Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach.
Purpose: Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an a...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 47; no. 12; pp. 2901 - 2911 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146479565&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146479565 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Nov2020 vid: 47 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146479565 144100051 10.1007/s00259-020-04785-z 146479565 ppf: 2901 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach. aug: au: Rus, Tomaž Tomše, Petra Jensterle, Luka Grmek, Marko Pirtošek, Zvezdan Eidelberg, David Tang, Chris Trošt, Maja affil: Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia sug: ab: Purpose: Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a "real-life" clinical setting. Methods: One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. Results: Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. Conclusion: The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10–15% in PD and 20% in APS when a movement disorder specialist is not easily available. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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