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, vol.92, no.1101, 2019 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 92 Issue: 1101
  • Publication Date: 2019
  • Doi Number: 10.1259/bjr.20190286
  • Journal Name: BRITISH JOURNAL OF RADIOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Dokuz Eylül University Affiliated: Yes

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.