Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

Journal: Arthritis & rheumatology (Hoboken, N.J.)
PMID:

Abstract

OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features.

Authors

  • Shengfa Li
    Zhujiang Hospital of Southern Medical University, Guangzhou, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, The Second Affiliated Chengdu Hospital of Chongqing Medical University, Chengdu, China.
  • Peihua Cao
    Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Jia Li
    Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Guangdong, 528400, PR China; School of Pharmacy, Zunyi Medical University, Zunyi, 563000, PR China; National Center for Drug Screening, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, PR China.
  • Tianyu Chen
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
  • Ping Luo
  • Guangfeng Ruan
    Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Xiaoshuai Wang
    Department of Bone Tumor Surgery, Orthopedics Center, the First Affiliated Hospital of Xinjiang Medical University, Urumchi Xinjiang, 830054, P.R.China.
  • Weiyu Han
    Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Zhaohua Zhu
    Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Qin Dang
    Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Qianyi Wang
    Hangzhou Hikvision Digital Technology Co., Ltd, Hangzhou, China.
  • Mengdi Zhang
    State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P. O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P. R. China.
  • Qiushun Bai
    Southern Medical University, Guangzhou, China.
  • Zhiyi Chai
    Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Hao Yang
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China.
  • Haowei Chen
    Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Mingze Tang
    School of Mechanical and Materials Engineering, North China University of Technology, Beijing, People's Republic of China.
  • Arafat Akbar
    Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Alexander Tack
    Therapy Planning Group, Zuse Institute Berlin, Berlin, Germany. Electronic address: tack@zib.de.
  • David J Hunter
    Royal North Shore Hospital and University of Sydney, Sydney, New South Wales, Australia.
  • Changhai Ding
    Zhujiang Hospital of Southern Medical University; Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China; and University of Tasmania, Hobart, Tasmania, Australia.