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Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers
Gupta Y, Lama RK, Kwon GR, Alzheimer's Disease Neuroimaging Initiative.. Frontiers in computational neuroscience, 2019-10-16
Topic: Amyloid, tau and blood-test diagnosis
As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches.
Key findings
- As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs.
- MCIs subjects which are high relative to single modality results and other state-of-the-art approaches.
Why it matters
This study combined genetic, spinal fluid, MRI, and PET data and reported high accuracy in distinguishing Alzheimer's disease from mild cognitive impairment. It is a diagnostic classification study and does not test any treatment, cure, or prevention.
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Study details
- Participants158
- Effect size95.24%
- Evidence strengthLow
How this study scored 55/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 53 of the 96 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in computational neuroscience) |
| Journal's citation record | 1 / 4 | Frontiers in Computational Neuroscience: journal h-index 105 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 158 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 155 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.2 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 4 / 6 | Cited 12.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 53 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 2 / 3 | University of California, Berkeley: institution h-index 2,012 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 9 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31680923) |
| Free to read | 2 / 2 | Free to read |
| Points | 53 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 53 of the 96 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fncom.2019.00072
- PubMed: 31680923
- Original source →
- Free full text: Frontiers in Computational Neuroscience →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Gupta, Y., Lama, R. K., Kwon, G. R., & Alzheimer's Disease Neuroimaging Initiative. (2019). Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Frontiers in computational neuroscience. https://doi.org/10.3389/fncom.2019.00072
Vancouver
Gupta Y, Lama RK, Kwon GR, Alzheimer's Disease Neuroimaging Initiative. Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Frontiers in computational neuroscience. 2019. doi:10.3389/fncom.2019.00072. PMID: 31680923.
BibTeX
@article{gupta2019-31680923,
author = {Gupta, Y. and Lama, R. K. and Kwon, G. R. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers}},
journal = {Frontiers in computational neuroscience},
year = {2019},
doi = {10.3389/fncom.2019.00072},
pmid = {31680923},
url = {https://doi.org/10.3389/fncom.2019.00072}
}
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