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Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly
Yoon Seong Choi, Pei Ing Ngam, Jeong Ryong Lee, Dosik Hwang, Eng-King Tan, Harvard Aging Brain Study, Australian Imaging Biomarkers and Lifestyle Study of Aging, Japanese Alzheimer’s Disease Neuroimaging Initiative et al.. Radiology advances, 2024-08-06
Topic: Brain imaging, connectivity and assessment
DL-ADprob was independently prognostic in both ADNI-MCI (P < .001) and HABS-CU (P = .048) sets. Adding DL-ADprob to other factors increased prognostic performances in both ADNI-MCI (tdAUC 0.758 [0.721-0.792] vs 0.782 [0.742-0.818], tdAUC difference 0.023 [0.007-0.038]) and HABS-CU (tdAUC 0.846 [0.755-0.925] vs 0.870 [0.773-0.943], tdAUC difference 0.022 [-0.004 to 0.053]). DL-ADprob was independently prognostic in amyloid-positive (P < .001) and amyloid-negative subgroups (P = .007). DL-ADprob showed incremental prognostic value in amyloid-positive (tdAUC 0.666 [0.623-0.713] vs 0.706 [0.657-0.755], tdAUC difference 0.039 [0.016-0.064]), but not in amyloid-negative (tdAUC 0.818 [0.757-0.882] vs 0.816 [0.751-0.880], tdAUC difference -0.002 [-0.031 to 0.029]) subgroup. The pairwise ICCs of DL-ADprob between Pittsburgh compound B and florbetapir, florbetaben, and flutemetamol, respectively, ranged from 0.913 to 0.935. Deep learning-based harmonization of amyloid PET improves cognitive decline prediction in non-demented elderly, suggesting it could complement conventional amyloid PET measures.
Key findings
- DL-ADprob was independently prognostic in both ADNI-MCI (P < .001) and HABS-CU (P = .048) sets.
- Adding DL-ADprob to other factors increased prognostic performances in both ADNI-MCI (tdAUC 0.758 [0.721-0.792] vs 0.782 [0.742-0.818], tdAUC difference 0.023 [0.007-0.038]) and HABS-CU (tdAUC 0.846 [0.755-0.925] vs 0.870 [0.773-0.943], tdAUC difference 0.022 [-0.004 to 0.053]).
- DL-ADprob was independently prognostic in amyloid-positive (P < .001) and amyloid-negative subgroups (P = .007).
- DL-ADprob showed incremental prognostic value in amyloid-positive (tdAUC 0.666 [0.623-0.713] vs 0.706 [0.657-0.755], tdAUC difference 0.039 [0.016-0.064]), but not in amyloid-negative (tdAUC 0.818 [0.757-0.882] vs 0.816 [0.751-0.880], tdAUC difference -0.002 [-0.031 to 0.029]) subgroup.
- The pairwise ICCs of DL-ADprob between Pittsburgh compound B and florbetapir, florbetaben, and flutemetamol, respectively, ranged from 0.913 to 0.935.
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Study details
- Participants1,050
- Effect sizeAUC 0.758; AUC 0.846; AUC 0.666
- Evidence strengthLow
How this study scored 59/100
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 journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 33 of the 56 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Radiology advances) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,050 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41059393) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 33 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 33 of the 56 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
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APA 7th
Choi, Y. S., Ngam, P. I., Lee, J. R., Hwang, D., Tan, E.-K., Harvard Aging Brain Study, et al. (2024). Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly. Radiology advances. https://doi.org/10.1093/radadv/umae019
Vancouver
Choi YS, Ngam PI, Lee JR, Hwang D, Tan E.-K, Harvard Aging Brain Study, et al. Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly. Radiology advances. 2024. doi:10.1093/radadv/umae019. PMID: 41059393.
BibTeX
@article{choi2024-41059393,
author = {Choi, Yoon Seong and Ngam, Pei Ing and Lee, Jeong Ryong and Hwang, Dosik and Tan, Eng-King and {Harvard Aging Brain Study} and {Australian Imaging Biomarkers and Lifestyle Study of Aging} and {Japanese Alzheimer’s Disease Neuroimaging Initiative} and others},
title = {{Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly}},
journal = {Radiology advances},
year = {2024},
doi = {10.1093/radadv/umae019},
pmid = {41059393},
url = {https://doi.org/10.1093/radadv/umae019}
}