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Abstract #3390

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

Yunjun Yang1, Cheng Li1, Chengming Wang2, Hai Zhao1, Jialu Zhang3, and Zhifeng Xu1
1Department of Radiology, The First People’s Hospital of Foshan, Foshan, China, 2The First People’s Hospital of Foshan, Foshan, China, 3MR Research, GE Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, biomarkers

Motivation: Improving early Parkinson’s disease diagnosis using advanced MRI techniques and machine learning to detect subtle neuroanatomical and microstructural changes.

Goal(s): To develop a machine learning model using quantitative MRI features to accurately diagnose early-stage Parkinson’s disease.

Approach: This study used QSM and DKI features, analyzed with an SVM model, to differentiate early-stage Parkinson’s disease from healthy controls and advanced-stage PD.

Results: The SVM model demonstrated moderate accuracy in detecting early-stage PD (accuracy: 0.78, AUC: 0.90) and high accuracy for advanced-stage PD (accuracy: 0.97, AUC: 0.97), with DKI's kurtosis feature crucial for classification.

Impact: This study demonstrates the potential of combining QSM and DKI features to improve early-stage Parkinson's disease diagnosis, offering clinicians a non-invasive tool for detection. It paves the way for future research into MRI-based biomarkers for disease progression monitoring.

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Keywords