Abstract #3258

Diagnosis of Parkinson’s disease using a radiomics approach based on STrategically Acquired Gradient Echo (STAGE)

Yi Duan¹, Yida Wang¹, Naying He², Yan Li², Zenghui Cheng², Yu Liu², Zhijia Jin², Pei Huang³, Shengdi Chen³, Ewart Mark Haacke^2,4, Fuhua Yan², and Guang Yang¹

¹East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China, ²Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ⁴Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States

Diagnosing Parkinson’s disease (PD) is still a clinical challenge. Deep grey matter is involved in the pathophysiological changes of PD. We built a radiomics model to distinguish PD from normal controls (NC) based on five brain nuclei in multiple quantitative images derived from STrategically Acquired Gradient Echo (STAGE) imaging. This model combined features from the caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra regions in QSM, T1and proton density maps and achieved a test AUC of 0.948. Features from the SN region as seen in the QSM images were found to be the most important ones for classification.

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