Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients.

Purpose: To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (CT) (18F-FDG-PET/CT). Methods: This retrospective study included 50 cervi...

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Publicado en:	Abdominal Radiology Vol. 47; no. 2; pp. 838 - 848
Autores principales:	Nakajo, Masatoyo, Jinguji, Megumi, Tani, Atsushi, Yano, Erina, Hoo, Chin Khang, Hirahara, Daisuke, Togami, Shinichi, Kobayashi, Hiroaki, Yoshiura, Takashi
Formato:	diagnostic images research tables/charts Journal Article
Publicado:	Springer Nature Feb2022
Acceso en línea:	Ver este registro en EBSCOhost