Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps.
Purpose: An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subjec...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 8; pp. 2486 - 2500 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2021
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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=152814331&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152814331 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: Jul2021 vid: 48 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152814331 148026224 10.1007/s00259-020-05175-1 152814331 ppf: 2486 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps. aug: au: Caminiti, Silvia Paola Sala, Arianna Presotto, Luca Chincarini, Andrea Sestini, Stelvio Perani, Daniela Schillaci, Orazio Berti, Valentina Calcagni, Maria Lucia Cistaro, Angelina Morbelli, Silvia Nobili, Flavio Pappatà, Sabina Volterrani, Duccio Gobbo, Clara Luigia affil: Vita-Salute San Raffaele University, Milan, Italy sug: ab: Purpose: An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level. Methods: Selection of HC images was based on visual rating, after Cook's distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB. Results: Two-step Cook's distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes. Conclusions: The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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