Intelligent platform for brain tumor diagnosis, treatment and recurrence monitoring
| ISRCTN | ISRCTN14076099 |
|---|---|
| DOI | https://doi.org/10.1186/ISRCTN14076099 |
| Sponsor | University of Malaya |
| Funder | Kementerian Sains, Teknologi dan Inovasi |
- Submission date
- 31/03/2026
- Registration date
- 06/04/2026
- Last edited
- 11/05/2026
- Recruitment status
- Recruiting
- Overall study status
- Ongoing
- Condition category
- Cancer
Prospectively registered
Protocol
Statistical analysis plan
Results
Individual participant data
Record updated in last year
Plain English summary of protocol
Not provided at time of registration
Contact information
Dr Shier Nee Saw
Principal investigator, Scientific, Public
Principal investigator, Scientific, Public
Universiti Malaya
Kuala Lumpur
50603
Malaysia
| Phone | +6016-4387089 |
|---|---|
| sawsn@um.edu.my |
Study information
| Primary study design | Observational |
|---|---|
| Observational study design | Cohort study |
| Scientific title | Comprehensive end-to-end intelligent platform for brain tumor diagnosis, treatment and recurrence monitoring using multimodal datasets |
| Study objectives | |
| Ethics approval(s) |
Approved 03/03/2026, University of Malaya Medical Centre Medical Research Ethics Committee (3rd floor, Menara Utama, Pusat Perubatan Universiti Malaya, Kuala Lumpur, 59100, Malaysia; +603 (0)7949 3209; ummc-mrec@ummc.edu.my), ref: 20251024-15771 |
| Health condition(s) or problem(s) studied | Brain tumour |
| Intervention | This study employs a multimodal artificial intelligence (AI) approach integrating radiological and pathological imaging data for brain tumour diagnosis and treatment support. MRI scans will be collected and anonymised for data cleaning, preprocessing, and expert annotation. Other data collected include Whole Slide Image (WSI) reports, Isocitrate Dehydrogenase (IDH) reports, and clinical, demographic, radiological, pathological, and treatment-related data available in the medical records of patients diagnosed with brain tumours. All data will be anonymised before analysis. Deep learning models will be trained using public datasets for pre-operative tumour diagnosis and postoperative recurrence prediction and validated using local clinical datasets from PPUM. Segmentation models will also be developed for tumour and blood vessel identification and integrated with a surgical navigation platform to enhance precision and safety. The outcome will be an end-to-end intelligent platform for multimodal analysis, recurrence warning, and surgical planning assistance. |
| Intervention type | Other |
| Primary outcome measure(s) |
|
| Key secondary outcome measure(s) | |
| Completion date | 31/12/2028 |
Eligibility
| Participant type(s) | |
|---|---|
| Age group | All |
| Lower age limit | 0 Years |
| Upper age limit | 99 Years |
| Sex | All |
| Target sample size at registration | 500 |
| Key inclusion criteria | 1. Patients diagnosed with primary brain tumour 2. Patients who underwent standard of care procedure at University of Malaya Medical Centre (UMMC) |
| Key exclusion criteria | 1. Patients diagnosed with other types of brain disease |
| Date of first enrolment | 01/04/2026 |
| Date of final enrolment | 31/12/2028 |
Locations
Countries of recruitment
- Malaysia
Study participating centres
Results and Publications
| Individual participant data (IPD) Intention to share | No |
|---|
Study outputs
| Output type | Details | Date created | Date added | Peer reviewed? | Patient-facing? |
|---|---|---|---|---|---|
| Protocol file | 11/05/2026 | No | No |
Additional files
- ISRCTN14076099_Protocol..pdf
- Protocol file
Editorial Notes
11/05/2026: Protocol uploaded and ethics approval date updated.
31/03/2026: Study's existence confirmed by Pusat Perubatan Universiti Malaya.
