Catharina Lange1, Per Suppa1,2, Lars Frings3, Winfried Brenner1, Lothar Spies2, Ralph Buchert1. 1. Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany. 2. jung diagnostics GmbH, Hamburg, Germany. 3. Department of Nuclear Medicine, University of Freiburg, Freiburg, Germany.
Abstract
BACKGROUND: Positron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved. OBJECTIVE: To optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI). METHODS: The study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure. RESULTS: The default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048). CONCLUSION: The prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.
BACKGROUND: Positron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved. OBJECTIVE: To optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI). METHODS: The study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure. RESULTS: The default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048). CONCLUSION: The prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.
Authors: Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; John C Morris; Ronald C Petersen; Andrew J Saykin; Leslie M Shaw; Arthur W Toga; John Q Trojanowski Journal: Alzheimers Dement Date: 2017-03-22 Impact factor: 21.566
Authors: Ivayla Apostolova; Catharina Lange; Per Suppa; Lothar Spies; Susanne Klutmann; Gerhard Adam; Michel J Grothe; Ralph Buchert Journal: Eur J Nucl Med Mol Imaging Date: 2018-03-03 Impact factor: 9.236
Authors: Martin Mamach; Florian Wilke; Martin Durisin; Frank A Beger; Mareike Finke; Andreas Büchner; Barbara Schultz; Arthur Schultz; Lilli Geworski; Frank M Bengel; Thomas Lenarz; Anke Lesinski-Schiedat; Georg Berding Journal: EJNMMI Res Date: 2018-02-05 Impact factor: 3.138
Authors: Marco Pagani; Gianluca Castelnuovo; Andrea Daverio; Patrizia La Porta; Leonardo Monaco; Fabiola Ferrentino; Agostino Chiaravalloti; Isabel Fernandez; Giorgio Di Lorenzo Journal: Front Psychol Date: 2018-04-16