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Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model
Shin NY, Bang M, Yoo SW, Kim JS, Yun E, Yoon U, Han K, Ahn KJ et al.. Radiology, 2021-05-25
Topic: Amyloid, tau and blood-test diagnosis
Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinical settings. Purpose To investigate whether cortical thickness from MRI can help predict conversion from mild cognitive impairment (MCI) to dementia in PD at an individual level using a machine learning-based model. Materials and Methods In this retrospective study, patients with PD and MCI who underwent MRI from September 2008 to November 2016 were included. Features were selected from clinical and cortical thickness variables in 10 000 randomly generated training sets. Features selected 5000 times or more were used to train random forest and support vector machine models. Each model was trained and tested in 10 000 randomly resampled data sets, and a median of 10 000 areas under the receiver operating characteristic curve (AUCs) was calculated for each. Model performances were validated in an external test set. Results Forty-two patients progressed to PDD (converters) (mean age, 71 years ± 6 [standard deviation]; 22 women), and 75 patients did not progress to PDD (nonconverters) (mean age, 68 years ± 6; 40 women). Four PDD converters (mean age, 74 years ± 10; four men) and 20 nonconverters (mean age, 67 years ± 7; 11 women) were included in the external test set. Models trained with cortical thickness variables (AUC range, 0.75-0.83) showed fair to good performances similar to those trained with clinical variables (AUC range, 0.70-0.81). Model performances improved when models were trained with both variables (AUC range, 0.80-0.88). In pair-wise comparisons, models trained with both variables more frequently showed better performance than others in all model types. The models trained with both variables were successfully validated in the external test set (AUC range, 0.69-0.84). Conclusion Cortical thickness from MRI helped predict conversion from mild cognitive impairment to dementia in Parkinson disease at an individual level, with improved performance when integrated with clinical variables. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Port in this issue.
Key findings
- Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinical settings.
- Results Forty-two patients progressed to PDD (converters) (mean age, 71 years ± 6 [standard deviation]; 22 women), and 75 patients did not progress to PDD (nonconverters) (mean age, 68 years ± 6; 40 women).
Why it matters
A machine learning model using cortical thickness from MRI helped predict conversion from mild cognitive impairment to dementia in Parkinson's disease. It is a prediction study and reports no treatment or cure.
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Study details
- Participants75
- Evidence strengthLow
How this study scored 46/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Radiology) |
| Journal's citation record | 3 / 4 | Radiology: journal h-index 431 (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 | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 1 / 4 | 75 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 51 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.3 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 3 / 6 | Cited 4.9× 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 | 2 / 4 | Cited 34 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Seung‐Koo Lee: h-index 60 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Yonsei University: institution h-index 777 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 34032515) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 46 / 100 | — |
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Read the original
- DOI: 10.1148/radiol.2021203383
- PubMed: 34032515
- Original source →
- Free full text: YUHSpace (Yonsei University Medical Library) →
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
Shin, N. Y., Bang, M., Yoo, S. W., Kim, J. S., Yun, E., Yoon, U., et al. (2021). Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model. Radiology. https://doi.org/10.1148/radiol.2021203383
Vancouver
Shin NY, Bang M, Yoo SW, Kim JS, Yun E, Yoon U, et al. Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model. Radiology. 2021. doi:10.1148/radiol.2021203383. PMID: 34032515.
BibTeX
@article{shin2021-34032515,
author = {Shin, N. Y. and Bang, M. and Yoo, S. W. and Kim, J. S. and Yun, E. and Yoon, U. and Han, K. and Ahn, K. J. and others},
title = {{Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model}},
journal = {Radiology},
year = {2021},
doi = {10.1148/radiol.2021203383},
pmid = {34032515},
url = {https://doi.org/10.1148/radiol.2021203383}
}
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