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

• Published in: European journal of nuclear medicine and molecular imaging Vol. 48; no. 11; pp. 3432 - 3443
• Main Authors: 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: Journal Article Web Resource
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021; Springer Nature B.V; Springer Verlag (Germany) [1976-....]; Springer
• Subjects: [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; F]FDG PET/CT; [; Cervical cancer; Disease-free survival; Machine learning; Radiomics; [18F]FDG PET/CT

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...