdieh Taghvaei
    Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Seyed Mohammad Tavangar
    Department of Pathology, Tehran University of Medical Sciences, Tehran, Iran.
  • Sara Farhat
    School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Hojat Ebrahiminik
    Department of Interventional Radiology and Radiation, Sciences Research Center, AJA University of Medical Sciences, Tehran, Iran. dr_ebrahiminik@ajaums.ac.ir.
  • Mahboobeh Hemmatabadi
    Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran. hemmatabadi55@yahoo.com.
  • Maryam Pourashraf
    Tirad Imaging Institute, Tehran, Iran.
  • Hossein Chegeni
    Fellowship of Interventional Radiology Imaging Center, IranMehr General Hospital, Iran.

Keywords

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