Research on AI-assisted Diagnosis of Common Malignant Brain Tumors Based on Magnetic Resonance Imaging
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 3,000
- Locations
- 2
- Primary Endpoint
- Construct an AI-assisted diagnostic system for multiple subtypes of brain tumors based on deep learning.
Study Overview
Brief 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. 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
- Study Type
- Observational
- Observational Model
- Case Control
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to 100 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
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.
Outcomes
Primary Outcomes
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.
Secondary Outcomes
No secondary outcomes reported