Keywords: Radiomics, Neurodegeneration
Motivation: Aim to differentiate PD from MSA in the early stage.
Goal(s): To build a radiomic model based on features derived from basal ganglia regions by using commonly applied sequences in clinical settings, to distinguish between PD and MSA.
Approach: This study constructed three machine learning models- logistic regression, support vector machine and light gradient boosting method to differentiate PD motor subtypes.
Results: The light gradient boosting machine trained by features extracted from SWI and T1 sequences achieved a great classification performance between PD and MSA (AUC=0.881).
Impact: This study has developed an effective classification model using commonly utilized clinical MRI sequences, which provides a valuable tool for distinguishing between PD and MSA in clinical practice.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords