Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners

Sylvain Reuzé, Fanny Orlhac, Cyrus Chargari, Christophe Nioche, Elaine Limkin, François Riet, Alexandre Escande, Christine Haie-Meder, Laurent Dercle, Sébastien Gouy, Irène Buvat, Eric Deutsch, Charlotte Robert, Sylvain Reuzé, Fanny Orlhac, Cyrus Chargari, Christophe Nioche, Elaine Limkin, François Riet, Alexandre Escande, Christine Haie-Meder, Laurent Dercle, Sébastien Gouy, Irène Buvat, Eric Deutsch, Charlotte Robert

Abstract

Objectives: To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study.

Methods: 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values.

Results: Eight features were statistically significant predictors of local recurrence in G1 (p < 0.05). The multivariate signature trained in G2 was validated in G1 (AUC=0.76, p<0.001) and identified local recurrence more accurately than SUVmax (p=0.022). Four features were significantly different between G1 and G2 in the liver. Spatial resampling was not sufficient to explain the stratification effect.

Conclusion: This study showed that radiomic features could predict local recurrence of LACC better than SUVmax. Further investigation is needed before applying a model designed using data from one PET scanner to another.

Keywords: PET imaging; cervical cancer; radiomics; texture.

Conflict of interest statement

CONFLICTS OF INTEREST

None.

Figures

Figure 1. Multivariate analysis using G1 for…
Figure 1. Multivariate analysis using G1 for training, G2 for validation (left) and G2 for training, G1 for validation (right)
ROC curves of SUVmax are also presented.
Figure 2. G1 vs. G2 in VOI-L…
Figure 2. G1 vs. G2 in VOI-L for the 4 features that were significanly different between groups (original images) (*: 0.01

Figure 3. Radiomic feature extraction pipeline
Figure 3. Radiomic feature extraction pipeline

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