High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in 123^{123}I-Ioflupane SPECT Imaging

Early and accurate identification of parkinsonian syndromes (PS) involving presynaptic degeneration from non-degenerative variants such as Scans Without Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for effective patient management as the course, therapy and prognosis differ substantially between the two groups. In this study, we use Single Photon Emission Computed Tomography (SPECT) images from healthy normal, early PD and SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, and process them to compute shape- and surface fitting-based features for the three groups. We use these features to develop and compare various classification models that can discriminate between scans showing dopaminergic deficit, as in PD, from scans without the deficit, as in healthy normal or SWEDD. Along with it, we also compare these features with Striatal Binding Ratio (SBR)-based features, which are well-established and clinically used, by computing a feature importance score using Random forests technique. We observe that the Support Vector Machine (SVM) classifier gave the best performance with an accuracy of 97.29%. These features also showed higher importance than the SBR-based features. We infer from the study that shape analysis and surface fitting are useful and promising methods for extracting discriminatory features that can be used to develop diagnostic models that might have the potential to help clinicians in the diagnostic process.Comment: 9 pages, 5 figures, Accepted in the IEEE Journal of Biomedical and Health Informatics, Additional supplementary documents available at http://ieeexplore.ieee.org/document/7442754

The Parkinson's progression markers initiative (PPMI) - establishing a PD biomarker cohort.

ObjectiveThe Parkinson's Progression Markers Initiative (PPMI) is an observational, international study designed to establish biomarker-defined cohorts and identify clinical, imaging, genetic, and biospecimen Parkinson's disease (PD) progression markers to accelerate disease-modifying therapeutic trials.MethodsA total of 423 untreated PD, 196 Healthy Control (HC) and 64 SWEDD (scans without evidence of dopaminergic deficit) subjects were enrolled at 24 sites. To enroll PD subjects as early as possible following diagnosis, subjects were eligible with only asymmetric bradykinesia or tremor plus a dopamine transporter (DAT) binding deficit on SPECT imaging. Acquisition of data was standardized as detailed at www.ppmi-info.org.ResultsApproximately 9% of enrolled subjects had a single PD sign at baseline. DAT imaging excluded 16% of potential PD subjects with SWEDD. The total MDS-UPDRS for PD was 32.4 compared to 4.6 for HC and 28.2 for SWEDD. On average, PD subjects demonstrated 45% and 68% reduction in mean striatal and contralateral putamen Specific Binding Ratios (SBR), respectively. Cerebrospinal fluid (CSF) was acquired from >97% of all subjects. CSF (PD/HC/SWEDD pg/mL) α-synuclein (1845/2204/2141) was reduced in PD vs HC or SWEDD (P < 0.03). Similarly, t-tau (45/53) and p-tau (16/18) were reduced in PD versus HC (P < 0.01).InterpretationPPMI has detailed the biomarker signature for an early PD cohort defined by clinical features and imaging biomarkers. This strategy provides the framework to establish biomarker cohorts and to define longitudinal progression biomarkers to support future PD treatment trials

Is transcranial sonography useful to distinguish scans without evidence of dopaminergic deficit patients from Parkinson's disease?

BACKGROUND: Approximately 10% of patients clinically diagnosed with early Parkinson's disease (PD) subsequently have normal dopaminergic functional imaging. Transcranial sonography (TCS) has been shown to detect midbrain hyperechogenicity in approximately 90% of Parkinson's disease (PD) patients and 10% of the healthy population. The aim of this study was to investigate the prevalence of midbrain hyperechogenicity in patients with suspected parkinsonism and scans without evidence of dopaminergic deficit (SWEDD), in comparison to PD patients. METHODS: TCS was performed in 14 patients with SWEDD and 19 PD patients. RESULTS: There was a significantly increased area of echogenicity in the PD group (0.24 ± 0.06 cm(2) ), compared to the group of patients with SWEDD (0.13 ± 0.06 cm(2) ; P < 0.001). One (9.1%) of these patients, compared to 14 (82.5%) of the PD patients, was found to have hyperechogenicity (P < 0.001). CONCLUSIONS: We conclude that TCS is useful to distinguish PD patients from patients with suspected parkinsonism and SWEDD

