AI-assisted diagnosis for malignant brain tumors
Official title: Research on AI-assisted Diagnosis of Common Malignant Brain Tumors Based on Magnetic Resonance Imaging
Run by Second Affiliated Hospital, Zhejiang University, School of Medicine · NCT07198256 · Data updated 2026-11-12
This project will test an AI system that uses routine preoperative MRI scans to automatically segment and classify malignant brain tumors in adults to help guide diagnosis and care.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 100 Years |
| Sex | All |
| Enrollment | 3000 (estimated) |
| Sponsor | Second Affiliated Hospital, Zhejiang University, School of Medicine Academic / other |
| Locations | 2 sites (Hangzhou, Zhejiang and 1 other locations) |
| Status | Recruiting |
| Started | September 1, 2025 |
| Main results expected | December 31, 2025 |
| Study ends | December 31, 2028 |
| Registry record updated | September 30, 2025 |
| Trial ID | NCT07198256 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (18 years or older) with histopathologically confirmed glioma, brain metastasis, or brain lymphoma who had complete preoperative CE-T1WI and T2-FLAIR MRI are the ideal candidates for inclusion.
✕Probably not a fit
Patients with poor image quality, a history of prior brain surgery or radiotherapy, other concurrent intracranial lesions, or under 18 years old are unlikely to benefit from this dataset or model development.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This retrospective, multi-center effort will build a large MRI database of about 3,000 adults with histologically confirmed malignant brain tumors (gliomas, brain metastases, and lymphomas) using preoperative CE-T1WI and T2-FLAIR images acquired on 1.5T or 3.0T scanners. Data are collected at two major centers in Hangzhou and will be used to train deep learning models for lesion segmentation and multi-subtype tumor classification. The work focuses on improving accuracy of non-invasive preoperative diagnosis across multiple tumor types and complex lesion anatomies by addressing prior small-sample and single-center limitations. The ultimate aim is an image-based AI-assisted diagnostic tool that can reduce unnecessary biopsies and support clinical decision making.
Why it matters
Potential benefit: If successful, the AI system could improve the accuracy of non-invasive preoperative diagnosis of malignant brain tumors and reduce the need for diagnostic biopsies.
How similar studies have gone: Deep learning methods for brain tumor segmentation and some subtype classification have shown promising results (for example in BraTS and other research), but prior work has been limited by smaller or single-center datasets and variable classification performance.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients diagnosed with glioma, brain metastases, and brain lymphoma by pathology, with the patient being at least 18 years old; preoperative MRI was complete. Exclusion Criteria: * Poor image quality; history of previous brain surgery or radiotherapy; accompanied by other intracranial lesions.
Where it's running
-
2nd Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, China Recruiting -
Zhejiang Cancer Hospital
Hangzhou, Zhejiang, China Not yet recruiting
Study contacts
- Study coordinator: Chao Wang, MD
- Email: wangchaosmart@zju.edu.cn
- Phone: 8613706518691
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.