Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

Abstract

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.

Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson's Disease

“…Furthermore, apart from analyzing the selected radiomic features in this study, we also trained SVM to classify the subjects as IPD patients or HCs automatically. Recently, several studies have proved that it is effective to diagnose PD using SVM and different features (Prashanth et al, 2016; Amoroso et al, 2018; Castillo-Barnes et al, 2018) with an accuracy ranging from 0.70 to 0.96. However, few of them trained the SVM using the radiomic features as input.…”

“…SVM is one of the most popular machine learning methods and has been used in PD diagnosis (Prashanth et al, 2016; Amoroso et al, 2018; Castillo-Barnes et al, 2018). It tries to find out the optimal hyperplane that minimizes the classification error and maximizes the geometric margin on the training set, which leads to high generalization ability on the new cases (Burges, 1998).…”

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal 123I-FP-CIT indices

“…We here report a striking SVM classification accuracy (92.9%, AUC 0.97) in disentangling PS from CTL, with a major contribution of both uptake and asymmetry parameters. Previous SVM studies have already attempted to separate PD from CTL subjects using 123 I-FP-CIT SPECT striatal uptake and length/volume [36] or complex SPECT and biological data (including serum and CSF) from the Parkinson Progressive Markers Initiative cohort [19] with Acc 96–97%.…”

“…Moreover, postsynaptic D2/D3 18 F-desmethoxyfallypride (DMFP) PET SVM has shown 70–75% accuracy for the distinction of PD from MSA and PSP) [18]. Although these efforts are commendable, they also have inherent limitations—e.g., small sample sizes [20, 22], the debatable choice of merging several APS groups together [18], the presence of significant gray matter changes only in patients with long disease duration, when the added value as compared with clinical evaluation is limited [17] or the need for complex and time-consuming processing [19] that is not compatible with a clinical routine application.…”

The Challenge and Opportunity to Diagnose Parkinson's Disease in Midlife

“…Olfactory impairment/hyposmia, rapid-eye movement sleep behavior disorder (RBD), constipation, impairments in memory retrieval and decision making as well as depression (42)(43)(44)(45) often have a greater impact on the patient's quality of life than motor deficits (46). A number of these symptoms precede motor deficits by years or even decades (Figure 1) (23,(47)(48)(49), but due to the lack of specificity, diagnostic possibilities based on these markers are still limited (19). However, for research purposes, many markers have been summarized in the Movement Disorder Society (MDS) research criteria for prodromal Parkinson's disease (50), in order to estimate the probability of prodromal PD.…”