Computer-aided diagnosis for (123I)FP-CIT imaging: impact on clinical reporting

BACKGROUND: For (123I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters' performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology. Three experienced (123I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1). Once completed, the process was then repeated (read 2). Immediately after submitting each image score for a second time, the CADx system output was displayed to reporters alongside the image data. With this information available, the reporters submitted a score for the third time (read 3). Comparisons between reads 1 and 2 provided evidence of intra-operator reliability, and differences between reads 2 and 3 showed the impact of the CADx. RESULTS: The performance of all reporters demonstrated a degree of variability when analysing images through visual analysis alone. However, inclusion of CADx improved consistency between reporters, for both clinical and research data. The introduction of CADx increased the accuracy of the radiologists when reporting (unfamiliar) research images but had less impact on the clinical scientist and caused no significant change in accuracy for the clinical data. CONCLUSIONS: The outcomes for this study indicate the value of CADx as a diagnostic aid in the clinic and encourage future development for more refined incorporation into clinical practice

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Diagnostic performance of the specific uptake size index for semi-quantitative analysis of I-123-FP-CIT SPECT: harmonized multi-center research setting versus typical clinical single-camera setting

Introduction: The specific uptake size index (SUSI) of striatal FP-CIT uptake is independent of spatial resolution in the SPECT image, in contrast to the specific binding ratio (SBR). This suggests that the SUSI is particularly appropriate for multi-site/multi-camera settings in which camera-specific effects increase inter-subject variability of spatial resolution. However, the SUSI is sensitive to inter-subject variability of striatum size. Furthermore, it might be more sensitive to errors of the estimate of non-displaceable FP-CIT binding. This study compared SUSI and SBR in the multi-site/multi-camera (MULTI) setting of a prospective multi-center study and in a mono-site/mono-camera (MONO) setting representative of clinical routine. Methods: The MULTI setting included patients with Parkinson’s disease (PD, n = 438) and healthy controls (n = 207) from the Parkinson Progression Marker Initiative. The MONO setting included 122 patients from routine clinical patient care in whom FP-CIT SPECT had been performed with the same double-head SPECT system according to the same acquisition and reconstruction protocol. Patients were categorized as “neurodegenerative” (n = 84) or “non-neurodegenerative” (n = 38) based on follow-up data. FP-CIT SPECTs were stereotactically normalized to MNI space. SUSI and SBR were computed for caudate, putamen, and whole striatum using unilateral ROIs predefined in MNI space. SUSI analysis was repeated in native patient space in the MONO setting. The area (AUC) under the ROC curve for identification of PD/“neurodegenerative” cases was used as performance measure. Results: In both settings, the highest AUC was achieved by the putamen (minimum over both hemispheres), independent of the semi-quantitative method (SUSI or SBR). The putaminal SUSI provided slightly better performance with ROI analysis in MNI space compared to patient space (AUC = 0.969 vs. 0.961, p = 0.129). The SUSI (computed in MNI space) performed slightly better than the SBR in the MULTI setting (AUC = 0.993 vs. 0.991, p = 0. 207) and slightly worse in the MONO setting (AUC = 0.969 vs. AUC = 0.976, p = 0.259). There was a trend toward larger AUC difference between SUSI and SBR in the MULTI setting compared to the MONO setting (p = 0.073). Variability of voxel intensity in the reference region was larger in misclassified cases compared to correctly classified cases for both SUSI and SBR (MULTI setting: p = 0.007 and p = 0.012, respectively). Conclusions: The SUSI is particularly useful in MULTI settings. SPECT images should be stereotactically normalized prior to SUSI analysis. The putaminal SUSI provides better diagnostic performance than the SUSI of the whole striatum. Errors of the estimate of non-displaceable count density in the reference region can cause misclassification by both SUSI and SBR, particularly in borderline cases. These cases might be identified by visual checking FP-CIT uptake in the reference region for particularly high variability

Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset