AI-assisted Diagnosis of Malignant Brain Tumors

Get Involved

AI-assisted Diagnosis of Malignant Brain Tumors

Study Purpose

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.

Recruitment Criteria

Accepts Healthy Volunteers Healthy volunteers are participants who do not have a disease or condition, or related conditions or symptoms	No
Study Type An interventional clinical study is where participants are assigned to receive one or more interventions (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes. An observational clinical study is where participants identified as belonging to study groups are assessed for biomedical or health outcomes. Searching Both is inclusive of interventional and observational studies.	Observational
Eligible Ages	18 Years and Over
Gender	All

More 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.

Trial Details

Trial ID: This trial id was obtained from ClinicalTrials.gov, a service of the U.S. National Institutes of Health, providing information on publicly and privately supported clinical studies of human participants with locations in all 50 States and in 196 countries.	NCT07198256
Phase Phase 1: Studies that emphasize safety and how the drug is metabolized and excreted in humans. Phase 2: Studies that gather preliminary data on effectiveness (whether the drug works in people who have a certain disease or condition) and additional safety data. Phase 3: Studies that gather more information about safety and effectiveness by studying different populations and different dosages and by using the drug in combination with other drugs. Phase 4: Studies occurring after FDA has approved a drug for marketing, efficacy, or optimal use.
Lead Sponsor The sponsor is the organization or person who oversees the clinical study and is responsible for analyzing the study data.	Second Affiliated Hospital, School of Medicine, Zhejiang University
Principal Investigator The person who is responsible for the scientific and technical direction of the entire clinical study.	N/A
Principal Investigator Affiliation	N/A
Agency Class Category of organization(s) involved as sponsor (and collaborator) supporting the trial.	Other
Overall Status	Recruiting
Countries	China
Conditions The disease, disorder, syndrome, illness, or injury that is being studied.	Gliomas, Brain Metastases, Adult, Lymphoma, Brain Tumor Adult

Additional Details

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.

Arms & Interventions

Arms

: 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

Interventions

Contact a Trial Team

If you are interested in learning more about this trial, find the trial site nearest to your location and contact the site coordinator via email or phone. We also strongly recommend that you consult with your healthcare provider about the trials that may interest you and refer to our terms of service below.