Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images.

Journal: Academic radiology
Published Date:

Abstract

RATIONALE AND OBJECTIVES: To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell carcinomas (ccRCCs).

Authors

  • Xiaopeng He
    Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China; Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
  • Yi Wei
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
  • Hanmei Zhang
    Hospital for Skin Diseases and Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China.
  • Tong Zhang
    Beijing University of Chinese Medicine, Beijing, China.
  • Fang Yuan
    Department of Pharmacy The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Zixing Huang
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
  • Fugang Han
    Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China.
  • Bin Song
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.