An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI - Shu, Zhenyu; Pang, Peipei; Wu, Xiao; Cui, Sijia; Xu, Yuyun; Zhang, Minming (Frontiers in Aging Neuroscience, 2020)
Purpose: To develop and validate an integrative nomogram for identifying early-stage Parkinson's disease (PD) based on nonmotor symptoms and radiomics white matter (WM) biomarkers derived from whole-brain MRI. Methods: The brain MRI and clinical characteristics of 336 subjects, including 168 PD patients, were collected from the Parkinson's Progress biomarkers Initiative (PPMI) database. All subjects were randomly divided into training and test sets. Based on the baseline MRI scans of the training set, the WM was segmented to extract the radiomics features of each patient, which were then combined with nonmotor symptoms to build an integrative nomogram using machine learning. Finally, the diagnostic accuracy and reliability of the nomogram were evaluated using a receiver operating characteristic curve and test data, respectively. In addition, we investigated 58 atypical PD patients who had imaging scans without evidence of a dopaminergic deficit (SWEDD) to verify whether the nomogram could distinguish typical PD patients from those with SWEDD. A decision curve analysis was also used to validate the clinical practicality of the nomogram. Results: The area under the curve of the integrative nomogram for the training, testing and verification sets was 0.937, 0.922 and 0.836, respectively; the specificity values were 83.8%, 88.2% and 91.38%, respectively; and the sensitivity values were 84.6%, 82.4% and 70.69%, respectively. There was a significant difference in the number of patients with PD between the high-risk group and the low-risk group based on the nomogram (P < 0.05). Conclusion: The integrative nomogram may provide a potential new method to identify early-stage PD.