Diagnostic Performance of Machine Learning Models Based on (18)F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules

OBJECTIVES: This study aimed to evaluate the ability of (18)fluorine-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN). METHODS...

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Detalles Bibliográficos
Autores principales: Salihoğlu, Yavuz Sami, Uslu Erdemir, Rabiye, Aydur Püren, Büşra, Özdemir, Semra, Uyulan, Çağlar, Ergüzel, Türker Tekin, Tekin, Hüseyin Ozan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Galenos Publishing 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246312/
https://www.ncbi.nlm.nih.gov/pubmed/35770958
http://dx.doi.org/10.4274/mirt.galenos.2021.43760

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9246312/
https://www.ncbi.nlm.nih.gov/pubmed/35770958
http://dx.doi.org/10.4274/mirt.galenos.2021.43760

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