The DOI matches its Crossref record; 387 of the 391 references it cites were found in a registry or an index.
Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment
Open accessAbstract
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 reg
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“a review summarising 33 studies in structural neuroimaging for the early diagnosis of AD in people with MCI (including 3935 participants) concluded that there is lack of systematic approach in data collection, analysis and interpretation [24].”
From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [24]
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199
Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR)
American Psychiatric Association (2000) · doi:10.1176/appi.books.9780890423349 · Cited by 11,069
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Research works printed but not linked: 11
- Chertkow, 2012, Amnestic mild cognitive impairment in a memory clinic: longitudinal course and predictors of progression (abstract), Alzheimer's & Dementia, 8 Suppl 4, 482
- Citak-Er, 2017, A novel convolutional neural network model based on voxel-based morphometry of imaging data in predicting the prognosis of patients with mild cognitive impairment, Journal of Neurological Sciences, 34, 52
- Cover, 2016, The measurement of hippocampal atrophy rates with MRI for a 3-year study appears to be at least 3 times more sensitive than a 1-year study based on back-to-back reproducibility, Alzheimer's & Dementia, 12 Suppl 7, 99–100
- Kong, 2014, Role of imaging and genetic data for predicting time to conversion to Azheimer disease in patients with mild cognitive impairment, Annals of Neurology, 76 Suppl 18, S94
- Nesteruk, 2015, Predicting the conversion of mild cognitive impairment to Alzheimer's disease based on the volumetric measurements of the selected brain structures in magnetic resonance imaging, Neurologia i Neurochirurgia Polska, 49, 349, 10.1016/j.pjnns.2015.09.003 · DOI
- Maurik, 2016, Personalized risk estimates for MCI patients: taking biomarkers into the clinic, Alzheimer's & Dementia, 12 Suppl 7, P393
- Alzheimer’s Disease International, 2019, World Alzheimer Report 2019: Attitudes to Dementia
- Bossuyt P Davenport C Deeks J Hyde C Leeflang M Scholten R Chapter 11: Interpreting results and drawing conclusions. In: Deeks JJ, Bossuyt PM, Gatsonis C (editors), Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.9. The Cochrane Collaboration, 2013 Available from: srdta.cochrane.org
- De Vet HC Eisinga A Riphagen II Aertgeerts B Pewsner D Chapter 7: Searching for Studies. In: Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.4 (September 2008). The Cochrane Collaboration, 2008: Section 7.2.1.2 Available from srdta.cochrane.org/handbook-dta-reviews
- Galton, 2001, Temporal lobe rating scale: application to Alzheimer's disease and frontotemporal dementia, Journal of Neurology, Neurosurgery, and Psychiatry, 70, 165, 10.1136/jnnp.70.2.165 · DOI
- Yue, 1997, Sulcal, ventricular, and white matter changes at MR imaging in the aging brain: data from the cardiovascular health study, Radiology, 202, 33, 10.1148/radiology.202.1.8988189 · DOI
Cited by
Sorted by citations
Safiri, S., Jolfayi, A. G., Fazlollahi, A., Morsali, S., Sarkesh, A., Sorkhabi, A. D., Golabi, B., Aletaha, R., Asghari, K. M., Hamidi, S., et al. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1474043 · Cited by 273
Quinn, T. J., Richard, E., Teuschl, Y., Gattringer, T., Hafdi, M., O'Brien, J. T., Merriman, N. A., Gillebert, C. R., Huygelier, H., Verdelho, A., et al. (2021) · European Journal of Neurology · doi:10.1111/ene.15068 · Cited by 201
XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease
Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R., & Yu, H. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02238-9 · Cited by 205
The use of neuroimaging techniques in the early and differential diagnosis of dementia
Chouliaras, L., & O’Brien, J. T. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02215-8 · Cited by 221
Alzheimer’s Disease: Exploring the Landscape of Cognitive Decline
Tenchov, R., Sasso, J. M., & Zhou, Q. A. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00339 · Cited by 170
Metadata
- Authors
- Lombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., & Filippini, G.
- Affiliations
- University of Florence (IT)
- Sorbonne Université (FR)
- University of Pisa (IT)
- University of Milan (IT)
- Azienda sanitaria universitaria Friuli Centrale (IT)
- Fondazione IRCCS Istituto Neurologico Carlo Besta (IT)
- University of Geneva (CH)
- ORCID
- Venue
- Cochrane Database of Systematic Reviews
- Publisher
- Wiley
- Published
- 2020-03-02
- Volume
- 2020
- Issue
- 3
- Pages
- CD009628-CD009628
- ISSN
- 1361-6137 (ISSN-L)
- 1465-1858
- PMC
- PMC7059964
- OpenAlex
- W3009532950
- Cited by
- 214
- References
- 391
- Citations by year
- 2026: 16 · 2025: 51 · 2024: 47 · 2023: 47 · 2022: 21 · 2021: 19 · 2020: 13
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cochrane Database of Systematic Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Oncology › Cancer-related cognitive impairment studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- structural MRI, Alzheimer's disease, mild cognitive impairment, early diagnosis, dementia, diagnostic accuracy, brain atrophy, hippocampal atrophy, medial temporal lobe, entorhinal cortex, amygdala, lateral ventricle, gray matter atrophy, volumetric MRI, visual assessment, conversion to dementia, white matter hyperintensities, amyloid imaging, neurodegeneration biomarkers, medial temporal lobe atrophy
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Middle Aged, Organ Size, Prospective Studies, Sensitivity and Specificity, Temporal Lobe, Disease Progression, Entorhinal Cortex, Lateral Ventricles, Neuroimaging, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review