Diagnostic performance of T1-Weighted MRI gray matter biomarkers in Parkinson's disease: A systematic review and meta-analysis.
Authors
Affiliations (6)
Affiliations (6)
- PhD Program in Psychology, Faculty of Psychology, Universidad del Valle, Cali, Colombia.
- Movement Disorders Unit, Institute of Neuroscience, Favaloro Foundation University Hospital, Buenos Aires, Argentina; Cognitive Neuroscience Laboratory, Institute of Cognitive Neurology (INECO), Buenos Aires, Argentina.
- Neurology Residency Program, Department of Internal Medicine, School of Health Sciences, Universidad del Valle, Cali, Colombia.
- Neurology Residency Program, Department of Internal Medicine, School of Health Sciences, Universidad del Valle, Cali, Colombia; Department of Neurology, Clínica Imbanaco, Grupo Quirónsalud, Cali, Colombia.
- PhD Program in Psychology, Faculty of Psychology, Universidad del Valle, Cali, Colombia; Hospital Universitario del Valle "Evaristo García" E.S.E, Cali, Colombia.
- PhD Program in Psychology, Faculty of Psychology, Universidad del Valle, Cali, Colombia; Department of Developmental Sciences, Cognition and Neuroscience, Faculty of Psychology, Universidad del Valle, Cali, Colombia. Electronic address: [email protected].
Abstract
T1-weighted structural MRI has advanced our understanding of Parkinson's disease (PD), yet its diagnostic utility in clinical settings remains unclear. 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. 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. 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 (I<sup>2</sup> = 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. 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.