Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes

PURPOSE: We developed a machine learning–based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes....

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Enregistré dans:
Détails bibliographiques
Publié dans:Eur J Nucl Med Mol Imaging
Auteurs principaux: Kim, Jun Pyo, Kim, Jeonghun, Kim, Yeshin, Moon, Seung Hwan, Park, Yu Hyun, Yoo, Sole, Jang, Hyemin, Kim, Hee Jin, Na, Duk L., Seo, Sang Won, Seong, Joon-Kyung
Format: Artigo
Langue:Inglês
Publié: Springer Berlin Heidelberg 2019
Sujets:
Original Article
Accès en ligne:https://ncbi.nlm.nih.gov/pmc/articles/PMC7299909/
https://ncbi.nlm.nih.gov/pubmed/31884562
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00259-019-04663-3
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