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09.12.2017 | Original Article | Ausgabe 5/2018

European Journal of Nuclear Medicine and Molecular Imaging 5/2018

Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy

European Journal of Nuclear Medicine and Molecular Imaging > Ausgabe 5/2018
François Lucia, Dimitris Visvikis, Marie-Charlotte Desseroit, Omar Miranda, Jean-Pierre Malhaire, Philippe Robin, Olivier Pradier, Mathieu Hatt, Ulrike Schick
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s00259-017-3898-7) contains supplementary material, which is available to authorized users.
Mathieu Hatt and Ulrike Schick contributed equally to this work.



The aim of this study is to determine if radiomics features from 18fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer.


One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control.


In the training cohort, median follow-up was 3.0 years (range, 0.43–6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I–II vs. III–IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non UniformityGLRLM in PET and EntropyGLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50–60% for clinical parameters).


In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.

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