Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.

Journal: World journal of urology
Published Date: Mar 21, 2024

Abstract

PURPOSE: To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC).

Authors

  • Yun Bai
    National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China. Electronic address: yunbai@foxmail.com.
  • Zi-Chen An
    Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China.
  • Fan Li
    Department of Instrument Science and Engineering, School of SEIEE, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Lian-Fang Du
    Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China.
  • Tian-Wu Xie
    Institute of Radiation Medicine, Fudan University, No. 2094 Xietu Road, Shanghai, 200032, China. tianwuxie@fudan.edu.cn.
  • Xi-Peng Zhang
    School of Computer Science and Technology, Taiyuan Normal University, No. 319 Daxue Street, Taiyuan, 030619, China. zxp_2576@163.com.
  • Ying-Yu Cai
    Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85/86 Wujin Road, Shanghai, 200080, China. yingyu.cai@shgh.cn.

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