Ribeirão Preto Multiple Sclerosis Quantitative MRI Cohort
Clinics Hospital of Ribeirão Preto Medical School, quantitative brain MRI of multiple sclerosis patients and controls
Brain MRI of multiple sclerosis patients and neurologically normal controls, used to classify patients from regional quantitative measures (diffusion, T2 relaxometry, magnetization transfer, morphometry). Images are not shared; only the paper's figures and result tables are public.
Overview
This entry covers the brain MRI cohort of a retrospective single-center study at the Clinics Hospital of Ribeirão Preto Medical School, University of São Paulo, Brazil. The authors extracted regional quantitative MRI measures from patients with clinically confirmed multiple sclerosis and from people without detected neurological abnormalities, and trained a k-nearest-neighbor classifier to separate the two groups and rank the brain regions and techniques that contributed most. The images are not public. The paper's figshare record holds only its figures and result tables.
Composition
Table 1 of the paper lists 144 patients (44 male, 100 female, mean age 41.5) and 203 controls (108 male, 95 female, mean age 29.3), both aged 7 to 65. Based on the radiology reports, 12% of the patients had lesions of low destructiveness, 32% moderate and 56% high. Not every person had every acquisition, and some scans were dropped in quality control, so the per-technique counts in Table 3 are smaller than the cohort.
Acquisition
All scans were acquired between 2008 and 2014 on a single 3 T Philips Achieva. The study used a 3D T1-weighted scan, diffusion tensor imaging, a magnetization transfer acquisition and a multi-echo T2 relaxometry acquisition. Fractional anisotropy and diffusivity maps came from the scanner, while MTR and T2 maps were computed with in-house tools.
Annotations
FreeSurfer 4.5 with the Destrieux atlas segmented the T1-weighted scans. The authors kept 126 of 166 labels after merging or dropping small regions, and extracted volume, cortical thickness, diffusion, MTR and T2 values per region.
Known limitations
- Images, segmentations and regional measures are not shared, and the paper has no data availability statement.
- The abstract reports 124 patients, while the methods text and Table 1 report 144.
- Per-technique counts come from the classification tables and reflect quality exclusions, not the full acquisition numbers.
- Patients and controls differ in mean age by about 12 years.
Cohort
Aggregate numbers from the sources below. Bars are relative to the 347 subjects.
Sex
- Female 195 56%
- Male 152 44%
Table 1 in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Condition
Groups can overlap
- Healthy control 203 59%Age mean 29.3
- Multiple sclerosis 144 41%Age mean 41.5
Table 1 in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Scanner vendor
- Philips 347 100%
Methods in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Condition by sex
Reported cross table. A dot marks a cell the source does not give.
| Female | Male | |
|---|---|---|
| Healthy control | 95 | 108 |
| Multiple sclerosis | 100 | 44 |
Table 1 in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Age
range 7 to 65
No age bins reported.
Table 1 in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Contrast / sequence
Groups can overlap
- T1-weighted 303 87%
- Magnetization transfer 200 58%
- T2 map 198 57%
- Diffusion-weighted 166 48%
Table 3 in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Field strength
- 3 T 347 100%
Methods in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
Contrast / sequence by condition
Reported cross table. A dot marks a cell the source does not give.
| Multiple sclerosis | Healthy control | |
|---|---|---|
| T1-weighted | 138 | 165 |
| Magnetization transfer | 109 | 91 |
| T2 map | 108 | 90 |
| Diffusion-weighted | 90 | 76 |
Table 3 in Pessini et al. 2018, Research on Biomedical Engineering 34(2)
License and access
Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer
The data are not available outside the institution or study that holds them
The paper has no data availability statement and the images were used retrospectively from the hospital's clinical archive. The figshare record that accompanies the paper holds only its figures and four result tables.
No published license or data use terms
The data holder has published no license and no data use terms. Any use, sharing or commercial right has to be agreed with the data holder, and nothing can be assumed to be allowed. Default copyright and data protection law still apply.
No license or data use terms are published for the images. The CC BY 4.0 license of the paper and the figshare record covers only the article, figures and tables.
What you can do
- Commercial use Not stated
- Not stated
- Train ML models Not stated
- Create derived data Not stated
- Publish results Not stated
What you can share
- Share the data Not stated
- Share derived data Not stated
- Share trained models Not stated
What you must do
- Cite or credit Not stated
- Share alike Not stated
- Sign an agreement Not stated
- Ethics approval Not stated
- Manuscript review Not stated
- Release code Not stated
- Return results Not stated
- Delete after use Not stated
Limits
- No re-identification Not stated
- Location limits Not stated
Citation
Pessini, R. A., Santos, A. C., & Salmon, C. E. G. (2018). Quantitative MRI data in multiple sclerosis patients: a pattern recognition study. Research on Biomedical Engineering, 34(2). https://doi.org/10.1590/2446-4740.07117
Sources
Every number on this page comes from one of these documents. Each chart names the table or page it is taken from. The raw numbers are in stats.csv.