Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Annals of Nuclear Medicine
Published in: Annals of Nuclear Medicine 8/2020

01-08-2020 | Alzheimer's Disease | Original Article

Brain 18F-FDG PET analysis via interval-valued reconstruction: proof of concept for Alzheimer’s disease diagnosis

Authors: Florentin Kucharczak, Marie Suau, Olivier Strauss, Fayçal Ben Bouallègue, Denis Mariano-Goulart

Published in: Annals of Nuclear Medicine | Issue 8/2020

Login to get access

Abstract

Objective

We propose an innovative approach for 18F-FDG PET analysis based on an interval-valued reconstruction of 18F-FDG brain distribution. Its diagnostic performance for Alzheimer’s disease (AD) diagnosis with comparison to a validated post-processing software was assessed.

Methods

Brain 18F-FDG PET data from 26 subjects were acquired in a clinical routine setting. Raw data were reconstructed using an interval-valued version of the ML–EM algorithm called NIBEM that stands for Non-Additive interval-based expectation maximization. Subject classification was obtained via interval-based statistical comparison (intersection ratio, IR) between cortical regions of interest (ROI) including parietal, temporal, and temporo-mesial cortices and a reference region, the sub-cortical grey nuclei, known not to be affected by AD. In parallel, PET images were post-processed using a validated automated software based on the computation of ROI normalized uptake ratios standard deviation (SUVr SD) with reference to a healthy control database (Siemens Scenium). Clinical diagnosis made during follow-up was considered as the gold-standard for patient classification (16 healthy controls and 10 AD patients).

Results

Both methods provided cortical ROI indices that were significantly different between controls and AD patients. The area under the ROC curve for control/AD classification was statistically identical (0.96 for NIBEM IR and 0.95 for Scenium SUVr SD). At the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were, respectively, 100%, 88%, 92%, 83%, and 100% for both Scenium SUVr SD and NIBEM IR methods.

