Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson's disease motor subtype analysis.

ABSTRACT: To evaluate the impact of various magnetic resonance imaging (MRI) preprocessing methods on radiomic feature reproducibility and classification performance in differentiating Parkinson's disease (PD) motor subtypes. We analyzed 210 T1-weighted MRI scans from the Parkinson's Progression Markers Initiative (PPMI) database, including 140 PD patients (70 tremor-dominant (TD), 70 postural instability/gait difficulty (PIGD)) and 70 healthy controls. Five preprocessing pipelines were applied, and 22,560 radiomic features were extracted from 16 brain regions. Feature reproducibility was assessed using intraclass correlation coefficients (ICC). Support Vector Machine (SVM) classifiers were developed using all features and only reproducible features to compare classification performance across prepr

SUBMITTER: Panahi M 

PROVIDER: S-EPMC12618640 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature