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Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics
Sumei Wang1, Sungheon Kim, Yu Zhang
1Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, 219 Dulles Bldg, Philadelphia, PA 19104, USA. Sumei.Wang@uphs.upenn.edu
Purpose:
To determine whether histogram analysis of diffusion-tensor (DT) magnetic resonance (MR) imaging metrics, including tensor shape measurements, can help determine the grades and subtypes of meningiomas.
Materials And Methods:
The institutional review board approved this HIPAA-compliant study. Nine atypical, three anaplastic, and 39 typical meningiomas were retrospectively studied. The 39 typical meningiomas included one secretory meningioma and 11 fibroblastic, 11 transitional, 14 meningothelial, and two angiomatous meningiomas. DT imaging metrics, including fractional anisotropy, mean diffusivity, linear anisotropy coefficient, planar anisotropy coefficient (CP), spherical anisotropy coefficient (CS), and eigenvalue skewness (SK), as well as normalized signal intensity from contrast-enhanced T1- and T2-weighted images, were measured from the enhancing region of the tumor. Mean, variance, skewness, and kurtosis were extracted from the histograms. A two-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.
Results:
Histogram skewness of SK and kurtosis of SK were significantly higher in atypical and anaplastic meningiomas than in typical meningiomas (P<.01). Among typical meningiomas, significant differences in histogram measures of CP and CS between fibroblastic meningiomas and other subtypes were observed (P<.01). The best model for differentiating atypical and anaplastic meningiomas from typical meningiomas consisted of mean and skewness of SK and kurtosis of T1 signal intensity, with an area under the receiver operating characteristic curve (AUC) of 0.946. The best model for differentiating fibroblastic meningiomas from other subtypes consisted of skewness of T2 signal intensity and kurtosis of CP (AUC, 0.970).
Conclusion:
Histogram analysis of DT imaging metrics can help determine the grades and subtypes of meningiomas, which can better assist in surgical planning.
Insights
Histogram analysis of diffusion tensor (DT) magnetic resonance (MR) imaging metrics can differentiate meningioma grades and subtypes. This technique aids in distinguishing aggressive tumors from typical ones and classifying subtypes, improving surgical planning.
Area of Science:
- Neuroradiology
- Medical Imaging Analysis
Background:
- Meningiomas are the most common primary intracranial tumors.
- Accurate grading and subtyping are crucial for treatment and prognosis.
- Distinguishing between typical, atypical, and anaplastic meningiomas, as well as subtypes, can be challenging with conventional imaging.
Purpose of the Study:
- To evaluate the utility of histogram analysis of diffusion tensor (DT) magnetic resonance (MR) imaging metrics for grading meningiomas.
- To assess the ability of these metrics to differentiate meningioma subtypes.
Main Methods:
- Retrospective analysis of DT MR imaging data from 39 typical, 9 atypical, and 3 anaplastic meningiomas.
- Extraction of histogram features (mean, variance, skewness, kurtosis) from DT imaging metrics (fractional anisotropy, mean diffusivity, tensor shape measurements) and T1/T2 signal intensities.
- Development of a two-level decision tree model with multivariate logistic regression for classification.
Main Results:
- Histogram skewness and kurtosis of eigenvalue skewness (SK) were significantly higher in atypical and anaplastic meningiomas compared to typical ones.
- Significant differences in histogram measures of planar anisotropy coefficient (CP) and spherical anisotropy coefficient (CS) were observed between fibroblastic meningiomas and other subtypes.
- A model combining SK and T1 signal intensity kurtosis achieved an AUC of 0.946 for differentiating grades; a model using T2 signal intensity skewness and CP kurtosis achieved an AUC of 0.970 for subtype differentiation.
Conclusions:
- Histogram analysis of DT imaging metrics provides valuable information for meningioma grading and subtyping.
- This advanced imaging analysis can assist in pre-operative classification, potentially improving surgical planning and patient management.
