Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment.

ABSTRACT:

Background

Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD.

Methods

We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for featur

SUBMITTER: Luo Y 

PROVIDER: S-EPMC10417024 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature