Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

Journal: European radiology
PMID:

Abstract

OBJECTIVE: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC).

Authors

  • Ruimeng Yang
    Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
  • Jialiang Wu
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, Guangdong, China.
  • Lei Sun
    1Department of Biological Engineering, Utah State University, 4105 Old Main Hill, Logan, UT 84322-4105 USA.
  • Shengsheng Lai
    Department of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, 510520, Guangdong, China.
  • Yikai Xu
  • Xilong Liu
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Ying Ma
    Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, Guangdong, China.
  • Xin Zhen
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.