Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on ¹⁸F-FDG PET.

ABSTRACT: Background: The characteristic magnetic resonance imaging (MRI) and the positron emission tomography (PET) findings of PCNSL often overlap with other intracranial tumors, making definitive diagnosis challenging. PCNSL typically shows iso-hypointense to grey matter on T2-weighted imaging. However, a particular part of PCNSL can demonstrate T2-weighted hyperintensity as other intracranial tumors. Moreover, normal high uptake of FDG in the basal ganglia, thalamus, and grey matter can mask underlying PCNSL in ¹⁸F-FDG PET. In order to promote the efficiency of diagnosis, the MRI-based or PET/CT-based radiomics models combining histograms with texture features in diagnosing glioma and brain metastases have been widely established. However, the diagnosing model for PCNSL has not

SUBMITTER: Cui C 

PROVIDER: S-EPMC10056979 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature