Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy

Purpose To construct multivariate radiomics models using hybrid F-18-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA). Methods Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent F-18-fluorodeoxyglucose (F-18-FDG) PET/MRI to simultaneously obtain metabolic images (F-18-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA). Results The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (F-18-FDG) sequences (Radscore(FDG_T1WI_SWI)) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore(FDG_T1WI_SWI), three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUVmax, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model. Conclusions The radiomics signature with metabolic, structural and functional information provided by hybrid F-18-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.

Automated summary

The study aimed to construct multivariate radiomics models using hybrid F-18-FDG PET/MRI to distinguish between PD and MSA, showing that the radiomics signature with metabolic, structural, and functional information may achieve promising diagnostic efficacy. The clinical-radiomics integrated model performed best in distinguishing between PD and MSA.