AI-assisted Diagnosis of Malignant Brain Tumors

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

3000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-09-01

Study Completion Date

2028-12-31

Brief Summary

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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.

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Detailed Description

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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

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Gliomas Brain Metastases, Adult Lymphoma Brain Tumor Adult

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Study Groups

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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

No interventions assigned to this group

Eligibility Criteria

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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.