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 preprocessing methods. Wavelet-based features showed the highest reproducibility, with 37% demonstrating excellent ICC values (≥ 0.90). Excluding non-reproducible features generally improved classification performance. Specific results include: (1) The Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising + Bias field correction + Z-score Normalization (S + B + ZN) method achieved the highest Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) (0.88) before feature exclusion. (2) After excluding non-reproducible features, the Bias field correction + Z-score Normalization (B + ZN) method showed the most significant improvement, with AUC increasing from 0.49 to 0.64. (3) Texture-based features, particularly from Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), were among the most reproducible across preprocessing methods. MRI preprocessing methods significantly impact radiomic feature reproducibility and subsequent classification performance in PD motor subtype analysis. Wavelet-based and texture features demonstrated high reproducibility, while excluding non-reproducible features generally improved classification accuracy. These findings underscore the importance of careful preprocessing method selection and feature reproducibility assessment in developing robust radiomics-based classification models for PD subtypes.

Keywords: Feature reproducibility; MRI radiomics; Machine learning classification; Motor subtypes; Parkinson’s disease; Preprocessing methods.