Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis - Shi, Dafa; Zhang, Haoran; Wang, Guangsong; Wang, Siyuan; Yao, Xiang; Li, Yanfei; Guo, Qiu; Zheng, Shuang; Ren, Ke (Frontiers in Aging Neuroscience, 2022)
Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging in clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgnetly needed. In our study, we analysed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance (accuracy = 81.45%, area under the curve [AUC] = 0.850) in the primary set and good generalization in the external validation set (accuracy = 67.44%, AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.