Dataset Information

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.

SUBMITTER: Castillo-Barnes D

PROVIDER: S-EPMC6102321 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Castillo-Barnes Diego D Ramírez Javier J Segovia Fermín F Martínez-Murcia Francisco J FJ Salas-Gonzalez Diego D Górriz Juan M JM

Frontiers in neuroinformatics 20180814

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 ...[more]

PMID: 30154711

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Project description:No disease modifying therapies for Parkinson's disease (PD) have been found effective to date. To properly power clinical trials for discovery of such therapies, the ability to predict outcome in PD is critical, and there is a significant need for discovery of prognostic biomarkers of PD. Dopamine transporter (DAT) SPECT imaging is widely used for diagnostic purposes in PD. In the present work, we aimed to evaluate whether longitudinal DAT SPECT imaging can significantly improve prediction of outcome in PD patients. In particular, we investigated whether radiomics analysis of DAT SPECT images, in addition to use of conventional non-imaging and imaging measures, could be used to predict motor severity at year 4 in PD subjects. We selected 64 PD subjects (38 male, 26 female; age at baseline (year 0): 61.9 ± 7.3, range [46,78]) from the Parkinson's Progressive Marker Initiative (PPMI) database. Inclusion criteria included (i) having had at least 2 SPECT scans at years 0 and 1 acquired on a similar scanner, (ii) having undergone a high-resolution 3 T MRI scan, and (iii) having motor assessment (MDS-UPDRS-III) available in year 4 used as outcome measure. Image analysis included automatic region-of-interest (ROI) extraction on MRI images, registration of SPECT images onto the corresponding MRI images, and extraction of radiomic features. Non-imaging predictors included demographics, disease duration as well as motor and non-motor clinical measures in years 0 and 1. The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77]). The RF prediction was evaluated using leave-one-out cross-validation. Our results demonstrated that addition of radiomic features to conventional measures significantly improved (p < 0.001) prediction of outcome, reducing the absolute error of predicting MDS-UPDRS-III from 9.00 ± 0.88 to 4.12 ± 0.43. This shows that radiomics analysis of DAT SPECT images has a significant potential towards development of effective prognostic biomarkers in PD.

Project description:Background: In clinical practice, assessment of the striatal accumulation in 123I-ioflupane single photon emission computed tomography (SPECT) is commonly performed calculating the specific binding ratio (SBR) for the whole striatum. On the other hand, visual assessment of striatal accumulation in the SPECT was recently established. However, correlations of visual assessment with motor and cognitive functions in Parkinson's disease (PD) have rarely been examined. Differences in the usefulness of these assessments at clinics are uncertain. Objective: We performed this study to compare correlations of cognitive and motor functions in drug-naive PD between the SBR and visual assessment using 123I-ioflupane SPECT. Methods: Cognitive and motor assessments and 123I-ioflupane SPECT were performed in 47 drug-naïve PD patients with Mini-mental State Examination scores of ≥25. Cognitive function was assessed using the total score and 6 subscores of the Montreal Cognitive Assessment (MoCA) and 10 separate subtests of the Neurobehavioral Cognitive Status Examination (COGNISTAT). Motor function was assessed using the Hoehn and Yahr scale and Unified Parkinson's Disease Rating Scale. Accumulation of 123I-ioflupane was determined by visual assessment based on five grades: 1, burst striatum; 2, egg-shaped; 3, mixed type; 4, eagle wing; 5, normal striatum; and by calculating SBR averaged for the bilateral striatum using the DaTView computer software commonly used in clinical practice. Each SPECT assessment was compared with each subscore for cognitive and motor assessments. Results: Spearman correlation analysis showed SBR was significantly correlated with the MoCA subscores of visuospatial function and attention, and with COGNISTAT subtests of attention. Visual assessment showed significant negative correlation with the Hoehn and Yahr scale. Mean score of postural instability in patients with visual grade of 1 was significantly higher than those in patients with visual grades of 2 and 3. Conclusion: Clinical symptoms reflected by 123I-ioflupane SPECT differ between the SBR and visual assessment. SBR reflects some cognitive functions, whereas a visual assessment grade of 1, which signifies decreased uptake of 123I-Ioflupane in the caudate nucleus, reflects postural instability. Thus, the caudate nucleus may play an important role in posture maintenance. Our results suggest that performing both assessments is of value.

Dataset Information

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

Publications

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

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