Advertisement

Molecular Diagnosis & Therapy

, Volume 22, Issue 4, pp 475–483 | Cite as

Visual Rating and Computer-Assisted Analysis of FDG PET in the Prediction of Conversion to Alzheimer’s Disease in Mild Cognitive Impairment

  • Jae Myeong Kang
  • Jun-Young Lee
  • Yu Kyeong Kim
  • Bo Kyung Sohn
  • Min Soo Byun
  • Ji Eun Choi
  • Soo Kyung Son
  • Hyung-Jun Im
  • Jae-Hoon Lee
  • Young Hoon Ryu
  • Dong Young Lee
Original Research Article

Abstract

Background

Fluorodeoxyglucose (FDG) positron emission tomography (PET) is useful to predict Alzheimer’s disease (AD) conversion in patients with mild cognitive impairment (MCI). However, few studies have examined the extent to which FDG PET alone can predict AD conversion and compared the efficacy between visual and computer-assisted analysis directly.

Objective

The current study aimed to evaluate the value of FDG PET in predicting the conversion to AD in patients with MCI and to compare the predictive values of visual reading and computer-assisted analysis.

Methods and materials

A total of 54 patients with MCI were evaluated with FDG PET and followed-up for 2 years with final diagnostic evaluation. FDG PET images were evaluated by (1) traditional visual rating, (2) composite score of visual rating of the brain cortices, and (3) composite score of computer-assisted analysis. Receiver operating characteristics (ROC) curves were compared to analyze predictive values.

Results

Nineteen patients (35.2%) converted to AD from MCI. The area under the curve (AUC) of the ROC curve of the traditional visual rating, composite score of visual rating, and computer-assisted analysis were 0.67, 0.76, and 0.79, respectively. ROC curves of the composite scores of the visual rating and computer-assisted analysis were comparable (Z = 0.463, p = 0.643).

Conclusions

Visual rating and computer-assisted analysis of FDG PET scans were analogously accurate in predicting AD conversion in patients with MCI. Therefore, FDG PET may be a useful tool for screening AD conversion in patients with MCI, when using composite score, regardless of the method of interpretation.

Notes

Author contributions

JMK, JYL, YKK, and DYL designed the study. JYL, BKS, MSB, and DYL acquired the data, and JMK, JYL, JEC, SKS, HJI, JHL, and YHR analyzed and interpreted the results of experiments. JMK wrote the main manuscript and prepared figures. JMK, JYL, BKS, and YKK edited and revised the manuscript. All authors reviewed and approved the manuscript.

Compliance with Ethical Standards

Conflict of interest

All authors (JMK, JYL, YKK, BKS, MSB, JEC, SKS, HJI, JHL, YHR, and DYL) declare that they have no conflict of interest.

Funding

This study was supported by the National Evidence-based Healthcare Collaborating Agency (NECA-C-13-010) and by a grant from Ministry of Science, ICT and Future Planning (grant no. NRF-2014M3C7A1046042). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethics statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consents were obtained from all individual participants included in the study.

Supplementary material

40291_2018_334_MOESM1_ESM.docx (450 kb)
Supplementary material 1 (DOCX 449 kb)

