Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.

Journal: Journal of cancer research and clinical oncology
PMID: 37672075

Abstract

PURPOSE: There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC).

Authors

  • Haohua Yao
    Department of Urology, the First Affiliated Hospital, Sun Yat-sen University. Guangzhou, China.
  • Li Tian
    Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha 410013, China. tianlixy3@csu.edu.cn.
  • Xi Liu
    Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou Medical College, Soochow University, Suzhou, Jiangsu 215123, China.
  • Shurong Li
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Yuhang Chen
    School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China,People's Republic of China.
  • Jiazheng Cao
    Department of Urology, Jiangmen Central Hospital, Jiangmen, China.
  • Zhiling Zhang
    Department of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
  • Zhenhua Chen
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Zihao Feng
    College of Urban and Environmental Science, Northwest University, Xi'an 710127, China.
  • Quanhui Xu
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Jiangquan Zhu
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Yinghan Wang
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Yan Guo
    State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Caixia Li
    Department of Thyroid and Breast Surgery, Tianjin 4th Centre Hospital, Tianjin, China.
  • Peixing Li
    School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China.
  • Huanjun Wang
    Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. wanghj45@mail.sysu.edu.cn.
  • Junhang Luo
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China. luojunh@mail.sysu.edu.cn.

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