Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.

Objectives: This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk...

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Publicado en:	European Radiology Vol. 32; no. 10; pp. 7056 - 7068
Autores principales:	Zhao, Meixin, Kluge, Kilian, Papp, Laszlo, Grahovac, Marko, Yang, Shaomin, Jiang, Chunting, Krajnc, Denis, Spielvogel, Clemens P., Ecsedi, Boglarka, Haug, Alexander, Wang, Shiwei, Hacker, Marcus, Zhang, Weifang, Li, Xiang
Formato:	pictorial research tables/charts Journal Article
Publicado:	Springer Nature Oct2022
Acceso en línea:	Ver este registro en EBSCOhost