Robustness of quantitative imaging biomarkers for classification of neurodegenerative disordersŠtokelj, Eva (Avtor) Simončič, Urban (Mentor) Quantitative imaging biomarkersneurodegenerative diseasesAlzheimer’s diseaseParkinson’s diseaseparkinsonismsFDG-PETMRIDTIdisease-related metabolic patternsasymmetry indexdiffusion measures from MR imagesThis thesis evaluates the robustness of quantitative imaging biomarkers (QIBs) for the classification of neurodegenerative diseases. Although many biomarkers distinguish patients from healthy controls at the group level, their clinical usefulness depends on reliable performance under biological and methodological variability. A unified robustness framework was developed, based on effect size (the absolute value of Cohen's d, |d|), its relationship to classification performance (AUC), and systematic sensitivity analysis. Three categories of biomarkers were investigated. The first comprised metabolic brain patterns derived from FDG-PET using the scaled subprofile model/principal component analysis (SSM/PCA). These showed excellent diagnostic performance for Alzheimer's and Parkinson's disease (AUC ≈ 0.9; |d| ≈ 1.7–1.8). A novel multi-group extension was developed to simultaneously differentiate Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy, achieving large effect sizes at the reference configuration (AUC ≈ 0.9–1.0; |d| ≈ 2.1–2.4) without requiring healthy controls. The second category, the asymmetry index from structural MRI, detected differences between Alzheimer's patients and controls, but diagnostic performance remained moderate (AUC ≈ 0.6; |d| ≈ 0.5) and did not improve across methodological variations, indicating limited potential within the tested implementation. The third category comprised diffusion-MRI biomarkers. TBSS and network-based tractography combined diagnostic performance and robustness more favorably (AUC ≈ 0.7–0.8; |d| ≈ 0.9–1.0) than histogram-based approaches (AUC ≈ 0.7; |d| ≈ 0.7), though they were more sensitive to methodological choices than metabolic patterns. Robustness and discriminative strength do not necessarily coincide: a biomarker may be robust yet weakly discriminative, or strongly discriminative yet not robust, so the two must be assessed jointly. Among the approaches studied, metabolic brain patterns were the most robust and clinically promising. The proposed approach enables a realistic comparison of QIBs under biological and methodological variability.20262026-10-30 08:15:08Doktorsko delo/naloga190591sl