18F-FDG hybrid PET/MR radiomics based on different segmentation methods for distinguishing Parkinson′s disease from multiple system atrophy / 中华核医学与分子影像杂志

ABSTRACT

Objective:To explore the impact of different segmentation methods on differential diagnostic efficiency of 18F-FDG PET/MR radiomics to distinguish Parkinson′s disease (PD) from multiple system atrophy (MSA). Methods:From December 2017 to June 2019, 90 patients (60 with PD and 30 with MSA; 37 males, 53 females; age (55.8±9.5) years) who underwent 18F-FDG PET/MR in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology were retrospectively collected. Patients were randomized to training set and validation set in a ratio of 7∶3. The bilateral putamina and caudate nuclei, as the ROIs, were segmented by automatic segmentation of brain regions based on anatomical automatic labeling (AAL) template and manual segmentation using ITK-SNAP software. A total of 1 172 radiomics features were extracted from T 1 weighted imaging (WI) and 18F-FDG PET images. The minimal redundancy maximal relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used for features selection and radiomics signatures (Radscore) construction, with 10-fold cross-validation for preventing overfitting. The diagnostic performance of the models was assessed by ROC curve analysis, and the differences between models were calculated by Delong test. Results:There were 63 cases in training set (42 PD, 21 MSA) and 27 cases in validation set (18 PD, 9 MSA). The Radscore values were significantly different between the PD group and the MSA group in all training set and validation set of radiomics models ( 18F-FDG_Radscore and T 1WI_Radscore) based on automatic or manual segmentation methods ( z values from -5.15 to -2.83, all P<0.05). ROC curve analysis showed that AUCs of 18F-FDG_Radscore and T 1WI_Radscore based on automatic segmentation in training and validation sets were 0.848, 0.840 and 0.892, 0.877, while AUCs were 0.900, 0.883 and 0.895, 0.870 based on manual segmentation. There were no significant differences in training and validation sets between Radiomics models based on different segmentation methods ( z values 0.04-0.77, all P>0.05). Conclusions:The 18F-FDG PET/MR radiomics models based on different segmentation methods achieve promising diagnostic efficacy for distinguishing PD from MSA. The radiomics analysis based on automatic segmentation shows greater potential and practical value in the differential diagnosis of PD and MSA in view of the advantages including time-saving, labor-saving, and high repeatability.