Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date:

Abstract

PURPOSE: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans.

Authors

  • Abdalla Ibrahim
    The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht.
  • Akshayaa Vaidyanathan
    The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands. akshayaa.vaidyanathan@oncoradiomics.com.
  • Sergey Primakov
    The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht.
  • Flore Belmans
    Radiomics (Oncoradiomics SA), Liege, Belgium.
  • Fabio Bottari
    Radiomics (Oncoradiomics SA), Liège, Belgium.
  • Turkey Refaee
    The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands, t.refaee@maastrichtuniversity.nl.
  • Pierre Lovinfosse
    Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU of Liège, Liège, Belgium.
  • Alexandre Jadoul
    Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium.
  • Celine Derwael
    Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium.
  • Fabian Hertel
    Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.
  • Henry C Woodruff
    The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
  • Helle D Zacho
    Department of Nuclear Medicine, Clinical Cancer Research Centre, Aalborg University Hospital, Aalborg, Denmark.
  • Seán Walsh
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Wim Vos
    FLUIDDA nv, Kontich, Belgium.
  • Mariaelena Occhipinti
    Dept of Biomedical, Clinical and Experimental Sciences "Mario Serio", University of Florence, Florence, Italy.
  • François-Xavier Hanin
    Department of Nuclear Medicine, Universite´CatholiqueUniversite´Catholique de Louvain, CHU-UCL-Namur, Ottignies-Louvain-la-Neuve, Belgium.
  • Philippe Lambin
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Felix M Mottaghy
    Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany.
  • Roland Hustinx
    Nuclear Medicine Department, Centre Hospitalier Universitaire de Liège, Liège, Belgium.