Time-dependent Diffusion MRI-based Microstructural Mapping Value for Predicting Meningioma Grade, Subtype, and...

Area of Science:

• Neuroradiology
• Oncology
• Medical Imaging

Background:

• Meningiomas are the most common primary central nervous system tumors.
• Accurate grading, subtyping, and assessment of proliferative activity are crucial for treatment planning and prognosis.
• Conventional MRI (cMRI) has limitations in differentiating meningioma characteristics.

Purpose of the Study:

• To evaluate the diagnostic performance of time-dependent diffusion MRI (t_d-dMRI) combined with histogram analysis.
• To predict meningioma grade, subtype, and proliferative activity.
• To compare the combined model with conventional MRI and single-parameter models.

Main Methods:

• Prospective enrollment of 107 participants with meningiomas.
• Presurgical t_d-dMRI and conventional MRI (cMRI) acquisition.
• Histogram feature extraction from t_d-dMRI parameters and semantic features from cMRI.
• Statistical analysis including AUC, DeLong, and IDI tests; Spearman correlations with Ki-67 index.

Main Results:

• The combined cMRI-t_d-dMRI model significantly outperformed cMRI and single-parameter models in meningioma grading (AUC 0.86).
• The combined model demonstrated high diagnostic performance for meningioma subtyping (AUC 0.86).
• Weak correlations were found between t_d-dMRI metrics and the Ki-67 index, suggesting potential for assessing proliferative activity.

Conclusions:

• Time-dependent diffusion MRI (t_d-dMRI) combined with histogram analysis is a valuable tool for assessing meningioma grade and subtype.
• This approach shows potential for evaluating meningioma proliferative activity.
• The integration of t_d-dMRI offers improved diagnostic performance over conventional MRI techniques.