Feasibility of radiomic feature harmonization for pooling of [18F]FET or [18F]GE-180 PET images of gliomas

Introduction: Large datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from di...

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Xehetasun bibliografikoak

Egile Nagusiak:	Adrian Jun Zounek, Nathalie Lisa Albert, Adrien Holzgreve, Marcus Unterrainer, Julia Brosch-Lenz, Simon Lindner, Andreas Bollenbacher, Guido Boening, Rainer Rupprecht, Matthias Brendel, Louisa von Baumgarten, Joerg-Christian Tonn, Peter Bartenstein, Sibylle Ziegler, Lena Kaiser
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Elsevier 2023-02-01
Saila:	Zeitschrift für Medizinische Physik
Gaiak:	Radiomics Robustness Data Pooling FET PET TSPO PET Glioma
Sarrera elektronikoa:	http://www.sciencedirect.com/science/article/pii/S0939388922001386
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