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Characterizing meningioma subtypes using MRI-cytometry1Siemens Healthineers Ltd., Shanghai, China, 2Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China, 3Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 4Human Phenome Institute, Fudan University, Shanghai, China, 5Siemens Healthcare GmbH, Erlangen, Germany
Synopsis
We used MRI-cytometry to estimate the mean cell diameter and intracellular volume of tumors of the meningioma subtype classification. We found cell size and intracellular volume to be very different between the three meningioma subtypes in this preliminary study. Our results reflected high collagen volume among the extracellular space in fibrous meningioma. We plan to scan more patients in the future to find a useful biomarker using MRI-cytometry.
Introduction
Determining the subtype and clinical characteristics of meningioma is crucial to treatment planning1. Imaging characteristics, such as tumor volume, presence of tumor necrosis, edema, or location acquired by T1w or T2w imaging, are related to the histological grade2. A machine-learning analysis method was applied to better classify the meningioma by grade, based on similar imaging features3. However, it is difficult to access the texture features of tumors or subtypes of meningiomas using routine MRI scans. The texture-related factors of meningioma, such as consistency, adherence, and neurovascular structures, are associated with surgical resection. Meningiomas with high stiffness and high adherence to surrounding brain tissue can be a challenge for surgical removal. It is necessary to predict these parameters before surgery to reduce the risk of surgical morbidity. A recent study showed a weak association between the T2/T1 intensity ratio and tumor consistency4. Diffusion-weighted imaging and apparent diffusion coefficient (ADC) maps have been used to correlate tumor grade and histopathologic findings5. Diffusion-weighted imaging provides information regarding tissue microstructure. However, the correlation between tumor cellularity and ADC reported in several studies is inconsistent6,7. Recently, multidimensional or oscillating gradient spin‐echo (OGSE) diffusion-weighted imaging provides a non-invasive tool to access tumor cellularity and its microstructure7,8. Here, we used MRI-cytometry9 to estimate the mean cell diameter and intracellular volume of the tumor. We hypothesized that the derived histopathological information could be used to estimate subtypes of meningiomas and their texture.Methods
Pre-operative MRI scans were acquired on a 3T MR scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany). Four meningioma patients, including two with fibrous meningioma, one with meningothelial meningioma, and one with angiomatous meningioma, were scanned. The MPRAGE sequence was used for structural T1 weighted imaging (TR/TE = 2300/2.29 ms, image matrix = 256 x 256 x 176, resolution = 0.93 mm iso). Pulsed and oscillating gradient spin-echo (PGSE and OGSE, respectively) diffusion-weighted images were acquired with the same oscillating frequencies and diffusion duration as previously described10 using a prototype sequence (TR/TE = 4000/119 ms, imaging matrix = 96 x 96 x 20, resolution = 2 x 2 x 5 mm3, GRAPPA = 2 and SMS = 2, PGSE: b= 0/250/500/750/1000/1400/1800 s/mm2, OGSE 25Hz: b= 0/250/500/750/1000 s/mm2, OGSE 50Hz: b = 0/100/200/300 s/mm2). The mean cell diameter and intracellular volume fraction (IVF) were calculated using MRI-cytometry9. Tumors were sectioned and stained with hematoxylin and eosin (H&E) for cellularity.Results
Figure 1 shows the T1 weighted images, ADC, IVF, mean cell size (diameter) maps, and H&E stain of the four patients. Based on the image intensities, it is difficult to tell the difference in the T1 and ADC values between the various tumor subtypes. Comparing the IVF maps, the first fibrous meningioma had a lower value, and the angiomatous meningioma had a greater value. Comparing the mean cell size maps, the fibrous meningioma had a smaller cell size, and angiomatous meningioma had a larger cell size. Figure 2 shows the boxplots of ADC, IVF, and mean cell size of voxels within the tumor for each patient. The median ADCs from left to right were 0.70, 0.89, 0.69, and 0.68 x 10-3 mm2/s, respectively. The median IVFs from left to right were 0.10, 0.11, 0.31, and 0.52, respectively. The median cell size from left to right were 12.62, 12.03, 14.38, and 18.60, respectively. The median IVF of meningothelial meningioma was 285% that of the fibrous meningioma, and the median IVF of angiomatous meningioma was 485% that of the fibrous meningioma. The median cell size of the meningothelial meningioma was 117% that of the fibrous meningioma, and the median cell size of the angiomatous meningioma was 151% that of the fibrous meningioma.Discussion and Conclusion
ADC values in meningiomas depend on cellularity, cell size, diffusion coefficient, and vascularity. Using MRI-cytometry, we were able to separate these effects and provide microstructural information which correlates with histological findings. The information provided by this method may enable to distinguish meningioma subtypes without the need for biopsy. In this preliminary study, we found that the IVF and cell type were different between the three meningioma subtypes. Particularly for the IVF, meningothelial meningioma and angiomatous meningioma were 285% and 485% that of fibrous meningioma, respectively. The lower IVF of fibrous meningioma reflected high collagen volume among the extracellular space. The relation between extracellular volume and collagen volume was implemented in diagnosing myocardial fibrosis11. We only compared three different meningiomas among four patients; the number was too small to provide useful conclusions that can be used for future subtype classification. In the future, we plan to recruit additional patients and aim to find biomarkers for subtype classification. Due to the slew rate limit in clinical scanners, this method may not be accurate for small cells due to the limited gradient oscillation frequencies. The assumption made for non-permeable cell membrane and spherical cells may not reflect reality, but the quantitative values estimated from MRI-cytometry can still be useful if used as a biomarker to classify the tumor subtype.Acknowledgements
No acknowledgement found.References
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Figures
Figure 1. T1 weighted images, ADC, IVF, and mean cell size (diameter) maps, respectively, of the four patients.
