2.5D Deep Learning-Based Prediction of Pathological Grading of Clear Cell Renal Cell Carcinoma Using Contrast-Enhanced CT: A Multicenter Study.

Journal: Academic radiology
Published Date:

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a deep learning model based on arterial phase-enhanced CT for predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).

Authors

  • Zi Yang
  • Haitao Jiang
    University of Science and Technology of China, No.96, JinZhai Road Baohe District,Hefei, Anhui 230026, PR China.
  • Shuai Shan
    Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210000, China (S.S., Y.Z.).
  • Xu Wang
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907.
  • Quanming Kou
    School of Automation, Hangzhou Dianzi University, Hangzhou 310018, P.R. China (Z.Y., Q.K., Y.Z.).
  • Chao Wang
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Pengfei Jin
    College of Environment and Plant Protection, Hainan University, Haikou 570228, China.
  • Yuyun Xu
  • Xiaohui Liu
    Science and Technology on Parallel and Distributed Laboratory, Changsha, China.
  • Yudong Zhang
    School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
  • Yuqing Zhang
    Division of Rheumatology, Allergy, and Immunology, Harvard Medical School, 2348Massachusetts General Hospital, Boston, MA, USA.

Keywords

No keywords available for this article.