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30/100

✓ Peer-reviewed alzhub usefulness score 30 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Synthetic images were visually consistent, producing plausible negative and positive patterns while preserving continuity in the sagittal plane. Voxel values of the synthetic images showed a significant linear relationship with the real images. The contrast correlated well with the real images, and the differences between the negative and positive cases were significant as well as those in the real images. The performance of the positive or negative 2-class classifier exceeded 85% for the synthetic images. The synthetic images successfully captured features of Aβ deposition, and evaluation with a 2-class classifier achieved an acceptable accuracy of 85%. These results suggest that amyloid images can potentially be generated from 18F-FDG PET images for use in clinical practice.

Key findings

Synthetic images were visually consistent, producing plausible negative and positive patterns while preserving continuity in the sagittal plane.
Voxel values of the synthetic images showed a significant linear relationship with the real images.
The contrast correlated well with the real images, and the differences between the negative and positive cases were significant as well as those in the real images.
The performance of the positive or negative 2-class classifier exceeded 85% for the synthetic images.
The synthetic images successfully captured features of Aβ deposition, and evaluation with a 2-class classifier achieved an acceptable accuracy of 85%.

Why it matters

This study used a generative adversarial network to create synthetic amyloid PET images from 18F-FDG PET images, and a two-class classifier distinguished positive from negative cases with over 85% accuracy. The authors suggest amyloid images can potentially be generated from 18F-FDG PET images for clinical use, but this is an imaging method, not a treatment, cure or prevention.

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Study details

Evidence strengthLow

How this study scored 30/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Journal of Nuclear Medicine Technology)
Journal's citation record	0 / 4	Journal of Nuclear Medicine Technology: journal h-index 51 (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	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	0 / 7	Cited 0 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.0 citations a year over 2 year(s) since 2026
Impact for its field and year	0 / 6	Cited 0.0× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Yuichi Kimura: h-index 36 (OpenAlex, whole career)
Institution's track record	0 / 3	Kindai University: institution h-index 479 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41529928)
Free to read	2 / 2	Free to read
Total	30 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.

APA 7th

Honda, M., Yamada, T., Watanabe, S., Watanabe, A., Nagaoka, T., Nemoto, M., et al. (2026). Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-Dimensional 18 F-FDG Images and Evaluation for Clinical Use. Journal of Nuclear Medicine Technology. https://doi.org/10.2967/jnmt.125.270154

Vancouver

Honda M, Yamada T, Watanabe S, Watanabe A, Nagaoka T, Nemoto M, et al. Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-Dimensional 18 F-FDG Images and Evaluation for Clinical Use. Journal of Nuclear Medicine Technology. 2026. doi:10.2967/jnmt.125.270154. PMID: 41529928.

BibTeX

@article{honda2026-41529928,
  author  = {Honda, Misa and Yamada, Takahiro and Watanabe, Shogo and Watanabe, Ai and Nagaoka, Takashi and Nemoto, Mitsutaka and Mikami, Katsuhiro and Hanaoka, Kohei and others},
  title   = {{Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-Dimensional 18 F-FDG Images and Evaluation for Clinical Use}},
  journal = {Journal of Nuclear Medicine Technology},
  year    = {2026},
  doi     = {10.2967/jnmt.125.270154},
  pmid    = {41529928},
  url     = {https://doi.org/10.2967/jnmt.125.270154}
}

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