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Oncotarget. 2017 Jun 27;8(26):43169-43179. doi: 10.18632/oncotarget.17856.

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

Author information

1
INSERM, U1030, F-94805, Villejuif, France.
2
Université Paris-Sud, Université Paris-Saclay, F-94270, Le Kremlin-Bicêtre, France.
3
Gustave Roussy, Université Paris-Saclay, Department of Radiotherapy, F-94805, Villejuif, France.
4
Gustave Roussy, Université Paris-Saclay, Department of Medical Physics, F-94805, Villejuif, France.
5
IMIV, CEA, INSERM, CNRS, Université Paris-Sud, Université Paris-Saclay, CEA-SHFJ, Orsay, France.
6
French Military Health Services Academy, Ecole du Val-de-Grâce, Paris, France.
7
Institut de Recherche Biomédicale des Armées, Bretigny-sur-Orge, France.
8
INSERM, U1015, F-94805, Villejuif, France.
9
Gustave Roussy, Université Paris-Saclay, Department of Nuclear Medicine and Endocrine Oncology, F-94805, Villejuif, France.
10
Gustave Roussy, Université Paris-Saclay, Department of Gynecologic Surgery, F-94805, Villejuif, France.

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

PMID:
28574816
PMCID:
PMC5522136
DOI:
10.18632/oncotarget.17856
[Indexed for MEDLINE]
Free PMC Article

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