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

Autor: Fan Hu, Fang Liu, Rui An, Tingfan Wu, Xiaoli Lan, Xuehan Hu, Weiwei Ruan, Xun Sun
Rok vydání: 2021
Předmět:
Zdroj: European Journal of Nuclear Medicine and Molecular Imaging. 48:3469-3481
ISSN: 1619-7089
1619-7070
Popis: Purpose To construct multivariate radiomics models using hybrid 18F-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 randomised to training and validation sets in a 7:3 ratio. All patients underwent 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI to simultaneously obtain metabolic images (18F-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 (18F-FDG) sequences (RadscoreFDG_T1WI_SWI) with the area under curves (AUCs) of the training and validation sets of 0.971 and 0.957, respectively. The integrated model, incorporating RadscoreFDG_T1WI_SWI, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUVmax, demonstrated satisfactory calibration and discrimination in the training and validation 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 18F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best. ໿
Databáze: OpenAIRE
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