2914
Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging1Radiology, Fudan Huashan Hospital, Shanghai, China, 2Fujian Medical University Union Hospital, Fuzhou, China, 3Radiology, Fujian Cancer Hospital&Fujian Medical University Cancer Hospital, Fuzhou, China, 4MR scientific marketing, Siemens Healthcare, Shanghai, China
Synopsis
Accurate grading is crucial to determine therapeutic strategies and to evaluate the prognosis, but no specific feature of conventional MRI has been found to be reliable in predicting the grade of the tumor. This study prospectively evaluated and compared the potential of various diffusion metrics obtained from mono-exponential model (MEM), bi-exponential model (BEM) and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in the grading of meningiomas. It was found that different models of DWI (MEM, BEM, and SEM) are useful in the differentiation between high-grade and low-grade meningiomas. However, D obtained from BEM is the most promising diffusion parameter for predicting the grade of meningiomas.
Purpose
Meningiomas are one of the most common primary brain tumors.1 Although conventional MRI provides several identifiable features for meningiomas, no specific feature has been found to be reliable in predicting the grade of the tumor.2,3 This study prospectively evaluated and compared the potential of various diffusion metrics obtained from mono-exponential model (MEM), bi-exponential model (BEM) and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in the grading of meningiomas.Methods
Patients with suspected meningiomas were consecutively enrolled in the study from October 2014 to July 2017. A total of 93 patients were included in the study. MRI scans were performed on a 3.0T MR scanner with an eight-channel receiver head coil. DWI used a SE-EPI diffusion sequence in the axial plane (TR/TE = 5,000/84.6 ms, slice thickness/ slice gap = 5 mm/0 mm, FOV = 24 cm, matrix = 192 × 192, number of sections = 30, FOV = 24 cm. Twelve b values from 0 to 3000 sec/mm2 (0, 50, 100, 150, 200, 300, 500, 800, 1000, 1500, 2000, and 3000 sec/mm2. The DWI data were obtained and transferred to a workstation (Advantage Workstation 4.6) for processing. Parameter maps were generated automatically by the MADC program in the Functool software. The quantitative measurement of parameter maps was performed on Image J (Version 1.50i). On contrast-enhanced T1-weighted imaging, an ROI was drawn to include the entire enhancing lesion on the largest section of the tumor. Each ROI was semi-automatically delineated by using the wand tool in Image J and copied to parameter maps for analyses.4 Afterward, mean diffusion values in the tumors were normalized to the corresponding values in the contralateral NAWM to reduce intersubject variation. Apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (alpha), and distributed diffusion coefficient (DDC) were calculated and compared between low-grade and high-grade meningiomas. Receiver operating characteristic and multivariable stepwise logistic regression were performed to evaluate the diagnostic performance of different parameters.Results
Representative cases of low-grade and high-grade meningioma are shown in Figs. 1 and 2, respectively. Fig. 3 show the quantitative comparison of differences in diffusion parameters between the two meningioma groups. The mean and normalized ADC, D, f and DDC values were significantly lower in high-grade meningiomas than those in low-grade meningiomas (P < 0.05). There was no significant difference in the mean and normalized values of D* and alpha between the two groups (P > 0.05). D exhibited the maximal AUC for differentiating high-grade meningiomas from low-grade meningiomas (Fig. 4). Moreover, the AUCs of D, ADC, and DDC was significantly higher than that of f in differentiating between high-grade meningiomas and low-grade meningioma (P < 0.05). In addition, multivariable stepwise logistic regression analysis showed that the normalized D was the most significant variable, with a parameter estimate of 11.86 and a standard error of 3.01 (P < 0.001).Discussion and conclusion
Our results demonstrated that the differentiation of low-grade and high-grade meningiomas is feasible by different models of DWI. Moreover, we found that the diffusion-related parameters (ADC, D, and DDC) generally performed better than the perfusion-related metric(f). However, diffusion-related parameters (ADC, D, and DDC) have significantly better diagnostic performances than the perfusion-related metric (f). In addition, D was the strongest independent predictor associated with the grade of meningiomas.Acknowledgements
No acknowledgement found.References
1. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. Jun 2016;131(6):803-820.
2. Kawahara Y, Nakada M, Hayashi Y, et al. Prediction of high-grade meningioma by preoperative MRI assessment. J Neurooncol. May 2012;108(1):147-152.
3. Lin BJ, Chou KN, Kao HW, et al. Correlation between magnetic resonance imaging grading and pathological grading in meningioma. J Neurosurg. Nov 2014;121(5):1201-1208.
4. L. Lin, R. Bhawana, Y. Xue, Q. Duan, R. Jiang, H. Chen, X. Chen, B. Sun, H. Lin, Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas, AJNR. American journal of neuroradiology 39(6) (2018) 1032-1038.
Figures
