Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients.
Robin GutscheJürgen ScheinsMartin KocherKhaled BousabarahGereon R FinkNadim Joni ShahKarl-Josef LangenNorbert GalldiksPhilipp LohmannPublished in: Cancers (2021)
Amino acid PET using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has attracted considerable interest in neurooncology. Furthermore, initial studies suggested the additional diagnostic value of FET PET radiomics in brain tumor patient management. However, the conclusiveness of radiomics models strongly depends on feature generalizability. We here evaluated the repeatability of feature-based FET PET radiomics. A test-retest analysis based on equivalent but statistically independent subsamples of FET PET images was performed in 50 newly diagnosed and histomolecularly characterized glioma patients. A total of 1,302 radiomics features were calculated from semi-automatically segmented tumor volumes-of-interest (VOIs). Furthermore, to investigate the influence of the spatial resolution of PET on repeatability, spherical VOIs of different sizes were positioned in the tumor and healthy brain tissue. Feature repeatability was assessed by calculating the intraclass correlation coefficient (ICC). To further investigate the influence of the isocitrate dehydrogenase (IDH) genotype on feature repeatability, a hierarchical cluster analysis was performed. For tumor VOIs, 73% of first-order features and 71% of features extracted from the gray level co-occurrence matrix showed high repeatability (ICC 95% confidence interval, 0.91-1.00). In the largest spherical tumor VOIs, 67% of features showed high repeatability, significantly decreasing towards smaller VOIs. The IDH genotype did not affect feature repeatability. Based on 297 repeatable features, two clusters were identified separating patients with IDH-wildtype glioma from those with an IDH mutation. Our results suggest that robust features can be obtained from routinely acquired FET PET scans, which are valuable for further standardization of radiomics analyses in neurooncology.
Keyphrases
- positron emission tomography
- newly diagnosed
- computed tomography
- pet ct
- deep learning
- machine learning
- pet imaging
- lymph node metastasis
- end stage renal disease
- contrast enhanced
- low grade
- ejection fraction
- chronic kidney disease
- prognostic factors
- wild type
- squamous cell carcinoma
- magnetic resonance imaging
- peritoneal dialysis
- amino acid
- convolutional neural network
- high grade
- case report
- patient reported outcomes
- patient reported
- blood brain barrier
- dual energy