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

Acar E., Leblebici A., Ellidokuz B. E., BAŞBINAR Y., Kaya G. C.

BRITISH JOURNAL OF RADIOLOGY, cilt.92, sa.1101, 2019 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 92 Sayı: 1101
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1259/bjr.20190286
  • Dergi Adı: BRITISH JOURNAL OF RADIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.