Artificial intelligence applications for oncological positron emission tomography imaging.

Journal: European journal of radiology
Published Date:

Abstract

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PET-based AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

Authors

  • Wanting Li
    Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China.
  • Haiyan Liu
    Department of Neurology, Xinyang Central Hospital, Xinyang 464000, China.
  • Feng Cheng
    Phase I Clinical Trial Site, Nanjing Gaoxin Hospital, Nanjing, Jiangsu, China.
  • Yanhua Li
    Department of Foreign Languages, Shanxi Medical University, Taiyuan, Shanxi, China.
  • Sijin Li
    Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China. Electronic address: lisjnm123@163.com.
  • Jiangwei Yan
    Shanxi Medical University, Taiyuan 030001, PR China. Electronic address: yanjw@sxmu.edu.cn.