Conclusion

This preliminary study shows that interval-valued reconstruction allows self-consistent analysis of brain 18F-FDG PET data, yielding diagnostic performances that seem promising with respect to those of a commercial post-processing software based on SUVr SD analysis.
Footnotes
Literature
1.
Prince M, Wimo AGM, Ali GC, Wu YT, Prina M. World Alzheimer Report 2015: the global impact of dementia: an analysis of prevalence, incidence, cost and trends. London: Alzheimer’s Disease International; 2015.
2.
Castro DM, Dillon C, Machnicki G, Allegri RF. The economic cost of Alzheimer’s disease: family or public health burden? Dement Neuropsychol. 2010;4(4):262–7.PubMedPubMedCentralCrossRef
3.
Bloudek LM, Spackman DE, Blankenburg M, Sullivan SD. Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer’s disease. J Alzheimers Dis. 2011;26(4):627–45.PubMedCrossRef
4.
Perani D, Cerami C, Caminiti SP, Santangelo R, Coppi E, Ferrari L, et al. Cross-validation of biomarkers for the early differential diagnosis and prognosis of dementia in a clinical setting. Eur J Nucl Med Mol Imaging. 2016;43(3):499–508.PubMedCrossRef
5.
Siderowf A, Aarsland D, Mollenhauer B, Goldman JG, Ravina B. Biomarkers for cognitive impairment in Lewy body disorders: btatus and relevance for clinical trials: biomarkers of cognitive impairment. Mov Disord. 2018;33(4):528–36.PubMedCrossRef
6.
Petrella JR. Neuroimaging and the search for a cure for Alzheimer disease. Radiology. 2013;269(3):671–91.PubMedCrossRef
7.
Nasrallah IM, Wolk DA. Multimodality imaging of Alzheimer disease and other neurodegenerative dementias. J Nucl Med. 2014;55(12):2003–111.PubMedCrossRef
8.
Bohnen NI, Djang DS, Herholz K, Anzai Y, Minoshima S. Effectiveness and safety of 18F-FDG PET in the evaluation of dementia: a review of the recent literature. J Nucl Med. 2012;53(1):59–71.PubMedCrossRef
9.
Kato T, Inui Y, Nakamura A, Ito K. Brain fluorodeoxyglucose (FDG) PET in dementia. Ageing Res Rev. 2016;30:73–84.PubMedCrossRef
10.
Nestor PJ, Altomare D, Festari C, Drzezga A, Rivolta J, Walker Z, Bouwman F, Orini S, Law I, Agosta F, Arbizu J, Boccardi M, Nobili F, Frisoni GB. Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia. Eur J Nucl Med Mol Imaging. 2018;45(9):1509–25.PubMedCrossRef
11.
Ng S, Villemagne VL, Berlangieri S, Lee ST, Cherk M, Gong SJ, et al. Visual assessment versus quantitative assessment of 11C-PIB PET and 18F-FDG PET for detection of Alzheimer’s disease. J Nucl Med. 2007;48(4):547–52.PubMedCrossRef
12.
Mosconi L, Mistur R, Switalski R, Tsui WH, Glodzik L, Li Y, et al. FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2009;36(5):811–22.PubMedPubMedCentralCrossRef
13.
Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al. EANM procedure guidelines for PET brain imaging using 18F-FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;12:2103–10.CrossRef
14.
Giovacchini G, Squitieri F, Esmaeilzadeh M, Milano A, Mansi L, Ciarmiello A. PET translates neurophysiology into images: a review to stimulate a network between neuroimaging and basic research. J Cell Physiol. 2011;226(4):948–61.PubMedCrossRef
15.
Anchisi D, Borroni B, Franceschi M, Kerrouche N, Kalbe E, Beuthien-Beumann B, et al. Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol. 2005;62(11):1728–33.PubMedCrossRef
16.
Silverman DH, Small GW, Chang CY, Lu CS, De Kung AMA, Chen W, et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA. 2001;286(17):2120–7.PubMedCrossRef
17.
Grimmer T, Wutz C, Alexopoulos P, Drzezga A, Förster S, Förstl H, et al. Visual versus fully automated analyses of 18F-FDG and amyloid PET for prediction of dementia due to Alzheimer disease in mild cognitive impairment. J Nucl Med. 2016;57(2):204–7.PubMedCrossRef
18.
Lehman VT, Carter RE, Claassen DO, Murphy RC, Lowe V, Petersen RC, et al. Visual assessment versus quantitative three-dimensional stereotactic surface projection fluorodeoxyglucose positron emission tomography for detection of mild cognitive impairment and Alzheimer disease. Clin Nucl Med. 2012;37(8):721–6.PubMedCrossRef
19.
Morbelli S, Brugnolo A, Bossert I, Buschiazzo A, Frisoni GB, Galluzzi S, et al. Visual versus semi-quantitative analysis of 18F-FDG-PET in amnestic MCI: an European Alzheimer’s Disease Consortium (EADC) project. J Alzheimers Dis. 2015;44(3):815–26.PubMedCrossRef
20.
Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology. 2018;290(2):456–64.PubMedCrossRef
21.
Liu M, Cheng D, Wang K, Wang Y. Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics. 2018;16:295–308.PubMedCrossRef
22.
De Carli F, Nobili F, Pagani M, Bauckneht M, Massa F, Grazzini M, et al. Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease. Eur J Nucl Med Mol Imaging. 2019;46(2):334–47.PubMedCrossRef
