This poster is published under an open license
. Please read the disclaimer
for further details.
Head and neck, Nuclear medicine, PET-CT, Outcomes analysis
G. Feliciani, F. Fioroni, E. Grassi, M. Bertolini, A. Nitrosi, P. Ciammella, A. Versari, C. Iotti, M. Iori; Reggio Emilia/IT
Aims and objectives
•A phantom study to select reproducible textural features in 18F-FDG PET/CT image analysis.
•A clinical study where Texture analysis is employed in predicting outcomes of patient affected by Head and Neck Squamous Cell Carcinoma (HNSCC).
Introduction: It has been proven that tumors often show high heterogeneity on microscopic level. Standard clinical procedure for tumor heterogeneity scoring is biopsy but unfortunately this technique is highly invasive and sometimes requires a large specimen of the tumor itself.
Current medical imaging techniques cannot provide resolutions up to the microscopic scale but they can still furnish macroscopic information about the internal structure of the tumor with the benefit of total non-invasiveness.
In recent years Texture Analysis (TA) applied to CT,
MR and PET was widely employed to fulfill this task.
TA based methods are well suited for this purpose because they provide unique information about the spatial variation of pixel in the region where they are applied.
Features extracted with this methods were employed to improve clinicians knowledge on prognosis or therapy effect in oncologic patient.
In particular 18F-FDG PET imaging has the ability to visualize cellular metabolism and thus able to reveal tumor heterogeneity even if spatial resolution is poorer than other imaging techniques.
Many studies reported that TA is effective in producing promising predictors in the assessment of therapy response but there is still no multi-center agreement on the quantitative scoring of tumor heterogeneity.
Purpose of this study is to acquire PET images of a standard quality assurance phantom following different clinical protocols and assess their intrinsic variability with different acquisition times,
reconstruction filters and region of interest dimension.
Moreover scanners from different producers will be taken into account.
Finally preliminary result of a clinical study followed by our institution will be presented regarding therapy outcome prediction in patient with HNSCC.
Every texture analysis in this study was performed employing CGITA software.