Differential Diagnosis of Patients with Inconclusive Parkinsonian Features Using [18F]FP-CIT PET/CT

Park, Eunkyung; Hwang, Yu Mi; Lee, Chan Nyoung; Kim, Sujin; Oh, Sun Young; Kim, Young Chul; Choe, Jae Gol; Park, Kun Woo

doi:10.1007/s13139-013-0253-1

Cited by 16 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid SPECT/CT and PET/CT platforms were the earliest examples of these platforms and allowed for improved integration of structural (CT) and metabolic (PET/SPECT) imaging data (Basu & Alavi, 2008). Some PD studies have employed hybrid 18 F-FP-CIT or 18 F-FDOPA PET/CT (Bhidayasiri et al, 2012; Park et al, 2014; Song et al, 2014) or 123 I-FP-CIT SPECT/CT (Sydoff et al, 2013)…”

Section: Neuroimaging Methodsmentioning

confidence: 99%

Neuroimaging of Parkinson's disease: Expanding views

Weingarten

Sundman

Hickey

et al. 2015

Neuroscience & Biobehavioral Reviews

137

View full text Add to dashboard Cite

Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson’s disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from L-DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD.

show abstract

Section: Neuroimaging Methodsmentioning

confidence: 99%

Neuroimaging of Parkinson's disease: Expanding views

Weingarten

Sundman

Hickey

et al. 2015

Neuroscience & Biobehavioral Reviews

137

View full text Add to dashboard Cite

show abstract

“…Over the last several decades, the search for biomarkers that have diagnostic and prognostic use in neurodegenerative diseases has grown exponentially [5,10,11] with most work focusing on neuroimaging and cerebrospinal (CSF) methods [5,10–14] and increasingly clinical‐genetic algorithms [15,16]. In fact, amyloid‐beta (Aβ) positron emission tomography (PET) scanning tracers and CSF assays have been approved by the Food and Drug Administration for use in the diagnostic process for Alzheimer's disease (AD), and dopamine transporter single photon emission computed tomography [17] has been established for PD. Recent work suggests CSF markers may also have use in the differential diagnosis of neurodegenerative diseases [18].…”

Section: Introductionmentioning

confidence: 99%

Potential two‐step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

O’Bryant

Edwards

Zhang

et al. 2019

Alz & Dem Diag Ass & Dis Mo

View full text Add to dashboard Cite

Introduction We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

show abstract

“…Radionuclide imaging with PET and SPECT in conjunction with dopaminergic tracers has also been proposed for this purpose (7,8). The differential diagnosis of parkinsonian disorders based on these methods has generally relied on the analysis of imaging signal from single brain regions, most notably the basal ganglia.…”

mentioning

confidence: 99%

Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study

et al. 2015

View full text Add to dashboard Cite

The differentiation of idiopathic Parkinson disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian look-alike syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations.In an initial study, we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based on the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered. Methods: We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 y or less at the time of imaging. After imaging, subjects were followed by blinded movement disorders specialists for an average of 2.2 y before final diagnosis was made. When the algorithm was applied to the individual scan data, the probabilities of IPD, MSA, and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared with the final clinical diagnosis. Results: IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV) using the original 2-level logistic classification algorithm. The algorithm achieved 90% specificity and 85% PPV for MSA and 94% specificity and 94% PPV for PSP. The diagnostic accuracy was similarly high (specificity and PPV . 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as level I indeterminate parkinsonism and 4 more subjects as level II indeterminate APS. Conclusion: Automated pattern-based image classification can improve the diagnostic accuracy in patients with parkinsonism, even at early disease stages.

show abstract

Differential Diagnosis of Patients with Inconclusive Parkinsonian Features Using [18F]FP-CIT PET/CT

Cited by 16 publications

References 28 publications

Neuroimaging of Parkinson's disease: Expanding views

Neuroimaging of Parkinson's disease: Expanding views

Potential two‐step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study

Contact Info

Product

Resources

About