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

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 pre...

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Foilsithe in:Br J Radiol
Main Authors: Acar, Emine, Leblebici, Asım, Ellidokuz, Berat Ender, Başbınar, Yasemin, Kaya, Gamze Çapa
Formáid: Artigo
Teanga:Inglês
Foilsithe: The British Institute of Radiology. 2019
Ábhair:
Full Paper
Rochtain Ar Líne:https://ncbi.nlm.nih.gov/pmc/articles/PMC6732932/
https://ncbi.nlm.nih.gov/pubmed/31219712
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1259/bjr.20190286
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