AI-assisted Diagnosis of Malignant Brain Tumors

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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Key information

Conditions

Gliomas Brain Diseases Brain Metastases, Adult Brain Neoplasms Brain Tumor Adult Central Nervous System Diseases Central Nervous System Neoplasms Glioma Hemic and Lymphatic Diseases Immune System Diseases Immunoproliferative Disorders Lymphatic Diseases Lymphoma Lymphoproliferative Disorders Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Germ Cell and Embryonal Neoplasms, Glandular and Epithelial Neoplasms, Nerve Tissue Neoplasms, Neuroepithelial Nervous System Diseases Nervous System Neoplasms Neuroectodermal Tumors

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Chao Wang, MD

CONTACT

[email protected]

8613706518691

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Other

Collaborators

Zhejiang Cancer Hospital

Registry information

Official study title

Research on AI-assisted Diagnosis of Common Malignant Brain Tumors Based on Magnetic Resonance Imaging

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2028
First posted: Sep 30, 2025
Registry last updated: Sep 30, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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