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Meta-Analysis
. 2020 Mar 2;3(3):CD009628.
doi: 10.1002/14651858.CD009628.pub2.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

Gemma Lombardi  1 , Giada Crescioli  1 , Enrica Cavedo  2 , Ersilia Lucenteforte  3 , Giovanni Casazza  4 , Alessandro-Giacco Bellatorre  5 , Chiara Lista  6 , Giorgio Costantino  7 , Giovanni Frisoni  8 , Gianni Virgili  1 , Graziella Filippini  9
Affiliations
Meta-Analysis

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

Gemma Lombardi et al. Cochrane Database Syst Rev. .

Abstract

Background: Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

Objectives: To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

Search methods: On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

Selection criteria: We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

Data collection and analysis: Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

Main results: We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

Authors' conclusions: The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

PubMed Disclaimer

Conflict of interest statement

Gemma Lombardi: none

Giada Crescioli: none

Enrica Cavedo: none

Ersilia Lucenteforte: none

Giovanni Casazza: none

Alessandro‐Giacco Bellatorre: none

Chiara Lista: none

Giorgio Costantino: none

Giovanni Frisoni: none

Gianni Virgili: none

Graziella Filippini: none

Figures

1
1
Figure 1. Flow of studies identified in literature search for systematic review on structural magnetic resonance imaging for an early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment
2
2
Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study
3
3
Forest plot of total hippocampal volume measured by structural magnetic resonance imaging (MRI) for early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Plot shows study‐specific estimates of sensitivity and specificity (squares) with 95% confidence interval (black line) and study. Studies are ordered according to the estimates of sensitivity. TP: true positive; FP: false positive; FN: false negative; TN: true negative
4
4
Summary receiver operating characteristic (ROC) plot of total hippocampus volume measured by structural magnetic resonance imaging (MRI) for early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Each point represents the pair of sensitivity and specificity from a study. The solid black circle represents the pooled sensitivity and specificity, which is surrounded by a 95% confidence region (dashed line)
5
5
Summary receiver operating characteristic curve (ROC) presenting direct comparisons of hippocampus left and hippocampus right
6
6
Summary receiver operating characteristic (ROC) plot of total medial temporal lobe volume measured by structural MRI for early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Each point represents the pair of sensitivity and specificity from a study. The solid black circle represents the pooled sensitivity and specificity, which is surrounded by a 95% confidence region (dashed line)
7
7
Summary receiver operating characteristic (ROC) plot of volume of lateral ventricles measured by structural magnetic resonance imaging (MRI) for early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Each point represents the pair of sensitivity and specificity from a study. The solid black circle represents the pooled sensitivity and specificity, which is surrounded by a 95% confidence region (dashed line)
See this image and copyright information in PMC

Comment in

References

References to studies included in this review

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Visser 2002 {published data only}
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References to studies excluded from this review

