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

The Role of Dual-Phase 18 F-FP-CIT PET to Early Diagnosis of Corticobasal Syndrome

BACKGROUND

Corticobasal syndrome (CBS) is a neurodegeneration characterized by asymmetric parkinsonism, dystonia, myoclonus, and apraxia. In the early stage, CBS presents with asymmetric parkinsonism and cortical symptoms (apraxia and alien hand), and neuroimaging finding is often vague, making early clinical differentiation from idiopathic Parkinson disease (IPD) challenging. This study was performed to delineate the specific patterns of cortical hypoperfusion, dopamine transporter (DAT) uptake using dual-phase FP-CIT PET in discriminating between CBS and IPD at early stage.

PATIENTS AND METHODS

The study enrolled clinically diagnosed CBS (n = 11) and IPD (n = 22) patients (age and sex matched). All participants underwent dual-phase 18 F-FP-CIT PET, and regional SUV ratio (SUVR) was obtained by semiquantitative analysis. The early perfusion imaging and DAT imaging were compared between groups.

RESULTS

The regional SUVRs (early phase) of the frontal lobe, thalamus, cingulate, and caudate were significantly lower in patients with CBS, whereas the SUVR of occipital lobe was lower in the IPD group. The CBS group exhibited more prominent asymmetry than the IPD group, particularly in the perirolandic area, superior frontal gyrus, and anterior parietal lobe in early phase PET. Striatal DAT uptake (delayed phase) revealed that the caudate showed lower SUVR and prominent asymmetry in the CBS group, and the caudate-to-putamen ratio (CP ratio) was significantly lower in CBS patients ( P < 0.001). Among the parameters (early and delayed), the CP ratio in DAT exhibited the most powerful discriminative power from receiver operating characteristic curve comparison (area under curve = 0.983).

CONCLUSIONS

This study demonstrated that the dual-phase FP-CIT PET is useful in differentiating CBS and IPD in the early stage of the disease, and a lower CP ratio of DAT imaging is highly informative for distinguishing between corticobasal degeneration and IPD.

Dopaminergic imaging in degenerative parkinsonisms, an established clinical diagnostic tool

Parkinson's disease (PD) and other neurodegenerative parkinsonisms are characterised by loss of striatal dopaminergic neurons. Dopamine functional deficits can be measured in vivo using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) ligands assessing either presynaptic (e.g. dopamine synthesis and storage, transporter density) or postsynaptic terminals (i.e. D2 receptors availability). Nuclear medicine imaging thus helps the clinician to separate degenerative forms of parkinsonism with other neurological conditions, e.g. essential tremor or drug-induced parkinsonism. With the present study, we aimed at summarizing the current evidence about dopaminergic molecular imaging in the diagnostic evaluation of PD, atypical parkinsonian syndromes and dementia with Lewy bodies (DLB), as well as its potential to distinguish these conditions and to estimate disease progression. In fact, PET/SPECT methods are clinically validated and have been increasingly integrated into diagnostic guidelines (e.g. for PD and DLB). In addition, there is novel evidence on the classification properties of extrastriatal signal. Finally, dopamine imaging has an outstanding potential to detect neurodegeneration at the premotor stage, including REM-sleep behavior disorder and olfactory loss. Therefore, inclusion of subjects at an early stage for clinical trials can largely benefit from a validated in vivo biomarker such as presynaptic dopamine pathways PET/SPECT assessment.

Molecular Imaging in Parkinsonian Disorders—What’s New and Hot?

Highlights

Abstract

Neurodegenerative parkinsonian disorders are characterized by a great diversity of clinical symptoms and underlying neuropathology, yet differential diagnosis during lifetime remains probabilistic. Molecular imaging is a powerful method to detect pathological changes in vivo on a cellular and molecular level with high specificity. Thereby, molecular imaging enables to investigate functional changes and pathological hallmarks in neurodegenerative disorders, thus allowing to better differentiate between different forms of degenerative parkinsonism, improve the accuracy of the clinical diagnosis and disentangle the pathophysiology of disease-related symptoms. The past decade led to significant progress in the field of molecular imaging, including the development of multiple new and promising radioactive tracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) as well as novel analytical methods. Here, we review the most recent advances in molecular imaging for the diagnosis, prognosis, and mechanistic understanding of parkinsonian disorders. First, advances in imaging of neurotransmission abnormalities, metabolism, synaptic density, inflammation, and pathological protein aggregation are reviewed, highlighting our renewed understanding regarding the multiplicity of neurodegenerative processes involved in parkinsonian disorders. Consequently, we review the role of molecular imaging in the context of disease-modifying interventions to follow neurodegeneration, ensure stratification, and target engagement in clinical trials.

