Article (Scientific journals)
[(18)F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.
Da Silva Ferreira, Marta; LOVINFOSSE, Pierre; HERMESSE, Johanne et al.
2021In European Journal of Nuclear Medicine and Molecular Imaging
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Keywords :
Cervical cancer; Disease-free survival; Machine learning; Radiomics; [18F]FDG PET/CT
Abstract :
[en] 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 (LACC). METHODS: One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners. RESULTS: After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F(1)-score, precision and recall were respectively 0.78 (0.67-0.88), 0.49 (0.25-0.67), 0.42 (0.25-0.60) and 0.63 (0.20-0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set. CONCLUSION: [(18)F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient's outcome but remain subject to variability across PET/CT devices.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Da Silva Ferreira, Marta ;  Université de Liège - ULiège > GIGA CRC In vivo Imaging - Nuclear Medicine Division
LOVINFOSSE, Pierre ;  Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale > Service médical de médecine nucléaire et imagerie onco
HERMESSE, Johanne ;  Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale > Service médical de radiothérapie
DE CUYPERE, Marjolein ;  Centre Hospitalier Universitaire de Liège - CHU > Département de gynécologie-obstétrique > Secteur oncologie
Rousseau, Caroline
Lucia, François
Schick, Ulrike
Reinhold, Caroline
Robin, Philippe
Hatt, Mathieu
Visvikis, Dimitris
Bernard, Claire ;  Université de Liège - ULiège > Département de physique > Département de physique
Leijenaar, Ralph T. H.
Kridelka, Frédéric ;  Université de Liège - ULiège > Département des sciences cliniques > Gynécologie-Obstétrique
Lambin, Philippe
Meyer, Patrick ;  Université de Liège - ULiège > Département des sciences de la vie > Biologie des systèmes et bioinformatique
Hustinx, Roland  ;  Université de Liège - ULiège > Département des sciences cliniques > Médecine nucléaire
More authors (7 more) Less
Language :
English
Title :
[(18)F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.
Publication date :
2021
Journal title :
European Journal of Nuclear Medicine and Molecular Imaging
ISSN :
1619-7070
eISSN :
1619-7089
Publisher :
Springer, Germany
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 30 April 2021

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