Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners
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Sylvain Reuzé1,2,3,4, Fanny Orlhac1,5, Cyrus Chargari1,2,3,6,7, Christophe Nioche5, Elaine Limkin1, François Riet3, Alexandre Escande3, Christine Haie-Meder3, Laurent Dercle8,9, Sébastien Gouy10, Irène Buvat5, Eric Deutsch1,2,3 and Charlotte Robert1,2,3,4
1INSERM, U1030, F-94805, Villejuif, France
2Université Paris-Sud, Université Paris-Saclay, F-94270, Le Kremlin-Bicêtre, France
3Gustave Roussy, Université Paris-Saclay, Department of Radiotherapy, F-94805, Villejuif, France
4Gustave Roussy, Université Paris-Saclay, Department of Medical Physics, F-94805, Villejuif, France
5IMIV, CEA, INSERM, CNRS, Université Paris-Sud, Université Paris-Saclay, CEA-SHFJ, Orsay, France
6French Military Health Services Academy, Ecole du Val-de-Grâce, Paris, France
7Institut de Recherche Biomédicale des Armées, Bretigny-sur-Orge, France
8INSERM, U1015, F-94805, Villejuif, France
9Gustave Roussy, Université Paris-Saclay, Department of Nuclear Medicine and Endocrine Oncology, F-94805, Villejuif, France
10Gustave Roussy, Université Paris-Saclay, Department of Gynecologic Surgery, F-94805, Villejuif, France
Charlotte Robert, email: firstname.lastname@example.org
Keywords: radiomics, cervical cancer, texture, PET imaging
Received: February 24, 2017 Accepted: April 11, 2017 Published: May 15, 2017
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.
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