Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson's disease motor subtypes in early-stages.

ABSTRACT: This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the

SUBMITTER: Panahi M 

PROVIDER: S-EPMC11377437 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature