Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.

BACKGROUND

Identifying Parkinson's disease (PD) during its initial phases presents considerable hurdles for clinicians.

PURPOSE

To examine the feasibility and efficacy of a machine learning model based on quantitative multiparametric magnetic resonance imaging (MRI) features in identifying early-stage PD.

METHODS

We recruited 33 participants, including 19 with early-stage PD, 14 with advanced-stage PD and 20 healthy control subjects. Each participant underwent both quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI). We utilized combined QSM and DKI features to establish a support vector machine (SVM) model to identify early-stage PD.

RESULTS

When comparing early-stage PD with healthy controls, the SVM model exhibited moderate performance, achieving a training set accuracy of 0.78 and an area under the receiver operating characteristic curve (AUC) of 0.90, and the accuracy of 0.77 (AUC = 0.87) in the test set. When comparing advanced-stage PD with healthy controls, the SVM model exhibited equally high accuracy in both training (0.97, AUC = 0.97) and test (0.94, AUC = 0.94) sets. In discriminating between early-stage PD and advanced-stage PD, the SVM model achieved an accuracy of 0.80 (AUC = 0.81) in the training set and an accuracy of 0.71 (AUC = 0.72) in the test set. The mean kurtosis feature of DKI in the substantia nigra, played a significant role in classification.

CONCLUSION

These findings suggest that early PD is associated with specific MRI features reflecting magnetic susceptibility and microstructural changes. The SVM model combining quantitative QSM and DKI features holds promise for improving early PD diagnosis.