Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).

Journal: PET clinics
Published Date: Jan 1, 2022

Abstract

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

Authors

  • Navid Hasani
    Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA.
  • Sriram S Paravastu
    Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Skeletal Disorders and Mineral Homeostasis Section, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIH), Bethesda, MD 20892, USA; School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA.
  • Faraz Farhadi
    Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD.
  • Fereshteh Yousefirizi
    Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada. Electronic address: frizi@bccrc.ca.
  • Michael A Morris
    Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Institute for Data Science, Department of Diagnostic Radiology and Nuclear Medicine - University of Miami Miller School of Medicine, Miami, FL, USA.
  • Arman Rahmim
  • Mark Roschewski
    Lymphoid Malignancies Branch, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Babak Saboury
    IBM Research, Almaden, San Jose, California.

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