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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 26.03.2021 | Original Article

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

verfasst von: Marta Ferreira, Pierre Lovinfosse, Johanne Hermesse, Marjolein Decuypere, Caroline Rousseau, François Lucia, Ulrike Schick, Caroline Reinhold, Philippe Robin, Mathieu Hatt, Dimitris Visvikis, Claire Bernard, Ralph T. H. Leijenaar, Frédéric Kridelka, Philippe Lambin, Patrick E. Meyer, Roland Hustinx

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 11/2021

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Abstract

Purpose

To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[¹⁸F] fluoro-2-deoxy-D-glucose ([¹⁸F]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), F₁-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

[¹⁸F]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.

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Titel: [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation
verfasst von: Marta Ferreira
Pierre Lovinfosse
Johanne Hermesse
Marjolein Decuypere
Caroline Rousseau
François Lucia
Ulrike Schick
Caroline Reinhold
Philippe Robin
Mathieu Hatt
Dimitris Visvikis
Claire Bernard
Ralph T. H. Leijenaar
Frédéric Kridelka
Philippe Lambin
Patrick E. Meyer
Roland Hustinx
Publikationsdatum: 26.03.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 11/2021
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05303-5

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

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

Abstract

Purpose

Methods

Results

Conclusion

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

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