Prediction and Visualization of Non-Enhancing Tumor in Glioblastoma via T1w/T2w-Ratio Map.
Shota YamamotoTakahiro SanadaMio SakaiAtsuko ArisawaNaoki KagawaEku ShimosegawaKatsuyuki NakanishiYonehiro KanemuraManabu KinoshitaHaruhiko KishimaPublished in: Brain sciences (2022)
One of the challenges in glioblastoma (GBM) imaging is to visualize non-enhancing tumor (NET) lesions. The ratio of T1- and T2-weighted images (rT1/T2) is reported as a helpful imaging surrogate of microstructures of the brain. This research study investigated the possibility of using rT1/T2 as a surrogate for the T1- and T2-relaxation time of GBM to visualize NET effectively. The data of thirty-four histologically confirmed GBM patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11 C-methionine positron emission tomography (Met-PET) were available were collected for analysis. Two of them also underwent MR relaxometry with rT1/T2 reconstructed for all cases. Met-PET was used as ground truth with T2-FLAIR hyperintense lesion, with >1.5 in tumor-to-normal tissue ratio being NET. rT1/T2 values were compared with MR relaxometry and Met-PET. rT1/T2 values significantly correlated with both T1- and T2-relaxation times in a logarithmic manner ( p < 0.05 for both cases). The distributions of rT1/T2 from Met-PET high and low T2-FLAIR hyperintense lesions were different and a novel metric named Likeliness of Methionine PET high (LMPH) deriving from rT1/T2 was statistically significant for detecting Met-PET high T2-FLAIR hyperintense lesions (mean AUC = 0.556 ± 0.117; p = 0.01). In conclusion, this research study supported the hypothesis that rT1/T2 could be a promising imaging marker for NET identification.
Keyphrases
- patient reported
- positron emission tomography
- contrast enhanced
- computed tomography
- pet ct
- magnetic resonance imaging
- diffusion weighted
- pet imaging
- magnetic resonance
- tyrosine kinase
- high resolution
- diffusion weighted imaging
- dual energy
- chronic kidney disease
- optical coherence tomography
- deep learning
- machine learning
- mass spectrometry
- multiple sclerosis
- white matter
- big data
- newly diagnosed
- ejection fraction
- amino acid
- prognostic factors