[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation

Purpose To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[ 18 F] fluoro-2-deoxy-D-glucose ([ 18 F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	European journal of nuclear medicine and molecular imaging 2021-10, Vol.48 (11), p.3432-3443
Hauptverfasser:	Ferreira, Marta, Lovinfosse, Pierre, Hermesse, Johanne, Decuypere, Marjolein, Rousseau, Caroline, Lucia, François, Schick, Ulrike, Reinhold, Caroline, Robin, Philippe, Hatt, Mathieu, Visvikis, Dimitris, Bernard, Claire, Leijenaar, Ralph T. H., Kridelka, Frédéric, Lambin, Philippe, Meyer, Patrick E., Hustinx, Roland
Format:	Artikel
Sprache:	eng
Schlagworte:	[18F]FDG PET/CT Adaptive algorithms Advanced Image Analyses (Radiomics and Artificial Intelligence) Bayes Theorem Bayesian analysis Cancer Cardiology Cervical cancer Cervix Disease-Free Survival Feature extraction Female Fluorine isotopes Fluorodeoxyglucose F18 Glucose Human health sciences Humans Imaging Learning algorithms Life Sciences Liver Machine learning Mathematical models Medicine Medicine & Public Health Neoplasm Recurrence, Local Nuclear Medicine Oncology Original Original Article Orthopedics Patients Performance prediction Positron emission tomography Positron Emission Tomography Computed Tomography Radiologie, médecine & imagerie nucléaire Radiology Radiology, nuclear medicine & imaging Radiomics Radiopharmaceuticals Reproducibility of Results Retrospective Studies Scanners Sciences de la santé humaine Statistical models Tumors Uterine Cervical Neoplasms - diagnostic imaging
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!