Use of deep learning-based radiomics to differentiate Parkinson’s disease patients from normal controls: a study based on [18F]FDG PET imaging
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-022-08799-z.pdf
Reference31 articles.
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2. Rahmim A, Huang P, Shenkov N et al (2017) Improved prediction of outcome in Parkinson’s disease using radiomics analysis of longitudinal DAT SPECT images. Neuroimage Cl 16:539–544. https://doi.org/10.1016/j.nicl.2017.08.021
3. Hussl A, Seppi K, Poewe W (2013) Nonmotor symptoms in Parkinson’s disease. Expert Rev Neurother 13:581–583. https://doi.org/10.1586/ern.13.53
4. Chaudhuri KR, Martinez-Martin P, Schapira AHV et al (2006) International multicenter pilot study of the first comprehensive self-completed nonmotor symptoms questionnaire for Parkinson’s disease: the NMSQuest study. Mov Disord 21:916–923. https://doi.org/10.1002/mds.20844
5. Müller B, Assmus J, Herlofson K et al (2013) Importance of motor vs. non-motor symptoms for health-related quality of life in early Parkinson’s disease. Parkinsonism Relat Disord 19:1027–1032. https://doi.org/10.1016/j.parkreldis.2013.07.010
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