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Multiple diffusion metrics in differentiating solid glioma from brain inflammation
Kai Zhao1, Ankang Gao1, Eryuan Gao1
1Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Background And Purpose:
The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models.
Materials And Methods:
Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated.
Results:
57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758).
Conclusion:
Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.
Insights
The mean apparent propagator (MAP) model’s non-Gaussianity (NG) metric best differentiates brain inflammation from solid glioma. This diffusion-weighted imaging approach aids in diagnosing challenging MRI cases.
Area of Science:
- Neuroimaging
- Radiology
- Medical Diagnostics
Background:
- Distinguishing solid glioma from brain inflammation on MRI is clinically significant but challenging.
- Diffusion-weighted imaging (DWI) offers advanced metrics for tissue characterization.
Purpose of the Study:
- To evaluate the efficacy of various diffusion metrics from multiple DWI models in differentiating solid glioma from brain inflammation.
- To compare the diagnostic performance of different DWI models for this differential diagnosis.
Main Methods:
- Prospective enrollment of 57 patients with glioma or inflammation and solid MRI lesions.
- Acquisition of multi-b-value DWI data (0-2500 s/mm²).
- Calculation of diffusion metrics using Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Mean Apparent Propagator (MAP), and Neurite Orientation Dispersion and Density Imaging (NODDI) models.
Main Results:
- The MAP model's non-Gaussianity (NG) metric demonstrated the highest diagnostic performance (AUC = 0.879).
- Diffusion Kurtosis Imaging's mean kurtosis (MK) (AUC = 0.855) and NODDI's intracellular volume fraction (ICVF) (AUC = 0.825) also showed good performance.
- Diffusion Tensor Imaging's mean diffusivity (MD) yielded the lowest diagnostic performance (AUC = 0.758).
Conclusions:
- Multiple diffusion metrics derived from advanced DWI models can effectively differentiate between brain inflammation and solid glioma.
- The MAP model, specifically its non-Gaussianity (NG) metric, shows superior performance for this differentiation.