Differentiation of Parkinson’s disease and Parkinsonism predominant multiple system atrophy in early stage by morphometrics in susceptibility weighted imaging

Background and purpose

We previously established a radiological protocol to discriminate multiple system atrophy-parkinsonian subtype (MSA-P) from Parkinson’s disease (PD). However, we do not know if it can differentiate early stage disease. This study aimed to investigate whether the morphological and intensity changes in susceptibility weighted imaging (SWI) of the lentiform nucleus (LN) could discriminate MSA-P from PD at early stages.

Methods

We retrospectively enrolled patients with MSA-P, PD and sex- and age-matched controls whose brain MRI included SWI, between January 2015 and July 2020 at the Movement Disorder Center. Two specialists at the center reviewed the medical records and made the final diagnosis, and two experienced neuroradiologists performed MRI analysis, based on a defined and revised protocol for conducting morphological measurements of the LN and signal intensity.

Results

Nineteen patients with MSA-P and 19 patients with PD, with less than 2 years of disease duration, and 19 control individuals were enrolled in this study. We found that patients with MSA- P presented significantly decreased size in the short line (SL) and corrected short line (cSL), ratio of the SL to the long line (SLLr) and corrected SLLr (cSLLr) of the LN, increased standard deviation of signal intensity (SIsd_LN, cSIsd_LN) compared to patients with PD and controls (P < 0.05). With receiver operating characteristic (ROC) analysis, this finding had a sensitivity of 89.5% and a specificity of 73.7% to distinguish MSA- P from PD.

Conclusion

Compared to PD and controls, patients with MSA-P are characterized by a narrowing morphology of the posterior region of the LN. Quantitative morphological changes provide a reference for clinical auxiliary diagnosis.

Machine Learning for Early Parkinson’s Disease Identification within SWEDD Group Using Clinical and DaTSCAN SPECT Imaging Features

Early Parkinson’s Disease (PD) diagnosis is a critical challenge in the treatment process. Meeting this challenge allows appropriate planning for patients. However, Scan Without Evidence of Dopaminergic Deficit (SWEDD) is a heterogeneous group of PD patients and Healthy Controls (HC) in clinical and imaging features. The application of diagnostic tools based on Machine Learning (ML) comes into play here as they are capable of distinguishing between HC subjects and PD patients within an SWEDD group. In the present study, three ML algorithms were used to separate PD patients from HC within an SWEDD group. Data of 548 subjects were firstly analyzed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. Using the best reduction technique result, we built the following clustering models: Density-Based Spatial (DBSCAN), K-means and Hierarchical Clustering. According to our findings, LDA performs better than PCA; therefore, LDA was used as input for the clustering models. The different models’ performances were assessed by comparing the clustering algorithms outcomes with the ground truth after a follow-up. Hierarchical Clustering surpassed DBSCAN and K-means algorithms by 64%, 78.13% and 38.89% in terms of accuracy, sensitivity and specificity. The proposed method demonstrated the suitability of ML models to distinguish PD patients from HC subjects within an SWEDD group.

A hybrid cost-sensitive machine learning approach for the classification of intelligent disease diagnosis

Automatic risk classification of diseases is one of the most significant health problems in medical and healthcare domain. However, the related studies are relative scarce. In this paper, we design an intelligent diagnosis model based on optimal machine learning algorithms with rich clinical data. First, the disease risk classification problem based on machine learning is defined. Then, the K-means clustering algorithm is used to validate the class label of given data, thereby removing misclassified instances from the original dataset. Furthermore, naive Bayesian algorithm is applied to build the final classifier by using 10-fold cross-validation method. In addition, a novel class-specific attribute weighted approach is adopted to alleviate the conditional independence assumption of naive Bayes, which means we assign each disease attribute a specific weight for each class. Last but not least, a hybrid cost-sensitive disease risk classification model is formulated, and a practical example from the University of California Irvine (UCI) machine learning database is used to illustrate the potential of the proposed method. Experimental results demonstrate that the approach is competitive with the state-of-the-art classifiers.

Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn)

Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)—a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.

Computer-Aided Classification Framework of Parkinsonian Disorders Using 11C-CFT PET Imaging

Purpose

To investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on ¹¹C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (¹¹C-CFT) positron emission tomography (PET) imaging.

Methods

Patients with different forms of PDs—including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)—underwent dopamine transporter (DAT) imaging with ¹¹C-CFT PET. A novel multistep computer-aided classification framework—consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification—was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages.

Results

Accuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively—with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype.

Conclusions

The proposed computer-aided classification framework based on ¹¹C-CFT PET imaging holds promise for improving the differential diagnosis of PDs.

Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes

Purpose

Deep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes.

Methods

The study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as “normal” or “reduced” by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification.

Results

Overall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an “inconsistent” relevance map more typical for the true class label.

Conclusion

LRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of “inconsistent” relevance maps to identify misclassified cases requires further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05569-9.

Single Photon Emission Computed Tomography/Positron Emission Tomography Molecular Imaging for Parkinsonism: A Fast-Developing Field

The early differential diagnosis of Parkinson disease and atypical parkinsonism is a major challenge. The use of single photon emission computed tomography (SPECT)/positron emission tomography (PET) molecular imaging to investigate parkinsonism is a fast‐developing field. Imaging biomarker research may potentially lead to more accurate disease detection, enabling earlier diagnosis and treatment. This review summarizes recent SPECT/PET advances in radiopharmaceuticals and imaging technologies/analyses that improve the diagnosis of neurodegenerative parkinsonism. We are currently witnessing a turning point in the field. Integrating molecular imaging as a diagnostic technique represents an opportunity to reassess the strategies for diagnosing neurodegenerative parkinsonism. ANN NEUROL 2021;90:711–719

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Artificial intelligence for molecular neuroimaging

In recent years, artificial intelligence (AI) or the study of how computers and machines can gain intelligence, has been increasingly applied to problems in medical imaging, and in particular to molecular imaging of the central nervous system. Many AI innovations in medical imaging include improving image quality, segmentation, and automating classification of disease. These advances have led to an increased availability of supportive AI tools to assist physicians in interpreting images and making decisions affecting patient care. This review focuses on the role of AI in molecular neuroimaging, primarily applied to positron emission tomography (PET) and single photon emission computed tomography (SPECT). We emphasize technical innovations such as AI in computed tomography (CT) generation for the purposes of attenuation correction and disease localization, as well as applications in neuro-oncology and neurodegenerative diseases. Limitations and future prospects for AI in molecular brain imaging are also discussed. Just as new equipment such as SPECT and PET revolutionized the field of medical imaging a few decades ago, AI and its related technologies are now poised to bring on further disruptive changes. An understanding of these new technologies and how they work will help physicians adapt their practices and succeed with these new tools.

Automatic classification of idiopathic Parkinson's disease and atypical Parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry

Objective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [¹¹C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [¹¹C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson's disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study

PURPOSE

Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Parkinson's disease (PD) from non-PD and to determine its generalizability and clinical value in two centers.

METHODS

We retrospectively included 210 consecutive patients who underwent I-123 FP-CIT SPECT imaging and had a clinically confirmed diagnosis. Linear support vector machine (SVM) was used to build a classification model to discriminate PD from non-PD based on I-123-FP-CIT striatal uptake ratios, age and gender of 90 patients. The model was validated on unseen data from the same center where the model was developed (n = 40) and consecutively on data from a different center (n = 80). Prediction performance was assessed and compared to the scan interpretation by expert physicians.

RESULTS

Testing the derived SVM model on the unseen dataset (n = 40) from the same center resulted in an accuracy of 95.0%, sensitivity of 96.0% and specificity of 93.3%. This was identical to the classification accuracy of nuclear medicine physicians. The model was generalizable towards the other center as prediction performance did not differ thereby obtaining an accuracy of 82.5%, sensitivity of 88.5% and specificity of 71.4% (p = NS). This was comparable to that of nuclear medicine physicians (p = NS).

