Magnetic Resonance Relaxometry for Tumor Cell Density Imaging for Glioma: An Exploratory Study via 11C-Methionine PET and Its Validation via Stereotactic Tissue Sampling.
Manabu KinoshitaMasato UchikoshiSouichiro TateishiShohei MiyazakiMio SakaiTomohiko OzakiKatsunori AsaiYuya FujitaTakahiro MatsuhashiYonehiro KanemuraEku ShimosegawaJun HatazawaShin-Ichi NakatsukaHaruhiko KishimaKatsuyuki NakanishiPublished in: Cancers (2021)
One of the most crucial yet challenging issues for glioma patient care is visualizing non-contrast-enhancing tumor regions. In this study, to test the hypothesis that quantitative magnetic resonance relaxometry reflects glioma tumor load within tissue and that it can be an imaging surrogate for visualizing non-contrast-enhancing tumors, we investigated the correlation between T1- and T2-weighted relaxation times, apparent diffusion coefficient (ADC) on magnetic resonance imaging, and 11C-methionine (MET) on positron emission tomography (PET). Moreover, we compared the T1- and T2-relaxation times and ADC with tumor cell density (TCD) findings obtained via stereotactic image-guided tissue sampling. Regions that presented a T1-relaxation time of >1850 ms but <3200 ms or a T2-relaxation time of >115 ms but <225 ms under 3 T indicated a high MET uptake. In addition, the stereotactic tissue sampling findings confirmed that the T1-relaxation time of 1850-3200 ms significantly indicated a higher TCD (p = 0.04). However, ADC was unable to show a significant correlation with MET uptake or with TCD. Finally, synthetically synthesized tumor load images from the T1- and T2-relaxation maps were able to visualize MET uptake presented on PET.
Keyphrases
- magnetic resonance
- positron emission tomography
- computed tomography
- mass spectrometry
- contrast enhanced
- multiple sclerosis
- diffusion weighted imaging
- magnetic resonance imaging
- ms ms
- single molecule
- pet ct
- high resolution
- diffusion weighted
- tyrosine kinase
- pet imaging
- stem cells
- bone marrow
- small cell lung cancer
- optical coherence tomography
- living cells
- convolutional neural network