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Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment
Li Y, Jiang J, Lu J, Jiang J, Zhang H, Zuo C.. Therapeutic advances in neurological disorders, 2019-03-29
Topic: Brain proteins, microglia and lab models
Background Alzheimer's disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18 F-fluorodeoxy-glucose positron emission tomography ( 18 F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information.
Key findings
- Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18 F-fluorodeoxy-glucose positron emission tomography ( 18 F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance.
- This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information.
- Methods In this study, 18 F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China.
- First, we performed a group comparison using a two-sample Student's t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts.
Why it matters
This research developed a new method called radiomics to improve computer-aided diagnosis of Alzheimer's disease and mild cognitive impairment using brain scans. It is about improving diagnosis, not about treating, curing, or preventing the disease.
Study details
- Participants422
- Effect size91.5%; 83.1%; 85.9%
- Evidence strengthModerate
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Therapeutic advances in neurological disorders) |
| Journal's citation record | 1 / 4 | Therapeutic Advances in Neurological Disorders: journal h-index 93 (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 | 3 / 5 | Rated “moderate” 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 | 422 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 | 4 / 7 | Cited 66 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.3 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 6.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 22 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Chuantao Zuo: h-index 38 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Fudan University: institution h-index 954 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30956687) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1177/1756286419838682
- PubMed: 30956687
- Original source →
- Free full text: Therapeutic Advances in Neurological Disorders →
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
Li, Y., Jiang, J., Lu, J., Zhang, H., & Zuo, C. (2019). Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Therapeutic advances in neurological disorders. https://doi.org/10.1177/1756286419838682
Vancouver
Li Y, Jiang J, Lu J, Zhang H, Zuo C. Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Therapeutic advances in neurological disorders. 2019. doi:10.1177/1756286419838682. PMID: 30956687.
BibTeX
@article{li2019-30956687,
author = {Li, Y. and Jiang, J. and Lu, J. and Zhang, H. and Zuo, C.},
title = {{Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment}},
journal = {Therapeutic advances in neurological disorders},
year = {2019},
doi = {10.1177/1756286419838682},
pmid = {30956687},
url = {https://doi.org/10.1177/1756286419838682}
}
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