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Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners
Author(s) -
Sylvain Reuzé,
Fanny Orlhac,
Cyrus Chargari,
Christophe Nioche,
Elaine Johanna Limkin,
F.-G. Riet,
Alexandre Escande,
Christine Haie-Méder,
Laurent Dercle,
Sébastien Gouy,
Irène Buvat,
Éric Deutsch,
Charlotte Robert
Publication year - 2017
Publication title -
oncotarget
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.373
H-Index - 127
ISSN - 1949-2553
DOI - 10.18632/oncotarget.17856
Subject(s) - medicine , cervical cancer , positron emission tomography , radiomics , nuclear medicine , cancer , artificial intelligence , radiology , computer science
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.

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