Parkinsonism & related disorders · 2025
Diagnostic performance of T1-Weighted MRI gray matter biomarkers in Parkinson's disease: A systematic review and meta-analysis.
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Torres-Parga Alejandra, Gershanik Oscar, Cardona Sebastian, Guerrero Jairo, Gonzalez-Ojeda Lina M, Cardona Juan F
Аннотация
BACKGROUND: T1-weighted structural MRI has advanced our understanding of Parkinson's disease (PD), yet its diagnostic utility in clinical settings remains unclear.
OBJECTIVE: To assess the diagnostic performance of T1-weighted MRI gray matter (GM) metrics in distinguishing PD patients from healthy controls and to identify limitations affecting clinical applicability.
METHODS: A systematic review and meta-analysis were conducted on studies reporting sensitivity, specificity, or AUC for PD classification using T1-weighted MRI. Of 2906 screened records, 26 met inclusion criteria, and 10 provided sufficient data for quantitative synthesis. The risk of bias and heterogeneity were evaluated, and sensitivity analyses were performed by excluding influential studies.
RESULTS: Pooled estimates showed a sensitivity of 0.71 (95 % CI: 0.70-0.72), specificity of 0.889 (95 % CI: 0.86-0.92), and overall accuracy of 0.909 (95 % CI: 0.89-0.93). These metrics improved after excluding outliers, reducing heterogeneity (I2 = 95.7 %-0 %). Frequently reported regions showing structural alterations included the substantia nigra, striatum, thalamus, medial temporal cortex, and middle frontal gyrus. However, region-specific diagnostic metrics could not be consistently synthesized due to methodological variability. Machine learning approaches, particularly support vector machines and neural networks, showed enhanced performance with appropriate validation.
CONCLUSIONS: T1-weighted MRI gray matter metrics demonstrate moderate accuracy in differentiating PD from controls but are not yet suitable as standalone diagnostic tools. Greater methodological standardization, external validation, and integration with clinical and biological data are needed to support precision neurology and clinical translation.
OBJECTIVE: To assess the diagnostic performance of T1-weighted MRI gray matter (GM) metrics in distinguishing PD patients from healthy controls and to identify limitations affecting clinical applicability.
METHODS: A systematic review and meta-analysis were conducted on studies reporting sensitivity, specificity, or AUC for PD classification using T1-weighted MRI. Of 2906 screened records, 26 met inclusion criteria, and 10 provided sufficient data for quantitative synthesis. The risk of bias and heterogeneity were evaluated, and sensitivity analyses were performed by excluding influential studies.
RESULTS: Pooled estimates showed a sensitivity of 0.71 (95 % CI: 0.70-0.72), specificity of 0.889 (95 % CI: 0.86-0.92), and overall accuracy of 0.909 (95 % CI: 0.89-0.93). These metrics improved after excluding outliers, reducing heterogeneity (I2 = 95.7 %-0 %). Frequently reported regions showing structural alterations included the substantia nigra, striatum, thalamus, medial temporal cortex, and middle frontal gyrus. However, region-specific diagnostic metrics could not be consistently synthesized due to methodological variability. Machine learning approaches, particularly support vector machines and neural networks, showed enhanced performance with appropriate validation.
CONCLUSIONS: T1-weighted MRI gray matter metrics demonstrate moderate accuracy in differentiating PD from controls but are not yet suitable as standalone diagnostic tools. Greater methodological standardization, external validation, and integration with clinical and biological data are needed to support precision neurology and clinical translation.