References

  1. 1.
    Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256(3):183–94.CrossRefPubMedGoogle Scholar
  2. 2.
    Feldman HH, Jacova C. Mild cognitive impairment. Am J Geriatr Psychiatry. 2005;13(8):645–55.  https://doi.org/10.1097/00019442-200508000-00003.CrossRefPubMedGoogle Scholar
  3. 3.
    Buchman AS, Bennett DA. Loss of motor function in preclinical Alzheimer's disease. Expert Rev Neurother. 2011;11(5):665–76.  https://doi.org/10.1586/ern.11.57.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Allegri RF, Glaser FB, Taragano FE, Buschke H. Mild cognitive impairment: believe it or not? Int Rev Psychiatry. 2008;20(4):357–63.CrossRefPubMedGoogle Scholar
  5. 5.
    Bruscoli M, Lovestone S. Is MCI really just early dementia? A systematic review of conversion studies. Int Psychogeriatr. 2004;16(02):129–40.CrossRefPubMedGoogle Scholar
  6. 6.
    Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, et al. Mild cognitive impairment: ten years later. Arch Neurol. 2009;66(12):1447–55.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Mielke R, Heiss W-D. Positron emission tomography for diagnosis of Alzheimer’s disease and vascular dementia. New York: Springer; 1998.Google Scholar
  8. 8.
    Herholz K, Salmon E, Perani D, Baron J, Holthoff V, Frölich L, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage. 2002;17(1):302–16.CrossRefPubMedGoogle Scholar
  9. 9.
    Silverman DH, Small GW, Chang CY, Lu CS, de Aburto MAK, Chen W, et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA. 2001;286(17):2120–7.CrossRefPubMedGoogle Scholar
  10. 10.
    Silverman DH. Brain 18F-FDG PET in the diagnosis of neurodegenerative dementias: comparison with perfusion SPECT and with clinical evaluations lacking nuclear imaging. J Nuc Med. 2004;45(4):594–607.Google Scholar
  11. 11.
    Patwardhan MB, McCrory DC, Matchar DB, Samsa GP, Rutschmann OT. Alzheimer disease: operating characteristics of PET—a meta-analysis 1. Radiology. 2004;231(1):73–80.CrossRefPubMedGoogle Scholar
  12. 12.
    Mosconi L, Silverman DH. FDG PET in the evaluation of mild cognitive impairment and early dementia. PET in the evaluation of Alzheimer’s disease and related disorders. New York: Springer; 2009. p. 49–65.Google Scholar
  13. 13.
    Patterson JC, Lilien DL, Takalkar A, Pinkston JB. Early detection of brain pathology suggestive of early AD using objective evaluation of FDG-PET scans. Int J Alzheimers Dis. 2011.  https://doi.org/10.4061/2011/946590.Google Scholar
  14. 14.
    Chen K, Langbaum JB, Fleisher AS, Ayutyanont N, Reschke C, Lee W, et al. Twelve-month metabolic declines in probable Alzheimer’s disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: findings from the Alzheimer’s Disease Neuroimaging Initiative. Neuroimage. 2010;51(2):654–64.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Morbelli S, Piccardo A, Villavecchia G, Dessi B, Brugnolo A, Piccini A, et al. Mapping brain morphological and functional conversion patterns in amnestic MCI: a voxel-based MRI and FDG-PET study. Eur J Nuc Med Mol Imaging. 2010;37(1):36–45.CrossRefGoogle Scholar
  16. 16.
    Yuan Y, Gu Z-X, Wei W-S. Fluorodeoxyglucose–positron-emission tomography, single-photon emission tomography, and structural MR imaging for prediction of rapid conversion to Alzheimer disease in patients with mild cognitive impairment: a meta-analysis. Am J Neuroradiol. 2009;30(2):404–10.CrossRefPubMedGoogle Scholar
  17. 17.
    Smailagic N, Vacante M, Hyde C, Martin S, Ukoumunne O, Sachpekidis C. 18F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2015.  https://doi.org/10.1002/14651858.CD010632.pub2.Google Scholar
  18. 18.
    Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. NeuroImage Clin. 2013;2:735–45.  https://doi.org/10.1016/j.nicl.2013.05.004.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Shaffer JL, Petrella JR, Sheldon FC, Choudhury KR, Calhoun VD, Coleman RE, et al. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology. 2013;266(2):583–91.  https://doi.org/10.1148/radiol.12120010.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    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.  https://doi.org/10.1001/archneur.62.11.1728.CrossRefPubMedGoogle Scholar
  21. 21.
    Petersen RC. Mild cognitive impairment. N Engl J Med. 2011;364(23):2227–34.CrossRefPubMedGoogle Scholar
  22. 22.
    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(8):734–46.CrossRefPubMedGoogle Scholar
  23. 23.
