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MR diffusion kurtosis imaging predicts malignant potential and the histological type of meningioma
Fen Xing1, Ning Tu1, Tong San Koh2
1Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan 430071, PR China.
Purpose:
To explore the value of Diffusion kurtosis imaging (DKI) metrics in the differential diagnosis of meningioma.
Methods:
For this study, we retrospectively enrolled 35 patients of cerebral meningioma with DKI which included axial diffusion coefficient (AD), radial diffusion coefficient (RD), mean diffusion coefficient (MD), fractional anisotropy (FA), axial kurtosis (AK), radial kurtosis (RK) and mean kurtosis (MK). All of these metrics were normalized according to contralateral normal-appearing white matter (NAWMc). Patients were divided into two groups (benign and malignant meningioma) and were further analyzed using the independent sample t-test and receiver operating characteristic (ROC) curve. A one-way ANOVA analysis was used to analyze four groups divided according to pathological subtypes.
Results:
The metrics of AD, normalized AD, normalized MD, MK and normalized MK showed a significant difference between benign and malignant group, and MK showed relatively higher diagnostic ability with its cut-off value, area under the curve (AUC), sensitivity and specificity of 0.875, 0.780, 70% and 89%, respectively. The metrics of normalized MD, RD and normalized RD, FA and normalized FA, AK and normalized AK, and RK showed significant difference among four subtypes. MK and RK in meningioma were found to correlate positively with the Ki-67 labeling index (Ki-67 LI).
Conclusions:
DKI metrics may be used to differentiate benign from malignant meningioma, and also to distinguish some histological subtypes of meningioma. Moreover, DKI metrics may potentially reflect cellular proliferation.
Insights
Diffusion kurtosis imaging (DKI) metrics can differentiate benign from malignant meningioma. Mean kurtosis (MK) showed high accuracy, and DKI metrics may reflect tumor cell proliferation.
Area of Science:
- Neuroimaging
- Radiology
- Oncology
Background:
- Meningiomas are the most common primary intracranial tumors.
- Accurate preoperative differentiation between benign and malignant meningiomas is crucial for treatment planning.
- Diffusion kurtosis imaging (DKI) offers advanced insights into tissue microstructure.
Purpose of the Study:
- To evaluate the diagnostic value of DKI metrics for distinguishing between benign and malignant meningiomas.
- To explore the potential of DKI in differentiating meningioma subtypes.
- To investigate the correlation between DKI metrics and tumor proliferation markers.
Main Methods:
- Retrospective analysis of 35 cerebral meningioma patients using DKI.
- Acquisition of metrics including AD, RD, MD, FA, AK, RK, and MK, normalized to contralateral white matter.
- Statistical analysis using independent t-tests, ROC curves, and ANOVA to compare groups and subtypes.
Main Results:
- Significant differences in AD, normalized AD, normalized MD, MK, and normalized MK between benign and malignant groups.
- Mean kurtosis (MK) demonstrated high diagnostic performance (AUC=0.780, sensitivity=70%, specificity=89%).
- Several DKI metrics showed significant differences among four pathological subtypes and correlated positively with Ki-67 labeling index.
Conclusions:
- DKI metrics show promise in differentiating benign from malignant meningiomas.
- DKI can aid in distinguishing between histological subtypes of meningioma.
- DKI metrics may serve as a non-invasive biomarker for assessing meningioma cellular proliferation.
