NDLI: Weighted quantification of $^{18}F-FDG$ tumor metabolism activity using fuzzy-thresholding to predict post-treatment tumor recurrence

Content Provider	IEEE Xplore Digital Library
Author	Roman-Jimenez, G. Acosta, O. Leseur, J. Devillers, A. Le Gouestre, J. Ospina, J.-D. Simon, A. Terve, P. De Crevoisier, R.
Copyright Year	2015
Description	Author affiliation: U1099, INSERM, Rennes, France (Roman-Jimenez, G.; Acosta, O.; Ospina, J.-D.; Simon, A.; De Crevoisier, R.) \|\| Dept. of Radiotherapy, Centre Eugne Marquis, Rennes, France (Leseur, J.; Devillers, A.) \|\| Keosys Med. Imaging, St. Herblain, France (Le Gouestre, J.; Terve, P.)
Abstract	Cervical cancer is one of the most common cancer to affect women worldwide. Despite the efficiency of radiotherapy treatment, some patients present post-treatment tumor recurrence which increases the risk of death. Early outcome prediction could help oncologists to adapt the treatment. Several studies suggest that quantification of tumor activity using $^{18}FFDG$ PET imaging could be used to predict post-treatment tumor recurrence. In this paper we study the predictive value of weighted quantification of tumor metabolism extracted by fuzzy-thresholding for tumor recurrence of locally advanced cervical cancer. Fifty-three patients with locally advanced cervical cancer treated by chemo-radiotherapy were considered in our study. For each patient, a coregistered $^{18}F-FDG$ PET/CT scan was acquired before the treatment and was segmented using different hard and fuzzy segmentations methods. The tumor activity was extracted through the total lesion glycolysis and through a weighted analog of the total lesion glycolysis using the probability maps provided by the fuzzy segmentations. Outcomes prediction was performed using the area under the receiver operating characteristic curve (AUC) and the Harrell's C-index. Results suggest that weighted quantification of tumor activity seems to be strongly informative and could be used to predict post-treatment tumor recurrence in cervical cancer.
Starting Page	2239
Ending Page	2242
File Size	765299
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318837
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Positron emission tomography Cervical cancer Feature extraction Biochemistry Lesions
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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