Aguilar 2014 {published data only}
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Apostolova 2006 {published data only}
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Archer 2010 {published data only}
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Callahan 2015 {published data only}
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Carmichael 2013 {published data only}
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Cheng 2015b {published data only}
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Chetelat 2005 {published data only}
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Chincarini 2014 {published data only}
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Davatzikos 2011 {published data only}
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    1. Khan W, Aguilar C, Kiddle SJ, Doyle O, Thambisetty M, Muehlboeck S, et al. Alzheimer’s Disease Neuroimaging Initiative. A subset of cerebrospinal fluid proteins from a multi‐analyte panel associated with brain atrophy, disease classification and prediction in Alzheimer's disease. PloS One 2015;10(8):e0134368. - PMC - PubMed
Killiany 2000 {published data only}
    1. Killiany RJ, Gomez‐Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease. Annals of Neurology 2000;47(4):430‐9. [PUBMED: 10762153] - PubMed
Kim 2017 {published data only}
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Kloppel 2015 {published data only}
    1. Klöppel S, Peter J, Ludl A, Pilatus A, Maier S, Mader I, et al. Alzheimer’s Disease Neuroimaging Initiative. Applying automated MR‐based diagnostic methods to the memory clinic: a prospective study. Journal of Alzheimer's Disease 2015;47(4):939‐54. - PMC - PubMed
Kong 2014 {published data only}
    1. Kong D, Giovanello KS, Wang Y, Lee E, Ibrahim JG, Lin W, et al. Role of imaging and genetic data for predicting time to conversion to Azheimer disease in patients with mild cognitive impairment. Annals of Neurology 2014;76 Suppl 18:S94.
Korf 2004 {published data only}
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Korolev 2016 {published data only}
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Kovacevic 2009 {published data only}
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Krashenyi 2016 {published data only}
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Laforce 2010 {published data only}
    1. 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. American Journal of Alzheimer's Disease and Other Dementias 2010;25(4):324‐32. [PUBMED: 20539026] - PMC - PubMed
Lan 2017 {published data only}
    1. Lan MJ, Ogden RT, Kumar D, Stern Y, Parsey RV, Pelton GH, et al. Utility of molecular and structural brain imaging to predict progression from mild cognitive impairment to dementia. Journal of Alzheimer's Disease 2017;60(3):939‐47. - PMC - PubMed
Landau 2010 {published data only}
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Lebedev, 2014 {published data only}
    1. Lebedev AV, Westman E, Westen GJ, Kramberger MG, Lundervold A, Aarsland D, et al. Alzheimer's Disease Neuroimaging Initiative and the AddNeuroMed consortium. Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between‐cohort robustness. Neuroimage: Clinical 2014;6:115‐25. - PMC - PubMed
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Lehman 2013 {published data only}
    1. Lehmann M, Koedam EL, Barnes J, Bartlett JW, Barkhof F, Wattjes MP, et al. Alzheimer's Disease Neuroimaging Initiative. Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers. Neurobiology of Aging 2013;34(1):73‐82. - PubMed
Leung 2010 {published data only}
    1. Leung KK, Barnes J, Ridgway GR, Bartlett JW, Clarkson MJ, Macdonald K, et al. Automated cross‐sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease. NeuroImage 2010;51(4):1345‐59. [PUBMED: 20230901] - PMC - PubMed
Leung 2013 {published data only}
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Li 2012 {published data only}
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Li 2014a {published data only}
    1. Li H, Liu Y, Gong P, Zhang C, Ye J, Alzheimers Disease Neuroimaging Initiative. Hierarchical interactions model for predicting mild cognitive impairment (MCI) to Alzheimer's disease (AD) conversion. PloS One 2014;9(1):e82450. - PMC - PubMed
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Lillemark 2014 {published data only}
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Lindemer 2015 {published data only}
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Liu 2013   {published data only}
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Liu 2014a {published data only}
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Llano 2011 {published data only}
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Long 2016 {published data only}
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Lopez 2016 {published data only}
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Luo 2016 {published data only}
    1. Luo Y, Cao Z, Liu Y, Wu L, Shan H, Liu Y, et al. T2 signal intensity and volume abnormalities of hippocampal subregions in patients with amnestic mild cognitive impairment by magnetic resonance imaging. International Journal of Neuroscience 2016;126(10):904‐11. - PubMed
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MacDonald 2013 {published data only}
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Mangialasche 2013 {published data only}
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Mascalchi 2016 {published data only}
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Massaro 2004 {published data only}
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McEvoy 2009 {published data only}
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Meguro 2016 {published data only}
    1. Meguro K, Akanuma K, Meguro M, Yamaguchi S, Ishii H, Tashiro M. Prevalence and prognosis of prodromal Alzheimer's disease as assessed by magnetic resonance imaging and 18F‐fluorodeoxyglucose‐positron emission tomography in a community: reanalysis from the Osaki‐Tajiri Project. Psychogeriatrics 2016;16(2):116‐20. - PubMed
Meyer 2005a {published data only}
    1. Meyer JS, Huang J, Chowdhury M. MRI abnormalities associated with mild cognitive impairments of vascular (VMCI) versus neurodegenerative (NMCI) types prodromal for vascular and Alzheimer's dementias. Current Alzheimer Research 2005;2(5):579‐85. [PUBMED: 16375661] - PubMed
Meyer 2005b {published data only}
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Meyer 2007 {published data only}
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Minhas 2017 {published data only}
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Mubeen 2017 {published data only}
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Nesteruk 2015 {published data only}
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Nordlund 2005 {published data only}
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Persson 2017 {published data only}
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Petersen 2010 {published data only}
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Prestia 2015 {published data only}
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Prins 2013 {published data only}
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Runtti 2014 {published data only}
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Salvatore 2018 {published data only}
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Sambuchi 2015 {published data only}
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Shaffer 2013 {published data only}
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Sheng 2017 {published data only}
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Sluimer 2009 {published data only}
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Sousa 2016 {published data only}
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