An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI.

Zhenyu Shu,Zhenyu Shu,Peipei Pang,Minming Zhang,Sijia Cui,Xiao Wu,Yuyun Xu

doi:10.3389/fnagi.2020.548616

Abstract

Purpose: To develop and validate an integrative nomogram based on white matter (WM) radiomics biomarkers and nonmotor symptoms for the identification of early-stage Parkinson's disease (PD).Methods: The brain magnetic resonance imaging (MRI) and clinical characteristics of 336 subjects, including 168 patients with PD, were collected from the Parkinson's Progress Markers Initiative (PPMI) database. All subjects were randomly divided into training and test sets. According to the baseline MRI scans of patients in the training set, the WM was segmented to extract the radiomic features of each patient and develop radiomics biomarkers, 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 patients with atypical PD who had imaging scans without evidence of dopaminergic deficit (SWEDD) to verify whether the nomogram was able to distinguish patients with typical PD from patients with SWEDD. A decision curve analysis was also performed to validate the clinical practicality of the nomogram.Results: The area under the curve values of the integrative nomogram for the training, testing and verification sets were 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. A significant difference in the number of patients with PD was observed between the high-risk group and the low-risk group based on the nomogram (P < 0.05).Conclusion: This integrative nomogram is a new potential method to identify patients with early-stage PD.

Highlights

Parkinson’s disease (PD) is a common age-related progressive neurodegenerative disease (Dorsey et al, 2007)
According to the baseline magnetic resonance imaging (MRI) scans of patients in the training set, the white matter (WM) was segmented to extract the radiomic features of each patient and develop radiomics biomarkers, which were combined with nonmotor symptoms to build an integrative nomogram using machine learning
A significant difference in the number of patients with PD was observed between the high-risk group and the low-risk group based on the nomogram (P < 0.05)

Summary

Introduction

Parkinson’s disease (PD) is a common age-related progressive neurodegenerative disease (Dorsey et al, 2007). The diagnosis of PD mainly depends on the patient’s medical history and clinical symptoms; the early stages of PD can include many atypical symptoms such as sleep disorders, decreased olfactory function and cognitive disturbances, and these nonmotor symptoms often precede clinical motor signs (Mielke and Maetzler, 2014). The cardinal and defining nonmotor symptoms used for the early diagnosis of PD in the clinic, the symptoms that are typical of the early stages, occur in patients with other disorders (Trojano and Papagno, 2018; De Pablo-Fernández et al, 2019), and the diagnostic error rate is as high as 25% among practitioners with limited clinical experience in diagnosing early-stage of PD (Miller and O’Callaghan, 2015). It is very challenging to diagnose early-stage PD based on current diagnostic standards

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