Evidence map›Paper›PMID 31228173›Full record

ArticleJournal of psychiatry & neuroscience : JPN2020

Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume

Subin Lee, Hyunna Lee, Ki Woong Kim, Alzheimer’s Disease Neuroimaging Initiative

Abstract read
In one paragraph

Article in Journal of psychiatry & neuroscience : JPN, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Subin LeeFrom the Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea (S. Lee, Kim); the Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (H. Lee); the Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea (Kim); and the Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea (Kim).
Hyunna LeeFrom the Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea (S. Lee, Kim); the Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (H. Lee); the Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea (Kim); and the Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea (Kim).
Ki Woong KimFrom the Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea (S. Lee, Kim); the Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (H. Lee); the Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea (Kim); and the Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea (Kim).
Alzheimer’s Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
CIHRNIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

Background: Early identification of people at risk of imminent progression to dementia due to Alzheimer disease is crucial for timely intervention and treatment. We investigated whether the texture of MRI brain scans could predict the progression of mild cognitive impairment (MCI) to Alzheimer disease earlier than volume. Methods: We constructed a development data set (121 people who were cognitively normal and 145 who had mild Alzheimer disease) and a validation data set (113 patients with stable MCI who did not progress to Alzheimer disease for 3 years; 40 with early MCI who progressed to Alzheimer disease after 12–36 months; and 41 with late MCI who progressed to Alzheimer disease within 12 months) from the Alzheimer’s Disease Neuroimaging Initiative. We analyzed the texture of the hippocampus, precuneus and posterior cingulate cortex using a grey-level co-occurrence matrix. We constructed texture and volume indices from the development data set using logistic regression. Using area under the curve (AUC) of receiver operator characteristics, we compared the accuracy of hippocampal volume, hippocampal texture and the composite texture of the hippocampus, precuneus and posterior cingulate cortex in predicting conversion from MCI to Alzheimer disease in the validation data set. Results: Compared with hippocampal volume, hippocampal texture (0.790 v. 0.739, p = 0.047) and composite texture (0.811 v. 0.739, p = 0.007) showed larger AUCs for conversion to Alzheimer disease from both early and late MCI. Hippocampal texture showed a marginally larger AUC than hippocampal volume in early MCI (0.795 v. 0.726, p = 0.060). Composite texture showed a larger AUC for conversion to Alzheimer disease than hippocampal volume in both early (0.817 v. 0.726, p = 0.027) and late MCI (0.805 v. 0.753, p = 0.019). Limitations: This study was limited by the absence of histological data, and the pathology reflected by the texture measures remains to be validated. Conclusion: Textures of the hippocampus, precuneus and posterior cingulate cortex predicted conversion from MCI to Alzheimer disease at an earlier time point and with higher accuracy than hippocampal volume.

Indexed as

Disease ProgressionAgedAged, 80 and overAlzheimer DiseaseCognitive DysfunctionFemaleGyrus CinguliHippocampusHumansMagnetic Resonance ImagingMaleNeuroimagingParietal LobePrognosisReproducibility of Results

Identifiers

PMID31228173
PMCPMC6919919

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.