Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.

Journal: European journal of radiology
Published Date: Nov 6, 2019

Abstract

PURPOSE: To evaluate the performance of machine learning (ML)-based computed tomography (CT) radiomics analysis for discriminating between low grade (WHO/ISUP I-II) and high grade (WHO/ISUP III-IV) clear cell renal cell carcinomas (ccRCCs).

Authors

  • Jun Shu
    Department of Radiology, Xijing Hospital, Fourth Military Medical University, Changle West Road 127#, Xi'an City, 710032, People's Republic of China.
  • Didi Wen
    Department of Radiology, Xijing Hospital, Fourth Military Medical University, Changle West Road 127#, Xi'an City, 710032, People's Republic of China.
  • Yibin Xi
    Department of Radiology, Xijing Hospital, Fourth Military Medical University, Changle West Road 127#, Xi'an City, 710032, People's Republic of China.
  • Yuwei Xia
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Zhengting Cai
    Huiying Medical Technology Co., Ltd., HaiDian District, Beijing City, 100192, People's Republic of China.
  • Wanni Xu
    Department of Pathology, Xijing Hospital, Fourth Military Medical University, Changle West Road 127#, Xi'an City, 710032, People's Republic of China; Deng Road 97#, Xi'an City, 710077, People's Republic of China.
  • Xiaoli Meng
    Department of Radiology, Xi'an XD Group Hospital, Shaanxi University of Chinese Medicine, Feng Deng Road 97#, Xi'an City, 710077, People's Republic of China.
  • Bao Liu
    Department of Radiology, Xijing Hospital, Fourth Military Medical University, Changle West Road 127#, Xi'an City, 710032, People's Republic of China.
  • Hong Yin
    Department of Mathematics, School of Information, Renmin University of China, No.59 Zhong Guan Cun Avenue, Hai Dian District, Beijing, 100872, China. yinxiaohong82@hotmail.com.

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