23.
Cerami C, Della Rosa PA, Magnani G, Santangelo R, Marcone A, Cappa SF, et al. Brain metabolic maps in mild cognitive impairment predict heterogeneity of progression to dementia. Neuroimage Clin. 2014;5(7):187–94.
24.
Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Vanoli EG, Panzacchi A, Nobili F, Pappata S, Marcone A, Garibotto V, Castiglioni I, Magnani G, Cappa SF, Gianolli L. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuroimage Clin. 2014;6:445–54.PubMedPubMedCentralCrossRef
25.
Yamane T, Ikari Y, Nishio T, Ishii K, Ishii K, Kato T, et al. Visual-statistical interpretation of (18)F-FDG-PET images for characteristic Alzheimer patterns in a multicenter study: inter-rater concordance and relationship to automated quantitative evaluation. AJNR Am J Neuroradiol. 2014;35(2):244–9.PubMedCrossRefPubMedCentral
26.
Caminiti SP, Ballarini T, Sala A, Cerami C, Presotto L, Santangelo R, et al. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. Neuroimage Clin. 2018;28(18):167–77.CrossRef
27.
Brugnolo A, De Carli F, Pagani M, Morbelli S, Jonsson C, Chincarini A, et al. Head-to-Head comparison among semi-quantification tools of brain FDG-PET to aid the diagnosis of prodromal Alzheimer’s disease. J Alzheimers Dis. 2019;68(1):383–94.PubMedCrossRef
28.
Kucharczak F, Loquin K, Buvat I, Strauss O, Mariano-Goulart D. Interval-based reconstruction for uncertainty quantification in PET. Phys Med Biol. 2018;63(3):035014.PubMedCrossRef
29.
Kucharczak F, Ben BF, Strauss O, Mariano-Goulart D. Confidence interval constraint-based regularization framework for PET quantization. IEEE Trans Med Imaging. 2019;38(6):1513–23.PubMedCrossRef
30.
Jena A, Taneja S, Goel R, Renjen P, Negi P. Reliability of semiquantitative 18F-FDG PET parameters derived from simultaneous brain PET/MRI: a feasibility study. Eur J Radiol. 2014;83(7):1269–74.PubMedCrossRef
31.
Dubois B, Feldman HH, Jacova C, DeKosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 2007;6:734–46.PubMedCrossRef
32.
Shepp L, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Medical Imaging. 1982;1(2):113–22.PubMedCrossRef
33.
Lange K, Carson R. EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr. 1984;8(2):306–16.PubMed
34.
Defrise M, Kinahan PE, Michel DT, Sibomana C, Newport MD. Exact and approximate rebinning algorithms for 3-D PET data. IEEE Trans Med Imaging. 1997;16(2):194–204.CrossRef
35.
Rolls ET, Joliot M, Tzourio-Mazoyer N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage. 2015;122:1–5.PubMedCrossRef
36.
Mosconi L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. FDG-PET studies in MCI and AD. Eur J Nucl Med Mol Imaging. 2005;32:486–510.PubMedCrossRef
37.
Perolat J, Couso I, Loquin K, Strauss O. Generalizing the Wilcoxon rank-sum test for interval data. J Approx Reason. 2015;56:108–21.CrossRef
38.
Smets P. Analyzing the combination of conflicting belief functions. Inf Fus. 2007;8(4):387–412.CrossRef
39.
Hanley JA, McNeil BJ. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology. 1982;143:29–36.PubMedCrossRef
40.
Petersen RC, Aisen PS, Beckett LA, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010;74(3):201–9.PubMedPubMedCentralCrossRef
Metadata
Title
Brain 18F-FDG PET analysis via interval-valued reconstruction: proof of concept for Alzheimer’s disease diagnosis
Authors
Florentin Kucharczak
Marie Suau
Olivier Strauss
Fayçal Ben Bouallègue
Denis Mariano-Goulart
Publication date
01-08-2020
Publisher
Springer Singapore
Published in
Annals of Nuclear Medicine / Issue 8/2020
Print ISSN: 0914-7187
Electronic ISSN: 1864-6433
DOI
https://doi.org/10.1007/s12149-020-01490-7

Other articles of this Issue 8/2020

Annals of Nuclear Medicine 8/2020 Go to the issue

Original Article

Detectable size of melanoma metastases to brain on PET/CT

Original Article

Applying Wells score to inconclusive perfusion only modified PIOPED II (Prospective Investigation of Pulmonary Embolism Diagnosis II) readings in order to optimize the lung scintigraphy diagnostic yield in acute pulmonary embolism detection

Original Article

Preoperative F-18 fluorocholine PET/CT for the detection of hyperfunctioning parathyroid glands in patients with secondary or tertiary hyperparathyroidism: comparison with Tc-99m sestamibi scan and neck ultrasound

Original Article

Improvement of biodistribution profile of a radiogallium-labeled, αvβ6 integrin-targeting peptide probe by incorporation of negatively charged amino acids

Original Article

Physiological 18F-FDG uptake in the normal adult anal canal: evaluation by PET/CT

Original Article

Accuracy of metabolic volume and total glycolysis among six threshold-based target segmentation algorithms