Simulation-based study to understand how brain tumour growth affects MRI appearance
| ISRCTN | ISRCTN66246505 |
|---|---|
| DOI | https://doi.org/10.1186/ISRCTN66246505 |
| Sponsor | Second Affiliated Hospital of Xuzhou Medical College |
| Funders | Xuzhou Municipal Science and Technology Bureau, Jiangsu Commission of Health, Xuzhou Medical University Affiliated Hospital |
- Submission date
- 03/02/2026
- Registration date
- 06/02/2026
- Last edited
- 05/02/2026
- Recruitment status
- No longer recruiting
- Overall study status
- Completed
- Condition category
- Cancer
Plain English summary of protocol
Background and study aims
Gliomas are a common type of brain tumour that can behave very differently depending on their grade. Some grow slowly, while others grow quickly and are more aggressive. Magnetic resonance imaging (MRI) is widely used to examine these tumours, but MRI images do not always clearly show how aggressive a tumour is. This study aims to use computer-based simulations to better understand how different patterns of tumour growth may lead to different MRI appearances. The goal is to improve the interpretation of MRI scans in a non-invasive way.
Who can participate?
No new participants will be recruited for this study. The research uses previously collected, fully anonymised MRI scans from adult patients who were diagnosed with glioma as part of routine clinical care.
What does the study involve?
The study involves analysing existing MRI scans using a computer model that simulates how brain tumours grow and spread. The model generates synthetic MRI images based on different tumour growth patterns. These simulated images are then examined to understand how features such as tumour shape, boundaries, and surrounding swelling may differ between lower-grade and higher-grade gliomas. No additional tests, scans, or treatments are performed on patients.
What are the possible benefits and risks of participating?
There are no direct benefits or risks to individuals whose data are included, as no new procedures or contact with patients are involved. All MRI data are fully anonymised. The study may benefit future patients by helping doctors better understand MRI features of brain tumours and support non-invasive assessment in clinical practice.
Where is the study run from?
The Second Affiliated Hospital of Xuzhou Medical University (China)
When is the study starting and how long is it expected to run for?
The study uses retrospective MRI data collected before treatment. The analysis phase began in 2023 and was completed in 2024.
Who is funding the study?
The study is supported by research funding from the Xuzhou Science and Technology Bureau, the Jiangsu Provincial Health Commission, and Xuzhou Medical University–affiliated research programs (China)
Who is the main contact?
Prof. Peng Du, dupeng0516@126.com
Contact information
Principal investigator, Scientific, Public
Department of Radiology, Xuzhou Mining Group General Hospital (The Second Affiliated Hospital of Xuzhou Medical University)
Xuzhou
-
China
 ORCID ID |
0009-0007-7350-3839 |
|---|---|
| Phone | +86 (0)131 2995 4662 |
| dupeng0516@126.com |
Study information
| Primary study design | Observational |
|---|---|
| Observational study design | Simulation-based observational study using retrospective, de-identified MRI exemplars |
| Scientific title | Simulation-based modelling of tumor growth dynamics and MRI manifestations in low- versus high-grade gliomas |
| Study acronym | SIM-Glioma-MRI |
| Study objectives | Primary objective: To evaluate whether a biophysical, simulation-based tumor growth model can reproduce and quantitatively distinguish magnetic resonance imaging (MRI) phenotypes associated with low-grade and high-grade gliomas using retrospective, fully de-identified imaging data. Secondary objectives: 1. To characterize the relationship between tumor growth kinetics (proliferation, diffusion, necrosis, and edema) and corresponding MRI features. 2. To derive interpretable imaging biomarkers that reflect underlying tumor biology and may support non-invasive glioma characterization. Rationale: Conventional MRI provides indirect and sometimes ambiguous information about glioma biology. Simulation-based modeling offers a complementary, mechanistic approach to link tumor growth dynamics with MRI appearance. This study was undertaken to improve the biological interpretability of MRI findings and to support standardized, non-invasive assessment of glioma grade in an observational research context. |
| Ethics approval(s) |
Approved 04/03/2024, Biomedical Research Ethics Committee of Xuzhou Mining Group General Hospital (The Second Affiliated Hospital of Xuzhou Medical University) (Science and Education Department, Xuzhou, -, China; +86 (0)516 85326137; zhaofangchao@suda.edu.cn), ref: 2024 030402 |
| Health condition(s) or problem(s) studied | Glioma (low-grade and high-grade brain tumors) |
| Intervention | This is an observational, simulation-based study using retrospective, fully de-identified magnetic resonance imaging (MRI) data. Pre-treatment MRI sequences (T1-weighted, post-contrast T1-weighted, T2-weighted, and FLAIR) from patients with histologically confirmed glioma are used as exemplar inputs to calibrate and qualitatively validate a biophysical tumor growth model. The model simulates tumor proliferation, diffusion, necrosis, and edema, and is coupled with a forward MRI synthesis framework to generate synthetic MRI images. Quantitative imaging features, including enhancement fraction, tumor margin sharpness, edema-to-core volume ratio, and growth-related metrics, are derived from the simulated data and compared across low-grade and high-grade glioma parameter regimes. No interventions are performed, no randomisation or allocation occurs, and no prospective participant recruitment is undertaken. All analyses are conducted on retrospective, anonymised data in accordance with institutional ethics approval. |
| Intervention type | Not Specified |
| Primary outcome measure(s) |
|
| Key secondary outcome measure(s) |
|
| Completion date | 31/05/2025 |
Eligibility
| Participant type(s) | |
|---|---|
| Age group | Mixed |
| Lower age limit | 18 Years |
| Upper age limit | 120 Years |
| Sex | All |
| Target sample size at registration | 10 |
| Total final enrolment | 10 |
| Key inclusion criteria | 1. Patients with histologically confirmed glioma 2. Availability of pre-treatment MRI (T1, post-contrast T1, T2, FLAIR) 3. MRI acquired prior to any surgical, radiotherapy, or systemic treatment 4. Fully de-identified imaging data available for retrospective analysis |
| Key exclusion criteria | 1. Prior surgical resection, radiotherapy, or chemotherapy before MRI 2. Incomplete or poor-quality MRI data 3. Imaging data containing identifiable patient information |
| Date of first enrolment | 01/04/2024 |
| Date of final enrolment | 31/03/2025 |
Locations
Countries of recruitment
- China
Study participating centres
Results and Publications
| Individual participant data (IPD) Intention to share | No |
|---|---|
| IPD sharing plan summary | Not expected to be made available |
| IPD sharing plan |
Editorial Notes
03/02/2026: Study's existence confirmed by the Biomedical Research Ethics Committee of Xuzhou Mining Group General Hospital (The Second Affiliated Hospital of Xuzhou Medical University).
