As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Nowadays, the quantitative analysis of PET/CT data in patients with glioblastoma is not strictly standardized in the clinic and does not exclude the human factor. This study aimed to evaluate the relationship between the radiomic features of glioblastoma 11C-methionine PET images and the tumor-to-normal brain (T/N) ratio determined by radiologists in clinical routine. PET/CT data were obtained for 40 patients (mean age 55 ± 12 years; 77.5% men) with a histologically confirmed diagnosis of glioblastoma. Radiomic features were calculated for the whole brain and tumor-containing regions of interest using the RIA package for R. We redesigned the original RIA functions for GLCM and GLRLM calculation to reduce computation time significantly. Machine learning over radiomic features was applied to predict T/N with the best median correlation between the true and predicted values of 0.73 (p = 0.01). The present study showed a reproducible linear relationship between 11C-methionine PET radiomic features and a T/N indicator routinely assessed in brain tumors. Radiomics enabled utilizing texture properties of PET/CT neuroimaging that may reflect the biological activity of glioblastoma and can potentially augment the radiological assessment.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.