Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.

Journal: The British journal of radiology
PMID: 31219712

Abstract

OBJECTIVE: Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated.

Authors

  • Emine Acar
    1Department of Nuclear Medicine, Ataturk Training and Research Hospital, İzmir Kâtip Celebi University, Izmir, Turkey.
  • Asım Leblebici
    2Department of Translational Oncology, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.
  • Berat Ender Ellidokuz
    3Department of Gastroenterology,Faculty of Medicine, Dokuz Eylul University, Izmır, Turkey.
  • Yasemin Başbınar
    4Department of Translational Oncology, Institute of Oncology, Dokuz Eylul University, Izmir, Turkey.
  • Gamze Çapa Kaya
    6Department of Nuclear Medicine, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey.

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