CONCLUSION

ML-based interpretation of I-123-FP-CIT scans results in accurate discrimination of PD from non-PD similar to visual assessment in both centers. The derived SVM model is therefore generalizable towards centers using comparable acquisition and image processing methods and implementation as diagnostic aid in clinical practice is encouraged.

Using Artificial Neural Network to Discriminate Parkinson's Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

Artificial intelligence techniques support nuclear medicine modalities to improve the diagnosis of Parkinson’s disease and Parkinsonian syndromes

Purpose

The aim of this review is to discuss the most significant contributions about the role of Artificial Intelligence (AI) techniques to support the diagnosis of movement disorders through nuclear medicine modalities.

Methods

The work is based on a selection of papers available on PubMed, Scopus and Web of Sciences. Articles not written in English were not considered in this study.

Results

Many papers are available concerning the increasing contribution of machine learning techniques to classify Parkinson’s disease (PD), Parkinsonian syndromes and Essential Tremor (ET) using data derived from brain SPECT with dopamine transporter radiopharmaceuticals. Other papers investigate by AI techniques data obtained by 123I-MIBG myocardial scintigraphy to differentially diagnose PD and other Parkinsonian syndromes.

Conclusion

The recent literature provides strong evidence that AI techniques can play a fundamental role in the diagnosis of movement disorders by means of nuclear medicine modalities, therefore paving the way towards personalized medicine.

A Shape Approximation for Medical Imaging Data

This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson’s disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ± 0.04 classification accuracy, 0.87 ± 0.06 sensitivity, and 0.88 ± 0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification.

Machine Learning in Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects

This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algorithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.

Imaging of dopamine transporters in Parkinson disease: a meta-analysis of 18 F/123 I-FP-CIT studies

OBJECTIVE

¹⁸ F-FP-CIT and ¹²³ I-FP-CIT are widely used radiotracers in molecular imaging for Parkinson's disease (PD) diagnosis. Compared with ¹²³ I-FP-CIT, ¹⁸ F-FP-CIT has superior tracer kinetics. We aimed to conduct a meta-analysis to assess the efficacy of using ¹⁸ F-FP-CIT positron emission tomography (PET) and ¹²³ I-FP-CIT single-photon emission computed tomography (SPECT) of dopamine transporters in patients with PD in order to provide evidence for clinical decision-making.

METHODS

We searched the PubMed, Embase, Wanfang Data, and China National Knowledge Infrastructure databases to identify the relevant studies from the time of inception of the databases to 30 April 2020. We identified six PET studies, including 779 patients with PD and 124 healthy controls, which met the inclusion criteria. Twenty-seven SPECT studies with 1244 PD patients and 859 controls were also included in this meta-analysis.

RESULTS

Overall effect-size analysis indicated that patients with PD showed significantly reduced ¹⁸ F-FP-CIT uptake in three brain regions [caudate nucleus: standardized mean difference (SMD) = -1.71, Z = -3.31, P = 0.0009; anterior putamen: SMD = -3.71, Z = -6.26, P < 0.0001; and posterior putamen: SMD = -5.49, Z = -5.97, P < 0.0001]. Significant decreases of ¹²³ I-FP-CIT uptake were also observed in the caudate (SMD = -2.31, Z = -11.49, P < 0.0001) and putamen (SMD = -3.25, Z = -14.79, P < 0.0001).

INTERPRETATION

In conclusion, our findings indicate that both ¹⁸ F-FP-CIT PET and ¹²³ I-FP-CIT SPECT imaging of dopamine transporters can provide viable biomarkers for early PD diagnosis.

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson’s Disease

Finding new biomarkers to model Parkinson’s Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[Formula: see text]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of [Formula: see text] scans from Parkinson’s Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Naïve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP).

The proposed system, based on a Mann–Whitney–Wilcoxon U-Test for feature selection and the SVM approach, yielded a [Formula: see text] balanced accuracy when the performance was evaluated using a [Formula: see text]-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity, among others, but including both internal and external isosurfaces.