    Lee JH, Lee KU, Lee DY, Kim KW, Jhoo JH, Kim JH, et al. Development of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD-K) clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci. 2002;57(1):P47–53.CrossRefPubMedGoogle Scholar
  24. 24.
    Riedl V, Utz L, Castrillón G, Grimmer T, Rauschecker JP, Ploner M, et al. Metabolic connectivity mapping reveals effective connectivity in the resting human brain. Proc Natl Acad Sci USA. 2016;113(2):428–33.  https://doi.org/10.1073/pnas.1513752113.CrossRefPubMedGoogle Scholar
  25. 25.
    Shon JM, Lee DY, Seo EH, Sohn BK, Kim JW, Park SY et al. Functional neuroanatomical correlates of the executive clock drawing task (CLOX) performance in Alzheimer’s disease: a FDG-PET study. Neuroscience. 2013;246(Supplement C):271–80.  https://doi.org/10.1016/j.neuroscience.2013.05.008.
  26. 26.
    Ito K, Fukuyama H, Senda M, Ishii K, Maeda K, Yamamoto Y, et al. Prediction of outcomes in mild cognitive impairment by using 18F-FDG-PET: a multicenter study. J Alzheimers Dis. 2015;45(2):543–52.  https://doi.org/10.3233/jad-141338.CrossRefPubMedGoogle Scholar
  27. 27.
    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.  https://doi.org/10.3233/jad-142229.CrossRefPubMedGoogle Scholar
  28. 28.
    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 Nuc Med. 2016;57(2):204–7.CrossRefGoogle Scholar
  29. 29.
    Frisoni GB, Bocchetta M, Chetelat G, Rabinovici GD, de Leon MJ, Kaye J, et al. Imaging markers for Alzheimer disease: which vs how. Neurology. 2013;81(5):487–500.  https://doi.org/10.1212/WNL.0b013e31829d86e8.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Ossenkoppele R, Prins ND, Pijnenburg YA, Lemstra AW, van der Flier WM, Adriaanse SF, et al. Impact of molecular imaging on the diagnostic process in a memory clinic. Alzheimers Dement. 2013;9(4):414–21.  https://doi.org/10.1016/j.jalz.2012.07.003.CrossRefPubMedGoogle Scholar
  31. 31.
    Laforce R Jr, Buteau JP, Paquet N, Verret L, Houde M, Bouchard RW. The value of PET in mild cognitive impairment, typical and atypical/unclear dementias: a retrospective memory clinic study. Am J Alzheimers Dis Other Demen. 2010;25(4):324–32.  https://doi.org/10.1177/1533317510363468.CrossRefPubMedGoogle Scholar
  32. 32.
    Arbizu J, Prieto E, Martinez-Lage P, Marti-Climent JM, Garcia-Granero M, Lamet I, et al. Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia. Eur J Nucl Med Mol Imaging. 2013;40(9):1394–405.  https://doi.org/10.1007/s00259-013-2458-z.CrossRefPubMedGoogle Scholar
  33. 33.
    Zhang S, Han D, Tan X, Feng J, Guo Y, Ding Y. Diagnostic accuracy of 18 F-FDG and 11 C-PIB-PET for prediction of short-term conversion to Alzheimer’s disease in subjects with mild cognitive impairment. Int J Clin Pract. 2012;66(2):185–98.  https://doi.org/10.1111/j.1742-1241.2011.02845.x.CrossRefPubMedGoogle Scholar
  34. 34.
    Pagani M, De Carli F, Morbelli S, Oberg J, Chincarini A, Frisoni GB et al. Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls. A European Alzheimer’s Disease Consortium (EADC) study. Neuroimage Clin. 2015;7:34–42.  https://doi.org/10.1016/j.nicl.2014.11.007.
  35. 35.
    Landau S, Harvey D, Madison C, Reiman E, Foster N, Aisen P, et al. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology. 2010;75(3):230–8.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Nobili F, Salmaso D, Morbelli S, Girtler N, Piccardo A, Brugnolo A, et al. Principal component analysis of FDG PET in amnestic MCI. Eur J Nucl Med Mol Imaging. 2008;35(12):2191–202.  https://doi.org/10.1007/s00259-008-0869-z.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Psychiatry, Gil Medical CenterGachon University College of MedicineIncheonRepublic of Korea
  2. 2.Department of PsychiatrySeoul National University College of MedicineSeoulRepublic of Korea
  3. 3.Department of Psychiatry and Behavioral ScienceSMG-SNU Boramae Medical CenterSeoulRepublic of Korea
  4. 4.Department of Nuclear MedicineSMG-SNU Boramae Medical CenterSeoulRepublic of Korea
  5. 5.Department of Psychiatry, Sanggye Paik HospitalInje University College of MedicineSeoulRepublic of Korea
  6. 6.Institute of Human Behavioral Medicine, Medical Research CenterSeoul National UniversitySeoulRepublic of Korea
  7. 7.Division for Healthcare Technology Assessment ResearchNational Evidence-based Healthcare Collaborating AgencySeoulRepublic of Korea
  8. 8.Department of Nuclear MedicineSeoul National University College of MedicineSeoulRepublic of Korea
  9. 9.Department of Nuclear Medicine, Yonsei University College of MedicineGangnam Severance HospitalSeoulRepublic of Korea

Personalised recommendations