Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [¹⁸F]FDG PET imaging.

Abstract

We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [¹⁸F]fluorodeoxyglucose (FDG) PET images. In this two-center study, 255 normal controls (NCs) and 103 PD patients were enrolled from Huashan Hospital, China; 26 NCs and 22 PD patients were enrolled as a separate test group from Wuxi 904 Hospital, China. The proposed DLR model consisted of a convolutional neural network-based feature encoder and a support vector machine (SVM) model-based classifier. The DLR model was trained and validated in the Huashan cohort and tested in the Wuxi cohort, and accuracy, sensitivity, specificity and receiver operator characteristic (ROC) curve graphs were used to describe the model's performance. Comparative experiments were performed based on four other models including the scale model, radiomics model, standard uptake value ratio (SUVR) model and DLR model. The DLR model demonstrated superiority in differentiating PD patients and NCs in comparison to other models, with an accuracy of 95.17% [90.35%, 98.13%] (95% confidence intervals, CI) in the Huashan cohort. Moreover, the DLR model also demonstrated greater performance in diagnosing PD early than routine methods, with an accuracy of 85.58% [78.60%, 91.57%] in the Huashan cohort. We developed a DLR model based on [¹⁸F]FDG PET images that showed good performance in the noninvasive, individualized prediction of PD and was superior to traditional handcrafted methods. This model has the potential to guide and facilitate clinical diagnosis and contribute to the development of precision treatment. The DLR method on [¹⁸F]FDG PET images helps clinicians to diagnose PD and PD subgroups from normal controls. A prospective two-center study showed that the DLR method provides greater diagnostic accuracy.