Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics

To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics.

One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([¹⁸F]FDG, [¹¹C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models.

The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model.

Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics.

This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance.

•Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.