Multi-parametric diffusion spectrum imaging in tuberous sclerosis complex: Identifying cortical tubers and predicting...

Area of Science:

• Neuroimaging
• Radiology
• Genetics

Background:

• Tuberous Sclerosis Complex (TSC) is a genetic disorder characterized by the growth of noncancerous tumors in various organs.
• Accurate identification of cortical tubers is crucial for TSC diagnosis and management.
• Advanced MRI techniques offer potential for improved lesion detection.

Purpose of the Study:

• To evaluate the diagnostic efficacy of advanced MRI diffusion models in identifying cortical tubers in TSC patients.
• To compare the performance of different diffusion model parameters in predicting TSC genotypes.
• To develop a predictive model for TSC genotype based on diffusion imaging parameters.

Main Methods:

• Prospective study of 83 TSC patients using Diffusion Spectrum Imaging (DSI).
• Analysis of Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Mean Apparent Propagator (MAP) models.
• Logistic regression and nomogram development for predictive modeling.

Main Results:

• NODDI-ICVF and MAP-QIV parameters demonstrated superior lesion visibility and contrast compared to DTI and DKI.
• The combined model using ICVF, QIV, and RTOP parameters achieved high diagnostic performance (AUC 0.89).
• The developed nomogram showed strong discrimination for TSC genotypes.

Conclusions:

• NODDI and MAP diffusion models enhance the clarity of cortical tuber visualization in TSC.
• A combined advanced diffusion parameter model provides the most effective prediction of TSC genotypes.