A Method for the Prediction of Clinical Outcome Using Diffusion Magnetic Resonance Imaging: Application on Parkinson’s Disease

Shu-Hang Ng,Yao-Liang Chen,Yu-Chun Lin,Po-Yuan Chen,Chin-Song Lu,Jur-Shan Cheng,Sung-Han Lin,Yi-Ming Wu,Chih-Chien Tsai,Jiun-Jie Wang,Yi-Hsin Weng

doi:10.3390/jcm9030647

Abstract

Robust early prediction of clinical outcomes in Parkinson’s disease (PD) is paramount for implementing appropriate management interventions. We propose a method that uses the baseline MRI, measuring diffusion parameters from multiple parcellated brain regions, to predict the 2-year clinical outcome in Parkinson’s disease. Diffusion tensor imaging was obtained from 82 patients (males/females = 45/37, mean age: 60.9 ± 7.3 years, baseline and after 23.7 ± 0.7 months) using a 3T MR scanner, which was normalized and parcellated according to the Automated Anatomical Labelling template. All patients were diagnosed with probable Parkinson’s disease by the National Institute of Neurological Disorders and Stroke criteria. Clinical outcome was graded using disease severity (Unified Parkinson’s Disease Rating Scale and Modified Hoehn and Yahr staging), drug administration (levodopa equivalent daily dose), and quality of life (39-item PD Questionnaire). Selection and regularization of diffusion parameters, the mean diffusivity and fractional anisotropy, were performed using least absolute shrinkage and selection operator (LASSO) between baseline diffusion index and clinical outcome over 2 years. Identified features were entered into a stepwise multivariate regression model, followed by a leave-one-out/5-fold cross validation and additional blind validation using an independent dataset. The predicted Unified Parkinson’s Disease Rating Scale for each individual was consistent with the observed values at blind validation (adjusted R2 0.76) by using 13 features, such as mean diffusivity in lingual, nodule lobule of cerebellum vermis and fractional anisotropy in rolandic operculum, and quadrangular lobule of cerebellum. We conclude that baseline diffusion MRI is potentially capable of predicting 2-year clinical outcomes in patients with Parkinson’s disease on an individual basis.

Highlights

Parkinson’s disease (PD) is a common progressive neurodegenerative disorder characterized by resting tremor, bradykinesia, restricted mobility, and postural instability
We first looked for any changes from baseline to the end of the study in clinical outcome, dose administration, and quality of life (Table 1)
The results of our study indicate that the clinical outcome over a 2-year follow-up period can be confidently predicted by baseline diffusion parameters measured in parcellated regions from the whole brain

Summary

Introduction

Parkinson’s disease (PD) is a common progressive neurodegenerative disorder characterized by resting tremor, bradykinesia, restricted mobility, and postural instability. The diagnosis of PD relies primarily on clinical signs and symptoms according to commonly accepted criteria [1]. PD has a progressive course [2] and is associated with increased mortality [3], with physical disabilities and non-motor symptoms exerting a significant negative impact on the quality of life [4]. In this context, robust early prediction of clinical outcomes would be paramount for implementing appropriate interventions. The assessment of clinical outcomes can be time-consuming and might fluctuate according to the patient’s conditions at the time of measurement

Methods

Results

Discussion

Conclusion