Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images.

Journal: Ultrasound in medicine & biology
PMID: 39317624

Abstract

OBJECTIVE: Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade.

Authors

  • YouChang Yang
    Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
  • ZiYi Yuan
    Department of Radiology, Qilu Hospital of Shandong University, Jinan, China.
  • QingGuo Ren
    Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
  • Jiajia Wang
    Department of Obstetrics and Gynecology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
  • Shuai Guan
    Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
  • Xiaoqiang Tang
    Radiology Department, the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou 213164, Jiangsu, China.
  • QingJun Jiang
    Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, 266035, China.
  • Xiangshui Meng
    Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.

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