Diagnostic and prognostic value of amyloid PET textural and shape features: comparison with classical semi-quantitative rating in 760 patients from ... View Full Text


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Article Info

DATE

2019-02

AUTHORS

Fayçal Ben Bouallègue, Fabien Vauchot, Denis Mariano-Goulart, Pierre Payoux

ABSTRACT

We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer's disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer's Disease Neuroimaging Initiative with available baseline 18F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up. PET images were normalized to MNI space and post-processed using in-house software. Relative retention indices (SUVr) were computed with respect to pontine, cerebellar, and composite reference regions. Several textural and shape features were extracted then combined using a support vector machine (SVM) to build a predictive model of AD conversion. Diagnostic and prognostic performance was evaluated using ROC analysis and survival analysis with the Cox proportional hazard model. The three SUVr and all the tested features effectively discriminated AD subjects in cross-sectional analysis (all p < 0.001). In longitudinal analysis, the variables with the highest prognostic value were composite SUVr (AUC 0.86; accuracy 81%), skewness (0.87; 83%), local minima (0.85; 79%), Geary's index (0.86; 81%), gradient norm maximal argument (0.83; 82%), and the SVM model (0.91; 86%). The adjusted hazard ratio for AD conversion was 5.5 for the SVM model, compared with 4.0, 2.6, and 3.8 for cerebellar, pontine and composite SUVr (all p < 0.001), indicating that appropriate amyloid textural and shape features predict conversion to AD with at least as good accuracy as classical SUVr. More... »

PAGES

111-125

References to SciGraph publications

  • 2017-12. Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer’s disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database in ALZHEIMER'S RESEARCH & THERAPY
  • 2009-06. Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis in EUROPEAN RADIOLOGY
  • 2016-12. The asphericity of the metabolic tumour volume in NSCLC: correlation with histopathology and molecular markers in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2011-04. PET amyloid imaging as a tool for early diagnosis and identifying patients at risk for progression to Alzheimer's disease in ALZHEIMER'S RESEARCH & THERAPY
  • 2013-01. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2014-09. Florbetapir F 18 amyloid PET and 36-month cognitive decline:a prospective multicenter study in MOLECULAR PSYCHIATRY
  • 2013-06. Perfusion-like template and standardized normalization-based brain image analysis using 18F-florbetapir (AV-45/Amyvid) PET in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2014-07. Insight on AV-45 binding in white and grey matter from histogram analysis: a study on early Alzheimer’s disease patients and healthy subjects in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2016-12. Reference tissue normalization in longitudinal 18F-florbetapir positron emission tomography of late mild cognitive impairment in ALZHEIMER'S RESEARCH & THERAPY
  • 2014-05. Comparison between PET template-based method and MRI-based method for cortical quantification of florbetapir (AV-45) uptake in vivo in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11682-018-9833-0

    DOI

    http://dx.doi.org/10.1007/s11682-018-9833-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1100917155

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29427064


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    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11682-018-9833-0'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11682-018-9833-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11682-018-9833-0'

    RDF/XML is a standard XML format for linked data.

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