GliomasNCT07198256
AI-assisted Diagnosis of Malignant Brain Tumors
An observational study of Gliomas and Brain Metastases, Adult, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 2 sites in China.
Trial summary
This study aims to establish a large-scale, multi-center MRI database for malignant brain tumors. It will develop an artificial intelligence system for the segmentation and classification of multiple subtypes of brain tumors (including glioma, metastatic tumor and lymphoma et al.) using deep learning technology. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Gliomas, Brain Metastases, Adult, Lymphoma
- How many people
- 3,000 participants
- Where
- 2 sitesChina
- Study dates
- 1 Sep 2025to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Retrospectively collected cases of malignant brain tumors (including gliomas, brain metastases, and brain lymphomas) that were confirmed by histopathology and had preoperative multimodal MRI images (mainly including CE-T1WI and T2-FLAIR) over the past 10 years.
Who may be able to join
- Patients diagnosed with glioma, brain metastases, and brain lymphoma by pathology, with the patient being at least 18 years old; preoperative MRI was complete.
Who cannot join
- Poor image quality; history of previous brain surgery or radiotherapy; accompanied by other intracranial lesions.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
2nd Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009
Chao Wang, MD, 8613706518691, wangchaosmart@zju.edu.cn
- Not yet recruiting
Zhejiang Cancer Hospital
Hangzhou, Zhejiang, 310022
Lei Shi, MD, 8615988872208, shilei@zjcc.org.cn
About this study
Official title: Research on AI-assisted Diagnosis of Common Malignant Brain Tumors Based on Magnetic Resonance Imaging
This study aims to establish a large-scale, multi-center MRI database for malignant brain tumors. It will develop an artificial intelligence system for the segmentation and classification of multiple subtypes of brain tumors (including glioma, metastatic tumor and lymphoma et al.) using deep learning technology. This will address the issues of small sample sizes and limited classification performance in existing methods, thereby improving the accuracy of non-invasive preoperative diagnosis, reducing the need for biopsies, and having significant clinical translational value.
Read the detailed description
This study is mainly based on two centers, the Second Affiliated Hospital of Zhejiang University School of Medicine and the Zhejiang Cancer Hospital. It retrospectively collects cases of malignant brain tumors (including gliomas, brain metastases, and brain lymphomas) that have been confirmed by histopathology and have preoperative multimodal MRI images (mainly including CE-T1WI and T2-FLAIR). It is expected to include 3,000 cases. Axial CE-T1WI and T2-FLAIR images of all patients were obtained on 3.0T or 1.5T magnetic resonance imaging systems. A large-scale, multi-center MRI image database for common malignant brain tumors (gliomas, brain metastases, and brain lymphomas) was planned to be constructed. To address the automatic segmentation of complex lesion tissues in brain tumors and the auxiliary diagnosis of common malignant brain tumors, a deep learning technical approach was adopted. A deep learning-based multi-subtype brain tumor segmentation and classification diagnostic method was proposed, aiming to build an image artificial intelligence-assisted diagnostic system for common malignant brain tumors and improve the accuracy of auxiliary diagnosis of common brain malignancies.
- Conditions
- Gliomas, Brain Metastases, Adult, Lymphoma, Brain Tumor Adult
- Keywords
- brain tumor, glioma, brain metastases, lymphoma, magnetic resonance imaging, artificial intelligence
- Medical subject headings
- Glioma, Brain Neoplasms, Lymphoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| malignant brain tumors | Retrospectively collected cases of malignant brain tumors (including gliomas, brain metastases, and brain lymphomas) that were confirmed by histopathology and had preoperative multimodal MRI images (mainly including CE-T1WI and T2-FLAIR) over the past 10 years |
What is being measured
The primary outcome is the main question the study is designed to answer.
Construct an AI-assisted diagnostic system for multiple subtypes of brain tumors based on deep learning.
Construct an AI-assisted diagnostic system for multiple subtypes of brain tumors based on deep learning, mainly including glioma, metastatic tumor and lymphoma.
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2025
- Study start1 Sep 2025
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GliomasStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Zhejiang Cancer Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07198256
- Sponsor's protocol ID
- 2023-1050
- Status last verified
- Sep 2025
- First submitted
- 22 Sep 2025
- Last updated
- 30 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on AI-assisted Diagnosis of Common Malignant Brain Tumors Based on Magnetic Resonance Imaging
- ClinicalTrials.gov ID
- NCT07198256
- Acronym / sponsor ID
- 2023-1050
- Conditions
- Gliomas, Brain Metastases, Adult, Lymphoma, Brain Tumor Adult
- Keywords
- brain tumor, glioma, brain metastases, lymphoma, magnetic resonance imaging, artificial intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Collaborators
- Zhejiang Cancer Hospital
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 30 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to establish a large-scale, multi-center MRI database for malignant brain tumors. It will develop an artificial intelligence system for the segmentation and classification of multiple subtypes of brain tumors (including glioma, metastatic tumor and lymphoma et al.) using deep learning technology. This will address the issues of small sample sizes and limited classification performance in existing methods, thereby improving the accuracy of non-invasive preoperative diagnosis, reducing the need for biopsies, and having significant clinical translational value.
Detailed description
This study is mainly based on two centers, the Second Affiliated Hospital of Zhejiang University School of Medicine and the Zhejiang Cancer Hospital. It retrospectively collects cases of malignant brain tumors (including gliomas, brain metastases, and brain lymphomas) that have been confirmed by histopathology and have preoperative multimodal MRI images (mainly including CE-T1WI and T2-FLAIR). It is expected to include 3,000 cases. Axial CE-T1WI and T2-FLAIR images of all patients were obtained on 3.0T or 1.5T magnetic resonance imaging systems. A large-scale, multi-center MRI image database for common malignant brain tumors (gliomas, brain metastases, and brain lymphomas) was planned to be constructed. To address the automatic segmentation of complex lesion tissues in brain tumors and the auxiliary diagnosis of common malignant brain tumors, a deep learning technical approach was adopted. A deep learning-based multi-subtype brain tumor segmentation and classification diagnostic method was proposed, aiming to build an image artificial intelligence-assisted diagnostic system for common malignant brain tumors and improve the accuracy of auxiliary diagnosis of common brain malignancies.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| malignant brain tumors | – | –Retrospectively collected cases of malignant brain tumors (including gliomas, brain metastases, and brain lymphomas) that were confirmed by histopathology and had preoperative multimodal MRI images (mainly including CE-T1WI and T2-FLAIR) over the past 10 years |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Retrospectively collected cases of malignant brain tumors (including gliomas, brain metastases, and brain lymphomas) that were confirmed by histopathology and had preoperative multimodal MRI images (mainly including CE-T1WI and T2-FLAIR) over the past 10 years.
Key inclusion criteria (1 listed)
- Patients diagnosed with glioma, brain metastases, and brain lymphoma by pathology, with the patient being at least 18 years old; preoperative MRI was complete.
Key exclusion criteria (1 listed)
- Poor image quality; history of previous brain surgery or radiotherapy; accompanied by other intracranial lesions.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Construct an AI-assisted diagnostic system for multiple subtypes of brain tumors based on deep learning.
Time frame: 30 days
Construct an AI-assisted diagnostic system for multiple subtypes of brain tumors based on deep learning, mainly including glioma, metastatic tumor and lymphoma.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gliomas and Brain Metastases, Adult, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Chao Wang, MD (8613706518691, wangchaosmart@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Construct an AI-assisted diagnostic system for multiple subtypes of brain tumors based on deep learning. (time frame: 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Sep 2025.